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Let me remind you that some of the matters we will discuss in today's conference, including our business outlook, are forward-looking and as such, are subject to known and unknown risks and uncertainties, including, but not limited to, those factors set forth in our most recent annual report on Form 10-K, quarterly reports on Form 10-Q and other documents filed with or furnished to the SEC. These risks and uncertainties may cause our actual results to differ materially from those expressed or implied during our presentation and are not a guarantee of our future performance.

As always, Accenture assumes no obligation to update any statements made in these presentations. During today's presentation, we will reference certain non-GAAP financial measures, which we believe provide useful information for our investors and our analysts. You'll be able to find materials from today's session, including reconciliation of non-GAAP to GAAP measures posted to the Investor Relations section of our website.

Hello, and welcome. We have many of you in the room and online. So thank you for joining us. And thank you to the people who actually make today possible. That's our reinventors and our clients around the world, the incredible technology companies that make up our ecosystem and of course, our production team who's with us today. We're also joined by some of our Board and many of our senior leaders. And for those of you here today in person, you're going to be able to meet some of those senior leaders.

We are really excited because you're investing time and we have the opportunity to tell our full story. Our goal for today is that you leave with a deeper understanding of how we have become a business reinvention and technology company and why we have significant growth ahead.

Today, we're going to cover three things. First, why is AI making Accenture more essential than ever; second, our new foundation for growth that we've been building for the last 4 years; and finally, third, our growth trajectory, how we're going to use that foundation to drive significant durable growth and shareholder value creation.

So let me start with some context. Twice in the last 20 years, there's been an economic reset in our industry. First, there was offshoring, made possible by the Internet and automation, offshoring drove down the cost of building software and routine IT services. In fact, at one point, there was a fear that automation would wipe out huge portions of the IT services industry. Then came Cloud and Digital. Cloud and digital drove down the cost of technology itself. And you saw for the first time, software starting to replace some services.

Now over both eras, an important thing happened. Offshoring, Cloud and Digital did drive down the cost of some of our services and eliminated some of our services. And at the same time, the underlying technology allowed enterprises to do so much more that the new demand that was created far exceeded over time the demand that it replaced. And in both cases, Accenture had significant growth and took significant market share.

How did we do that? Well, first of all, we embrace these developments quickly. We invested in innovation, and we were willing to reinvent ourselves from how we worked to where we worked to how we organize to what we sold. And we invested each time in new sources of revenue that were being created by the technology wave. So we're now 4 years into the next economic reset of our industry, the age of AI. We think the opportunity is bigger than ever before. And while AI does have the potential with AI efficiencies to impact our business more than ever before, we again believe that, ultimately, the new demand that will be created will far exceed the demand that's being replaced.

Now across both of these areas, clients, the ecosystem and trust have been at the center of our strategy, and they remain so. And we have confidence in our future because we've had a great track record in prior technology waves, we're running -- a playbook that is informed by the past but accelerated. And now we can see it in the opportunities that we've created already and the new foundation.

So I'm with clients almost daily. It's the best part of my job because it's so inspiring to be with CEOs with bold ambitions. And those ambitions are being driven by the twin catalysts of AI and the macro environment.

Now when I look at where we are across our client base, there's wide varieties of AI readiness. We have some clients who are moving from using AI to running on AI with enterprise-wide programs. Most are still quite early, starting to scale some things across enterprise and many more are just getting started. Wherever they are starting, there's one thing, though, that is very clear. Incremental change is not enough.

We see it this way. AI is the breakthrough innovation. Only reinvention can unlock the value. And this is what matters to Accenture. Our clients turn to us in times of change. The bigger the change, the more they need us. And reinvention is fundamental change. And our clients over the years have turned to us over and over again because we've delivered reinvention. That is why 85% of our top 200 commercial clients and all of the G7 governments have been clients for 20 years or more. They turn to us because we have a unique set of strengths that no other company can fully replicate.

Now you're going to hear a lot about of our strengths over the course of the day. But I want to highlight a few that we've built over decades, who we serve, who we build with, what we know and what we've done. So when you think about who we serve, we operate in every major market. 75% of the Fortune 100 and Fortune 500 are in our client base. We serve every G7 government. Half the world's economy run through our client relationships. And we have an incredible ability to build trusted relationships sometimes built over decades because we have a cadre of client account leaders who wake up every day thinking about how to help their clients be successful and what we can do to be a part of that. This means that we have unmatched insight and experience.

And there's who we build with. We have, by far, the largest and most strategic ecosystem of technology companies in our industry. We have over 200 in our network, but 18 of them make up about 65% of our revenue. They are the largest technology companies. They are the emerging technology companies. They are all at the center of the AI wave. And because of these relationships and our ability to invest, we use the latest technologies early, and we build the skills our clients need. We understand technology. We understand the enterprise. We help our clients decide what is fit for purpose and then we help them create value.

We work with each of our technology companies in the ecosystem to create joint solutions that we take to our clients to help them accelerate value and lower risk. And we have the largest distribution channel in the industry with over 9,000 clients, which means our technology partners know that we can help them scale. They also know that we will have the people, our reinventors, that they need to scale because we have the best ability to attract, retain and grow talent in the industry. That is why we are #4 on the Great Place to Work global ranking. This is cross industry, and that ranking is by far the highest in our industry. And when you take all of these factors together, that is why decade after decade, we have been the #1 partner to the ecosystem that matters to our clients.

And that brings me to what we know. We have over 800,000 people. And last quarter, they were 93% utilized. Our clients value our reinventors. Our people have deep experience across 13 industries in all major functions. So that spans from corporate functions like HR and finance, to core operations, manufacturing, R&D, the network to everything customer, sales, marketing, service, product development. And our capabilities span from strategy, operating model, change management, process, data, AI, technology, these are all of the things that we talk about are so critical to reinvention. And we're able to deliver them in the way the client needs it through consulting, through managed services, with proprietary assets and platforms and increasingly through software that we are either buying or building.

That brings me to what we've done. Our purpose is to deliver on the promise of technology and human ingenuity. We use the word deliver very deliberately. This is our point of pride. People at Accenture get up every day proud because we deliver results. We have built our reputation, our brand on being at the center of every technology wave and delivering complex, hard programs. And when you think about it, every year, the number of clients who are doing $100 million quarters of bookings a quarter with us has increased. You do not have that level of relationship unless you trust your partner to deliver.

And when you think of each of these strengths, who we serve, who we build with, what we know, and what we've done, they're the reason why Accenture is a force multiplier for our clients. We help them do more than they can do alone. And all of this adds up to our ultimate strength, trust. Our clients trust us. They trust our expertise, they trust our ability to deliver and they trust our commitment to their success. And underpinning that trust is our commitment to live every day our core values and our leadership essentials.

Whenever I'm talking to a new young Managing Director, I tell them that everything they need to know about how to be successful personally and how to help Accenture be successful is there in our core values and our leadership essentials. They matter.

Now before I take you to where we're going, I want to spend a few minutes on our starting position. Over the last 4 years, we have invested organically and inorganically to build a new foundation for growth. It is not just a new foundation. It is the strongest foundation we have ever built. Over the last 4 years, we've increased our addressable market from $1.5 trillion to $3 trillion. And in the age of AI, we have built the largest AI and data workforce in our industry. And while we've done that, we've added $12.6 billion in revenue.

We've taken significant market share, growing more than 2x our investable basket, and we've returned $35 billion in cash to shareholders. We've made three big strategic moves over the last 4 years. The first, I will call accelerating large-scale reinventions. Once generative AI emerged in November 2022, we knew that it was going to take a while for the AI to mature to be able to have an impact, and we made the deliberate decision to go after more large transformations. These transformations put us at the center of our clients' agenda, and we knew that not only would they deepen our relationships, but they would position us to do the next waves as AI matures, which is exactly what we're seeing now. And we've been incredibly successful.

We use our $100 million bookings a quarter as a proxy for reinventions. In FY '22, that was $100 million. Today, it's $141 million in the year that we just completed, which means over 500, 501 to be precise that we have done over the last 4 years, not only is that driving our growth, but it positions us for the next rounds of growth. And the way that works is well illustrated by a meeting I had this week.

So just a couple of days ago, I met with the CEO of a new client, a $20 billion client -- annual revenue client. And they had come to us after looking at all of our competitors, and we're signing our first reinvention with them. It will be over $100 million.

Now our teams have already shaped Wave 2 and Wave 3. And by the end of breakfast, we had shaped Wave 4. This is how these reinventions compound, wave after wave because we're strategically positioned at the top of the house in the agenda that matters. And you can see that with our Diamond client relationships, which are our largest relationships, in FY '22, we had 267. We now have 317.

Now over the course of the 4 years, as we focused on reinventions, we also learned a lot. In just last year in the spirit of reinventing ourselves, we made the biggest organizational change in our history and created reinvention services which brought together all of our capabilities so that we could much more easily embed AI and data and move more quickly.

Just a few months ago, we created 7 units. We call them reinvention Partners. Each of them is organized around how our clients operate, invest and buy. And you're going to hear more about these later today with Manish.

That brings us to our second big strategic move, taking an early leadership position in AI. In November '22, when generative AI emerged, we moved quickly. Within months, we had announced a $3 billion 3-year investment specific to AI. The best way to evaluate our achievement is to look at the numbers.

So let's start with, in November 2022, we had a handful of generative AI projects going when it hit the public. Today, we've done over 18,000 advanced AI projects. I just want to pause for a moment because when people ask me what reinvention means, I point to the statistic. This is over 18,000 around the globe in multiple industries, from a starting standstill point of 5, right? What it takes to do this is not operating the same way we operated before. We changed the way we worked. We invested. We focused, and this is an incredible number that's climbing every day.

We also went from 40,000 AI and data professionals to nearly 110,000. We created 9 new business groups with the emerging companies that mattered in AI and data following our playbook that we build relationships with the technology company that matter to our clients. And we also expanded our AI and data partnerships with our largest ecosystem players. And today, all of them are at the center of the AI wave.

Just a couple of months ago, maybe it's only a month now. We also announced the creation of our new AI safety business using faculty, our AI specialized business that we launched that we bought earlier this year, and you're going to hear from Marc Warner, the CEO, a little bit later.

So when you think about why is it important? We took early leadership, mostly, it's because this is where the demand is going to grow and it's where our clients want to spend their money. But that early leadership also gave us another advantage. It helped us understand what the new growth would be. And our third strategic move has been to use acquisitions to accelerate into higher growth markets.

Our strong balance sheet and our strong free cash flow gives us investment capacity that we can flex up and flex down. And over the years, we've used that to accelerate growth. And when we look at our business today, we have a multibillion-dollar businesses that were built initially through a lot of inorganic cloud, cybersecurity, song, supply chain and engineering. So this is something we know how to do, and we think of our acquisition experience as a growth accelerator.

We have spent over the last 4 years, including the acquisitions that slipped into September $19 billion. We've done so for the three reasons we make acquisitions. The first is to scale markets where we're already in but have higher growth. So cybersecurity is one of them, traditional cybersecurity. And so you saw us, for example, buy CyberCX in Australia. The second reason is to deepen industry and functional skills. So we've done a lot of supply chain acquisitions. Those tend to be smaller and more niche.

And then the third reason is to expand into new markets. And this is where when you think about our move from $1.5 trillion to $3 trillion, this has contributed the most. Every technology reset creates new growth. And because we were in the center of what was happening with AI, we could see that there was going to be more opportunities than ever before, new markets, new buyers, new commercial models. And so this was the time to ratchet up our ability to use our competitive advantage, our knowledge of how to do acquisitions, our strong balance sheet, our strong cash flow and invest.

We have now created what we call a network of businesses. You're going to hear more about the specific businesses a little bit later. Those businesses followed a playbook. They were in large and growing markets, they would use the power of Accenture to achieve more than they could stand alone. They are AI-powered and AI resilient and they have revenue growth above the average of Accenture increasingly non-FTE models.

So that playbook, we've executed, and that brings us to our next chapter. Our next chapter is about scaled reinvention. The last 4 years, they've been about building, reinventing, expanding. The next chapter will be about scaling reinvention for our clients and for us. Our 3-year financial ambition is to achieve at least mid-single digit revenue growth in constant currency in FY '27 and FY '28 and then a step up in the range of growth from mid-single digit to high single digit starting in FY '29, and taking market share throughout the entire period.

Our ambition includes continuing to expand operating margin while investing at scale in our business and returning significant cash to shareholders. Angie is going to take you through more of the details at the end of the day.

Now our business goals are to continue to be the reinvention partner of choice and the leader in the safe widespread adoption of AI and to be the most client-focused AI-enabled Great Place to Work for our reinventors.

In the next chapter, our growth strategy has two components: Grow the core and scale the network. These are mutually reinforcing and our reinvention partners will drive both. And we will continue to invest organically and inorganically in both the core and the network. So let's start with the core.

The core was 94% of our business in FY '26, growing approximately mid-single digits. We see a few growth drivers in the core. Most of our growth will continue to come from large-scale reinventions. Those will have different flavors depending on the client. For many, it's still about building the digital core, modernizing ERP, modernizing the mainframe, moving to cloud, eliminating technical debt and creating the technology foundation needed for AI.

For others, it will be about transforming a function, creating investment capacity, doing cross enterprise transformation. And in all cases, our clients value that we're embedding more and more AI helping them execute on an AI strategy by leveraging our expertise and our platforms. And we're starting to see the green shoots of companies who are already more mature in their digital foundation to do enterprise-wide programs.

Another big driver will be creating the enterprise AI technology stack, which is necessary to actually run your company on AI. Everest recently put out a report that said, only about 5% of clients have the foundation, the enterprise AI technology to actually be able to scale AI in a repeatable way, and that's exactly what we see. This is a massive opportunity that is net new and will help drive our growth.

The third big driver as we see the core is going to be helping our clients use AI to do what they couldn't use before -- do before. Some of that's going to be about running the enterprise, but there's a lot of net new opportunities like agenetic commerce. And Manish will talk later about these growth arenas that we've identified that are expanding our addressable market and are part of our long-term growth plan as we scale. So that's the core.

Let's talk about the network. We're really excited about what we're building here. Today, it's only about 6% of Accenture, and it's growing double digit. But over time, we expect it to be a material contributor. And the network, what makes it different is that these are companies that leverage all of our strengths, but they use different operating and commercial models in order to grow. So we have two engines, the core and the network. But we also grow by who we sell to.

And so when you think about our client base, we have a ton of white space in our large enterprises and emerging large enterprises, which is where we've traditionally been. I just talked about the brand-new client, $20 billion of annual revenue. And we'll grow with them by leveraging our reinvention partners to grow our market share within a client and then we'll take our credentials and continue to grow with similar profiles based on our credentials. At the same time, we and our ecosystem believe that the mid-market is an important growth opportunity. The mid-market is growing -- is a $240 billion market growing high single digits.

Now we've always been in the mid-market, but not as a growth strategy. Last year, it was about 8% of Accenture. Over the last couple of years, we bought mid-market focused companies to test our strategy. And starting in FY '27, we are going intentionally into the mid-market to take market share. This means that we are able to grow by taking more of Accenture to more clients. And the mid-market will be part of our core and part of our network.

So before we go to the next section today, I just want to take a moment to summarize the big picture. We have invested for 4 years in building the foundation that we believe will allow us to step up our growth to mid-single digit to high single digit in FY '29. That belief is grounded on what we see today in our core and the network. It is also grounded in the reality that it is going to take some time to scale AI across the enterprise. And in the meantime, we're absorbing AI efficiencies in our business.

We also have a macro that's contributing to a challenging spending environment. And as we look forward, what we actually achieve will, of course, reflect what's happening in the macro, but we're not waiting for a meaningful tailwind, although we do expect over the next 3 years, some normalization to occur.

In our core, our growth will be large-scale reinventions. And as AI adoption accelerates, we'll have significant opportunities in existing work and in the new work that -- the new areas that we've identified. And our ambition is to grow on a very, very large base, mid-single digits while absorbing AI efficiencies.

And with respect to our network, we believe that we have the potential to double the size, it's currently 6%, by FY '29. So we're excited to now be able to dive deeper into those growth opportunities. And in a few moments, Manish Sharma, our Chief Strategy and Reinvention Services Officer, is going to come on stage to do so.

But before we do that, we're going to talk about -- we're going to hear directly from some of our ecosystem partners. And throughout the day, you'll hear more from our ecosystem partners as well as our clients. After Manish, John, our CEO of the Americas; and Mauro, our CEO of EMEA, will come and give you a market perspective. They'll take it from the client up and the market demand in our two biggest markets. I'll then be joined by Kate Clifford, our Chief Leadership and Human Resources Officer, to talk about how we're reskilling and upskilling and hiring the best talent in the world to make sure that we can hit our growth ambitions. Over lunch, you'll have the opportunity to meet with many of our senior leaders, and you've all been assigned a table to make sure that happens, and then Angie will take us home this afternoon.

So that's the day. Thanks again for investing the time. And now you'll hear directly from 5 of our ecosystem partners, all of whom we both use at Accenture and we help our clients use and then Manish will join us. Thank you.

Accenture is one of our largest and most strategic global systems integrator partners. At Reinvent 2025, they won 12 AWS partner awards, including GenAI Consulting Partner of the Year, GenAI Industry Solutions Partner of the Year and Global Consulting Partner of the Year. This is partly because Accenture's expertise in helping customers adopt and scale generative AI, along with launching transformative applications across industries and delivering significant contributions in revenue certifications and customer outcomes all on AWS.

Accenture was also one of the first global systems integrators to recognize the transformational power of our technology and build an entire business group around AWS, one that we've grown together, backed by dedicated AWS certified practitioners. We've been building together for a long time, and we keep delivering for customers. Together, AWS and Accenture have helped enterprises around the world modernize their business and adopt AI faster. In fact, customers who work with a partner like Accenture get their AI projects into production, about 25% faster.

One great example is NatWest, one of the largest banks in the U.K. We're helping them transform into a data and AI-powered bank. And through a 5-year collaboration together with Accenture, we're now modernizing how NatWest serves their 20 million customers with more personalized intelligent experiences. This kind of work requires AWS's cloud infrastructure, our AI services and security alongside Accenture's deep banking expertise and ability to execute at enterprise scale. When you combine those strengths, no challenge is too complex for our customers to take on. And that is why this partnership is so central to how customers adopt AI. I can't wait to see what Accenture and AWS do next for customers.

Congratulations on everything that Accenture has achieved.

Google Cloud's partnership with Accenture is built on combining our full stack AI capabilities with Accenture's deep industry expertise. For the fourth consecutive year, Accenture has been named Google Cloud's Global Partner of the Year. And Accenture ranks #1 globally in active Google Cloud certifications with nearly 50,000 Google Cloud skilled professionals.

Deploying Agentic AI is a top priority for enterprises today. But to achieve real business value, companies need to rethink how AI will impact processes across their organization. This is where Accenture comes in. We launched the Accenture Gemini Enterprise business group to meet clients wherever they are in their AI and data journeys. 1,000 forward-deployed Accenture engineers work directly inside customer environments, alongside our Google DeepMind and cloud engineering teams. They'll build agenting solutions tailored to how specific industries operate, including marketing, cybersecurity, AI and data, contact centers and solved programs. Together, we're helping enterprises move past pilot phases and delivering transmission at scale. Thank you for your continued partnership.

For decades, Microsoft and Accenture have enabled companies to harness the power of technology to achieve their highest aspirations. Together, we have helped organizations across industries and around the world, transform their business accelerate their digital journeys. Today, that partnership is more important than ever. With more than 95,000 Microsoft certified professionals and 21 Partner of the Year awards, Accenture brings tremendous depth and expertise across the Microsoft portfolio. But what really sets center apart is that they are not simply advising clients on becoming Frontier, they're living it themselves and at extraordinary scale.

Accenture has deployed Microsoft 365 Copilot to their 743,000 employees and is driving significant business process improvements with more than 50,000 developers using Getco Pilot. That first-hand expertise matters. It gives Accenture a unique understanding of both the AI opportunity and the complexity that it creates when you put it into the workforce across a massive enterprise. They are bringing that expertise to organizations like NHS England, Nestle, Repsol -- and so many more, helping them move from AI ambitions to meaningful business outcomes. I'm grateful for the work our teams have delivered together to serve our customers, and I look forward to the continued partnership on the Frontier transformation journey ahead. Thank you.

Accenture is one of our most valued partners. Julie and the team are trusted advisers to me and our leadership team, especially when it comes to how we can make sure Frontier AI is safe and useful for large organizations. On safety, Accenture is helping us do something no AI company has done before. Last month, I called on the AI industry to pace the frontier to ensure our capability and safety advance together. As part of that effort, we committed to bringing in independent evaluators to assess how we train and deploy frontier AI models, and we call on others to follow our lead.

This is new territory. It was not a decision we took lightly, and Accenture is the first partner that we chose. Led by faculty, Accenture's team will read team our models, study their alignment and test our safeguards. Both companies expect to invest at least $1 billion over the next 5 years in this -- This is money well spent because getting this right will both manage risk and bring forward this technology's profound benefits to humanity.

On AI usefulness and adoption, Accenture plays a similarly essential role. AI is advancing very quickly. 4 years ago, it could barely write a line of code. Today, more than 80% of all code committed to when tropics main code base is authored by Claude. Accenture is bringing this exponential capability more deeply into the world's organizations, to drive growth, increase productivity and to innovation. Accenture was one of our launch partners, for example, on enterprise frontier safeguards, which lets customers keep data on cloud infrastructure they control while still benefiting from entropic safeguards to detect mixed use.

EFS has been a significant unlock for companies looking to deploy frontier models in high-value sensitive context. Internally, Accenture also watched the walk making the most of AI's capabilities. About 115,000 of its people use Claude in their work every week. Accenture's anthropic Business Group has built a broad portfolio of Claude-powered offerings. These are now in use across 9 industries in 18 markets.

Cyber.ai launched in March with Claude as its reasoning engine. -- is putting Frontier capability to work in helping organizations defend against cyber attacks. Very few organizations can match Accenture's efforts here or their scale and economic impact. That's why we're proud to call them a global premier partner, our highest partnership here.

Thank you to Julie and the entire Accenture team for your leadership and your partnership now and in the future. My best wishes for the rest of today's session.

At opening, our mission is to ensure that AGI or artificial general intelligence, benefits fallout or people everywhere, as I like to say. Accenture's purpose to deliver on the promise of technology and human ingenuity speaks to that same belief. Progress matters when it makes life better for real people.

We're proud to recognize Accenture as an elite partner and is OpenAI's AI Transformation Partner of the Year. Together, we're helping clients move from AI that finds information to AI that gets worked on. asking actually doing.

With a leading global hotel chain, we're building an experience that lets travelers explore destinations and book their entire trip through ChatGPT. We're helping clinical research teams at a leading life sciences company, analyze complex findings and draft medical writing with experts guiding and reviewing the work. These capabilities give people more time to think, create, to solve problems.

Building on its $3 billion commitment to data and AI, Accenture is training 100,000 people on ChatGPT work and Thank you. Our teams worked through a technical center of excellence with 700 Accenture forward deployed experts specializing in GenAI, open clients realize value faster. -- of course, requires detected information, carefully tested systems and strong governance with safety and security built in from the very beginning.

Through OpenAI's Daybreak cyber program, we're partnering with Accenture to help secure the networks, data and core business applications that keep our world and our clients running. Our ambition is a future where people work alongside AI and ultimately, AGI, with the tools, confidence and the opportunity to just do extraordinary things.

Really excited to be here. I have a health warning before I start. So I think just seeing these videos and everything else, I get very excited. And as I get excited, I'll speak even faster. So Rod, you got to kind of just say if I'm speeding too much. So I thought it will be good to kind of get that out first. But I think thanks, Julie. It's great to hear from our ecosystem partners and the role we play in helping companies adopt and deploy their technologies.

As you heard from Julie, there are two important elements that define our growth strategy. First, it is about growing our core. And second, is scaling our network of businesses. You also heard that we have doubled our addressable market going from $1.5 trillion to $3 trillion. And here is the math to kind of explain what we are saying here. You got the base, which was about $1.5 trillion. We added with 20 new growth arenas, another $700 million to that. This brings to a total of $2.2 trillion addressable market in the core. Then we add $800 million for our network of businesses. In total, this makes a $3 trillion addressable market.

Now let me talk about how do we think about executing our strategy. And I'm going to break it into three parts, three. First, how we organize for growth and serve the enterprise through our reinvention partners. Second, how we grow the core. This is the vast majority, 94% of our business and is growing mid-single digit. We believe there is tremendous opportunity out there. In many ways, created by AI. Third, how we scale the network of businesses. This represents 6% of our business and is growing double digits. The network has been built through organic capabilities and acquisition focused on large, fast-growing areas.

I'll take a pause and kind of now go into the -- how do we serve the enterprise. We serve the key buyers and budget holders through our seven reinvention partners. Want my friend, who is the CEO of Americas, we were chatting and he said, "Manish, like nobody understands what a reinvention partner means." And I say, "Reinvention partners is just think about them as the key budget holders, the key decision-makers in any enterprise." It's as simple as that, just focused on the budget holders.

And -- as I look across the patch, it's very clear, every reinvention partner has a massive addressable market. We approach each of the seven areas the same. Start with the buyer and then understand the business outcome they need to achieve. These business outcomes are what clients measure to drive growth, improve performance and create resilience. And for example, any company that we take Chief Revenue Officer focused on revenue growth and customer loyalty. The Chief Supply Chain Officer is focused on new products and faster time to market. And the CIO cares about lower cost, about figuring out higher productivity, about security, about deploying AI at scale. Tying our success to our clients' business outcomes helps build the kind of us Julie referenced earlier.

I always like to think about tying AI to APS. Go away for the hype and the frenzy, it's about do I get the EPS out of the AI or not. From there, we bring the full power of Accenture. Our services, industry expertise, ecosystem, assets and platforms. And we do right from strategy to implementation, to operations, we serve our client end-to-end.

Now let's think about any company that you know, in pharma, in utilities, in banking, telco or any other industry, they all have financed. They all have HR. They have a digital core. They have customer, cybersecurity and most have supply chain. And next, think about the parts that are unique to an industry, unique, the core value chain. It is why our industry depth is so critical and where AI can unlock the most value. We call this partner, industry and enterprise. And for example, I'll give you a few of them, right?

For pharma, you have R&D. Utilities, there is the grid. For banking, it has got core banking, and telco has network. That is the logic behind the seven reinvention partners, or "the budget holders" who we are very working very closely with. For all the partners, AI is expanding the opportunity.

Clients can do things they have never been able to do before. This approach gives us several ways to grow, deeper with an existing client, access to new buyers and budgets or adding new clients. For example, we can start working with a client in digital core, move to the talent and then on to supply chain, so just kind of moving gradually. One reinvention naturally creates the opportunity for the next. This is our compounding growth model, deliver outcomes deepen the relationship and expand into the next opportunity.

Let's look closer at how this translates into growth in our core. As I mentioned, the core is about 94% of exchange. What's most crucial to remember about the core is that its biggest growth driver will continue to be large-scale reinventions for our clients. We see this reinvention show up in many different ways in our business.

Let's look at four examples, some illustrations, which can bring to life. Digital core, managed services, enterprise AI stack and AI growth arenas. First, digital core. Even after years and years of cloud and digital transformation, there is substantial work ahead to build the foundation, enterprises need for the AI era.

Many of these companies are on a multiyear journey and all will need to respond to the new tech evolution. And some of the stats which you will see here, right, modernizing ERP is only 41% complete.

Moving to the cloud, where only 28% of workloads have actually moved. So we're an AI, absolutely new thing that has emerged in the last few years as clients seek more control, we are seeing new demand in sovereign cloud already at $215 billion market. On modernizing the legacy technology, the hardest parts of technology estates are only starting to modernize. And I'll give an example on core banking.

In July of 2026, we announced a partnership with UniCredit and IBM to modernize the mainframe that runs a bank while moving the right workloads to the cloud. And in this, I would like you to hear from Arvind, CEO of IBM, on the importance of our partnership. can we roll the video?

Hello, Julie, and everyone, Accenture's Investor and Analyst Day. Accenture is one of the largest systems integrators and operators of IBM Technology with more than 30,000 of their employees holding IBM skills. IBM technology run the payments and core banking that cannot fail. Much of it runs on IBM Z. At those volumes, nothing matches it on cost, security or resilience. Together, IBM and Accenture keep modernizing these workloads, so clients can keep getting that return and IBM Z stays one of the most enduring platforms there is. That is hard work.

You have to understand decades of undocumented change. rebuild everything connected to it and migrate without going off-line. IBM Bob, our AI development partner makes Accenture's consultants more productive at this. We have also published a joint road map for clients modernizing an IBM Z. And Accenture has built IBM Apptio into the tools it uses to assess cost and value. So clients can see what the workloads on Z actually cost and what they're worth.

Same is true in AI. Clients want AI inside the systems they already run with governance, the regulators accept, Watson X to orchestrate and govern it with any model. Red Hat and HashiCorp to run and secure it on any cloud. Confluent for real-time data. Accenture consultants work with these IBM technologies in these systems every day.

Here's a great example of our partnership. In July, UniCredit, Accenture and IBM announced that we will build Europe's next-generation banking platform. Accenture leads the operation, IBM provides the technology underneath. I fundamentally believe the most demanding work is ahead of us. That is why this partnership matters now more than ever.

It's always good to hear from our partners because it kind of just tells you how we are uniquely leveraging our partnerships in different ways.

Now I want to move to the next section, which is around the managed services. Just to remind you, I'm still in the core. So that is my second section of the core, which we are talking about. I've been with the firm for -- my 32nd year, and I've been in almost every single part of Accenture's business over the last three decades. Managed Services is extremely dear and near to me. I spent most of my -- almost two decades in Managed Services.

AI has really opened up exciting new ways we can serve our clients in this space. Because the question always comes is about efficiencies. And I said, greater efficiencies have always, always created more demand. And I've seen this through my -- whether it was -- whether we came RPA, the cloud wave and all the things that came out, I've always seen that like it has always created more demand for us.

I think client come to us for the Managed Services as they are seeking a partner to run all or part of business function and achieve their desired business outcomes. This means they get better, faster and lower cost solution while getting expertise and talent. And we are at the helm of delivering business outcomes for our clients in a tangible and a sustained way.

Managed Services consists of multiyear contracts creating some of our largest, our most enduring relationship and represents 50% of our business. People ask me all the time, what is the impact of AI on Managed Services, always. This is like the favorite question in the last, I would say, maybe more than a year, right? And I will say AI is creating more demand.

In FY '26, we grew 7%, and we expect to grow in the coming years based on the opportunities we see in the Managed Services. we see AI really accelerating the Managed Services evolution. Traditional areas are changing. I remember our first one was in July of 2001, right? And we were doing all the Managed Services in different we got approval for doing 40 people that time and to remember even today that day.

In tech, agents are now embedded to run and modernize apps, that is a tech-managed services. In business processes, we have gone from key strokes and all the stuff that we used to do of transaction processing to delivering tangible sustained business outcomes for our clients. And we have new areas, new areas, which I think are still -- and I think they're exploding in some ways, like security, where we are defending against AI era threats. A $58 billion addressable market growing at 12%, new area. AI and data managed services a 110 addressable market growing at 15%. It encompasses enterprises, re-platforming for AI, monitoring agents in production, managing the data polls that are out there and addressing rising cost governance and the compliance pressure that we always have to kind of think about.

Finally, another massive thing for exciting thing for me, right, underneath all we have invention. AI. I just us seeing a report where it is kind of mentioned as the best, which is there on this planet, right. An integrated platform for identification and automation, where we have invested almost $1 billion over the last 4 years, reinvention.ai.

Now, to be to life, I think I want to kind of starter companies is a great example of large-scale cross-functional reinvention looks like. Julie recently sat down with Stephane, the CEO of Steel Auto companies to talk about the reinvention, he is driving across the enterprise. Let's hear from them now.

Well, Stephane, welcome. Thank you so much for joining. I know about a year ago, the two of us were in India at our delivery center with your entire leadership team, and you're going to be there again during our Investor and Analyst Day. So with your leadership team, so I really appreciate you building this in advance so that you could join us for Investor and Analyst Day.

No. And thank you so much, Julie, for being here. I'm so excited. I still remember this visit that we had together in India like it was yesterday and the leadership team and I had this view of like the scale, but more importantly, see the talent that you have like at our disposal today like in India. And I'm looking forward to be in your office, again, like in a couple of days.

Well, thanks. Very excited to welcome you back. And maybe we go back for a moment to actually the beginning because as we -- I know we're going to talk about the partnership. But at the very beginning, when you and I first had like -- those first couple of conversations, -- the thing that struck me was that you had this very clear strategy, but the pace at which you wanted to execute was faster, not only that we've seen at lot of companies in the past, but in across industries, like really fast, you want it to move, but also the scope. I mean you basically said to me, Julie, I'm looking at everything. I want your best views. I want to reinvent how we're working, and there's nothing that is out of scope. I want to just really think about differently. And so maybe like as we get started, you can talk a little bit about just who Estee Lauder companies is, not everybody may know you as well. But also what was kind of going through your mind at the beginning in terms of pace and scope and what you were trying to achieve.

First of all, thank you for giving me the opportunity to speak a little bit about the Estero company. As you know, we are celebrating our 80th anniversary today. So it's a special moment for us. And we have like more than 20 beauty prestige luxury brands around the world. Among some of them with 6 of them that are $1 billion brand, Lauder, Clinique, MAC, Jo Malone, Tom Ford and even La Mer...

And when I became CEO now approximately 18 months ago, when you and I had this discussion, we introduced our new vision, called Beauty ReMagine. And this vision was really to fundamentally transform the company at every level. We were looking at like our partners on how we were not going to do it, but we knew we needed it to do it fast. And that's where Accenture came into play.

You and I had like a discussion at the beginning was like a simple guidance that you were giving me like on the way that the world is evolving clearly, what I appreciate it's very early on understanding how we could transform and we can transform absolutely every level. And I'm happy to just like say that today, we are partners. And this partnership is actually more than just a cost-cutting partnership. It's actually a partnership that is going to help us as a company to reignite growth. And I'm very proud of where we are today, looking fast forward at the end of fiscal '26. We just finished 3 months ago, we are back to organic sales growth.

Our adjusted margin has increased by 300 basis points. And as an example, we have like 1/3 of our business online that is growing and gaining market share. So we are doing this transformation with speed. But this transformation needed more than just like growth.

We needed to, I would say, free up resources to be able to invest in growth. And that's where really Accenture came into play. And today, this partnership is allowing us to just simplify, to create efficiency, to create speed, but more importantly, to also get access to AI, an agentic strategy that is -- is giving us the opportunity to not only see today but also the future efficiency that we can bring to the company.

And Stephane, maybe give our investors a bit of a view of just how broad it is. And why you wanted to go so broad in terms of like how do you think about -- you talk about this being a platform for growth. So maybe we can just talk a little bit about the scope.

Beauty ReImagine for us is a broad vision in the center. We simply but boldly want to become the best consumer-centric beauty, prestige company in the world. And for that, we needed to be fast, we needed to be agile, and we needed to just like bring a set of partners that are just going to help us to do the transformation. I think the first thing that we had to realize is look at our own operation. How were we operating, and we were doing it in a very fragmented way with like a thousand of vendors and partners around the world.

So the way we were operating our brand and our regions and our functions was very disconnected and then you need to find a set of partners we will allow us to just change again, from a simplification and efficacy and, at the same time, also like speed. Today, when you look at the scope of what we had discussed at the beginning is there's not one area of the business that we are not looking into. Today, we are deploying our transformation, our simplification through like multiple transformation in finance, in supply chain, in technology, in people, in consumer care, but also in many marketing like pillars. So really, the fundamental transformation is allowing us to leverage our scale.

Because ultimately, we are a large -- one of the largest beauty company in the world, beautiful brands operating in 150 countries around the world, but very fragmented. We needed to just be able to face an increased competition around the world and bring simplification. So we can do best what we do, which is creativity, storytelling, invasion and also consumer experience at scale and leverage the power of the scale of the companies.

And you mentioned earlier, AI and Agentic AI, and that's an area that we are always talking about because it's so exciting. How do you about the technology and the AI component as you approached our partnership?

Very early on, on February 4, 2025, when we put the beauty margin, I've made it very clear that AI will be hardwired in absolutely everything we do. So I needed to find like a partner that was going to allow us not only to transform and to simplify and standardize, I think many of the processes that we didn't have in the company. But also the right AI and agentic strategy that will allow us to just like create the ongoing efficacy because I want to be very clear. What we are not doing together is not just a cost-cutting exercise because we were in the midst of a restructuring to rebuild operating margin. It is actually to create an ongoing efficient model that through AI and agentic, we will continue to unlock efficiencies over time.

Well, thanks. I mean, I -- this is something you and I have done together, which has been really exciting, but also your entire leadership team is involved because it is so broad. So maybe as we wrap up, it would be great in sort of just your terms, kind of your words is, why did you pick Accenture?

First of all, you are very convincing, actually. -- is very, very good. But I think I really enjoyed like this partnership like since the beginning because -- this transformation is the biggest transformation that this company has done in the last 80 years. It is a leadership. It is a cultural and it's an operational transformation. So we needed a set of partners that we're going to be able to just operate at the speed that you've described at the beginning, but also allows us to just create the efficiency. And this is where Accenture came into play.

And I think the way I would describe it is almost like four things that you and I looked at and that took into consideration in the decision to partner together. One was simplification. And Accenture is very well known for it, and you really help us, and you are helping us today because we are really deploying. We are going live as we speak in things like CRM, consumer care, tech platform. And frankly, everything is just going to be transferred, every single area of business is going to be transferred very, very soon. And so that was one of the first point.

The second point was I needed a partner that we're going to be able to just give us the right technology platform and the right AI tools. So you, amongst other partners are really helping us to transform these beautiful beauty companies to be really AI first in the way that we are going to see how we're going to look at like consumer insights, how we are operating -- meaning like operationalizing all our processes internal with like AI, how we are deploying in supply chain, how we're deploying it in consumer care, to ultimately do what we do best. And I think this is one of the things that was very important. But I think there's 2 other things that becomes more apparent as we are developing this partnership. It is also the tech infrastructure.

I have been very clear, we are hardwiring AI through everything that we are doing. And Accenture, like other partners is really helping us to really transform the company in so many that allows us to just improve our consumer insights, improve our supply chain, improve our financial processes and governance like across basically all like the pillars that we've discussed that we are deploying. And that's really key for us for ongoing and future benefit that we will create an efficiency for the company.

And the last point is, I know it is dear to your heart and to my how to is also the talent. And I think I saw that like the 2 of us since the beginning, having had this relationship. But the second thing is also the talent that you deploy and the talent that we have in our organization. And I don't think with this talent, we would have been able to just prepare with the speed at which we are operating, not only it is fast in estate company standard, but it is also fast in industry standards. And I think I'm really proud of what we are doing, and it's really thanks to the talent. So I don't tenets really helping us to develop and to really get the business outcome that we are looking at.

We are entering fiscal '27 in a very different company than we were in the past. We're much more agile. We simplified the organization. We are growing again, which was the objective that we give ourselves. And I know there's a share objective. I know Accenture doesn't look at this partnership only as a cost cutting, but as a partnership that helps us to free up resources for us to invest in where we want to be the best and where we want to be unique, and to just go back in growing and delighting consumer around the world. So I couldn't be more happy about the partnership that we are developing together.

Thank you, Stephan. Well, we feel really proud and privileged to be a part of it. Thank you for allowing us to partner. And it's great to see where you guys are going, your results and excited to see this next chapter. So -- take care.

Thank you.

What I described is exactly what Julie and I have been talking about what it takes to deliver reinvention at scale. At that means transforming how the company operates across finance, supply chain, technology, marketing and consumer care across 150 countries, 150. And I don't think there's any company on the planet that can kind of do this transformation across so many countries. It requires simplifying and standardizing processes, modernizing technology, embedding AI throughout the business and bringing people together the right talent to deliver change at speed, which Stephan described about.

I think Stephan also mentioned one important point. It's not about just reducing costs because sometimes we kind of get caught up about that. It is also about creating efficiencies that free up resources to invest in growth. In this case, they have invested in marketing operations to really drive an uplift in revenue. So that's kind of just our partnership with

Now I want to go on -- I'm still in the core. I'm going to now enterprise AI stack. This is the third example in our last scale reinventions that we're going to talk about. Simply put, the enterprise AI stack includes all the capabilities in an organization that needs to run AI at scale. You can think of it as the new systems integration work that we really, really need to kind of now help our clients with.

The enterprise AI stack has an addressable market of $500 billion. Is that like, wow, this market was just not there earlier. And simply put, it's not about putting a model on top of the building and the building gets automated and AI, right? And this is where there is real amount of hours and work that needs to be done as you are building the enterprise AI stack. It includes the seven layers which bring together the models, the data and the infrastructure that companies need to integrate AI into operations, modernize systems and turn my favorite experimentation into business outcomes, that is the kind of the real key on this.

Now safety is an important part of the stack. I think last month, we announced our partnership with Anthropic’. As you heard from Dario, we established a team of embedded evaluators to work alongside Anthropic's internal teams and safety partners to evaluate and let the models conduct alignment assessments and test model safeguards. And security is not just IT. It is also operational technology as well, and we will hear from CEO of Dragos, Rob, as more systems and assets are connected, will need more of this. The enterprise AI stack requires continuous care within an organization, and we see this market scaling. And I'll give you one stat because you're always looking for stats and proofs on this.

In FY '23, we had only four deals that were data and AI led, four. Today, we have just a data point. Now for bringing this to life, I recently had a chance to sit down with my clients and my friend, Kim Krogh Andersen from Telstra, a leading telco in Australia. In 2025, we launched a 7-year JV to reinvent work, customer experience and the ways of working, building the full enterprise stack from top to bottom. Let's roll the video to kind of hear from Kim.

Manish, thank you so much, and thanks for having me. The reason for us coming up with these untraditional partnership, the joint venture, the data and AI avenue that was really to accelerate our data and AI journey, take the 5-year road map and cut that down to 2 years, get the foundation in place and deliver on our ambition, which is to be an AI-first company.

If you look at all the stuff that we are doing, right? I think a strong foundation matters. What measurable returns are you seeing so far from accelerating the data potation to putting AI into production?

It become clear for us to scale AI in a sustainable way and create real value, but also fund the journey. We need to have some foundation in place. And this foundation for us was really to clean up our data estate. We are now down to [ 17 ] data platform. Years back, we were close to 100 data platforms. So that means now we can scale using less tokens, but we can also scale use cases because we have built any use case we do in a repeatable basis. On top of that, we are not just plumbing AI on top of existing processes. Together with Accenture, we have identified 8 cross-functional areas where we really believe there is big value.

One example is the front of house where we start seeing scale and real benefit. We also see it when we migrate from old systems to new systems, old products to new products, there we have really used AI to scale that in a very effective way. Reinvented the way we do sales and sales proposition in our enterprise business a way to generate these offers in days instead of ways, we have massive value pools in our network part of the operation. And we, of course, also have massive value pools when it comes to AI in the HR finance, corporate functions inside Helmand this partnership help us extract that value.

You have described the foundation as much more than data and technology. How do the capabilities such as the AI control plane, which I know it's your favorite and took an economics help telcos still AI with confidence?

I think a lot of companies, they will run out of money quite fast if they don't get control of the use cases and the way they use tokens and LLMs. We have an ambition to get all our use cases into what we call the control plan. the control plane, we have developed together with Accenture, a plane where we get control of each use cases from a sustainability, responsibility point of view. So we monitor any single use case and see if it's behaved as intended. We want to ensure that all the value we get from AI on most of that value is captured to Telstar. Telstar's customer, Australia.

Can you talk about the approaches you are taking at Telstar to enable people and help drive the adoption of AI?

Yes, we have been on a journey when it's come to adoption. The Telstar AI Academy is really a way to systematically give our employees specific AI expertise in a holistic manner. And actually, last year, we have more than 15,000 that all took a cause in that AI academy. We need to ensure that people understand AI because if you understand, you can also -- you know how to use AI in a more secure and responsible way. So that has been -- really we have been on from the beginning, even more critical is to really ensure that -- Manish, we are operating critical infrastructure as a telco. We cannot allow any risk when it's come to cyber, when it's come to resiliency, the whole way we have built the foundation together with Accenture is such a critical part of that.

As you have built this foundation, where are you now seeing the greatest business impact? Like can you share a couple of examples of [indiscernible]

Energy consumer because we run a lot of infrastructure. So there we have used AI early on. I will say I think the network and the network operation is probably where we have the biggest potential coming. What I really like with Accenture as our partner was, there was deep expertise in AI. There was deep expertise in the telco industry. And then there was also the willingness to invest and reinvent. We had an ambition to be an AI-first company, and you're really reinventing your company with AI and you ensure you have the right foundation in place.

And Accenture were the partner we looked at that had the most aligned ambition and we're most aggressive also because AI is about pace. We need to ensure we're in front of the pack. So we needed a partner that has access to all the new knowledge and always on top of what's coming next. We also needed a partner that we were a little bit more objective. We wanted to have some freedom to move between LLMs, between hyperscalers, between software providers, et cetera. We ended up with this joint venture with Accenture because we felt Accenture had the right capabilities, the right expertise and also the right ambition that resonated with our Connected Future 30 strategy. We wanted to reinvent. We want to be a leader, and we want to set the standards for the industry.

Thank you, Kim. Thank you for your time.

Welcome. It was a pleasure. Thank you, as always, Manish.

So I think you heard from Kim described the move from AI and data foundations to business outcomes from AI, really creating the full enterprise AI stack.

I want to just repeat the stat that I mentioned earlier. We had in FY '23, four 50 million-plus deals, four 50 million-plus deals. And today, we are talking about 50 -- 50 million-plus deals. Because I might have lost in my speed that I forgot to mention because there are large-scale reinventions driven by data and AI, 50 million-plus.

Now I want to now move to the fourth area. I'm still in the core and still trying to tell you how we are going to grow the core. And I'd like to highlight the real big -- and I think one of the most exciting things is about AI growth arenas. Now AI growth arenas are entirely new categories of demand created by AI. They did not exist 1 year back. AI is opening new possibilities and new budgets at almost every part of enterprise and with the new bias of our services. We are seeing demand in the last count, about 20 growth arenas. And each of them, we have industry, engineering, software and AI capabilities that position us to win.

I would like to highlight, I can go on for the full day, but I don't want to kill you guys, only two of the arenas. One is agentic commerce and the second one is software-defined vehicles. Now looking at agentic commerce. Very simply put, AI agents are changing who or what they discover, they recommend, they select and they buy products and services. In January, the pipeline was 0. We didn't have the service. Today, it is $1 billion, and we have already won $125 million as we speak. Today, it is $1 billion, and we have already won $125 million as we speak.

Looking at software-defined vehicles. The industry is changing. The software is becoming one of the most important components. Automakers across, they need to build and integrate more software and AI shorten the development life cycles and improve vehicles long after the ship. My friend, Daisy, and you meet her in a short time. She says, think of this as iPhone on wheels. -- is a simple explanation about that. Again, very small at the beginning of this year, on Jan 1, and now a $600 million pipeline and winning. The pattern is the same in all the 20 AI growth arenas, identify the opportunity early, build the capabilities and the credentials and be ready as demand scales. You heard me talk about now how we organize for growth. And you heard me talk about four of the ways we are growing our core business through large-scale reinventions. I talked about the digital core and the white space there, Managed Services, which I am still the most excited about. Enterprise AI stack and how we got from 40 million to 50 million-plus deals and the AI group arena. There's a lot of opportunity out there.

Now let me shift my focus to how we scale our network of businesses. Second part. So Chapter One. Now we're going to Chapter Two. The network of businesses represents 6% of our revenues today, growing at double digits. Today, we have nine network of businesses. For each of this, the model is the same, and it is simple. We identify large growing markets where Accenture has a right to win. We give the business, the operating and the commercial model it needs. We connect it to Accenture strength. We will dig into several of the businesses on the panel that follows this and then later with John and Mauro. But now let's talk few of them.

I want to talk about Accenture construct, DLB and Learn Vantage. So before we get into Accenture construct, I would like to share a little history first, really show how we build a network of businesses. In FY '22, we had a capital business of about $300 million. We saw the opportunity early, AI is digital, but it needs physical infrastructure. We move fast marrying existing capabilities and adding targeted acquisitions like Answer in August '23. And then we got DLB in July '26. 4 years later, as you can see in the graphics, the capital projects business is growing 55% with roughly half of that growth coming organically.

This is exactly how we want to use acquisitions, acquiring to accelerate our growth. Today, capital projects is comprised of two network of businesses, Accenture construct and DLB.

Let me talk about Accenture construct. We help clients' plan deliver and optimize large capital infrastructure programs. It's a $260 billion market driven by AI infrastructure, the energy transition and the aging infrastructure replacement. We already serve 19 of the 20 utilities in Americas, 19. And Mauro, my friend in EMEA, serves all the 20 utilities in Europe -- in EMEA. Construct opens the door to new buyers inside existing clients, leaders running major capital programs we couldn't reach before. And connected to our supply chain and the reengineering reinvention partner, we can now bring the full range of capabilities for which our clients need. Think '19 and '20, we couldn't serve them earlier. Now we are there ready to serve with a pretty new capability.

Next, DLB. Data centers are one of the clearest examples of AI driving capital investments. DLB associates bring specialized engineering, commissioning and consulting for data centers and mission-critical facilities. With DLB, we support clients across the entire data center life cycle, right from site selection through commissioning to decommissioning. This is a $12 billion market, $12 billion, expected to double by 2030.

Third one, Learn Vantage. As I said, we have got nine. I'm talking about period, we will dig into slightly even deeper in the panel. Learn Vantage is an AI-native learning platform serving a $110 billion market. As workforce capability is becoming a constraint on AI adoption, Learn Vantage really, really helps clients upskill their people in technology, data and AI at scale. And because it operates on a platform, it is also a non-FTE commercial model.

So as we look ahead, right, we will selectively expand our network into new markets. And I'm really looking forward to our next launch where we will bring to market products built using our deep industry and functional domain expertise. So I think the 9 will become 10, and it will continue to grow. So as I look across the network, -- these businesses are different, but the model is the same. Identify the opportunity early, build a differentiated position, use the right business model and scale it with the power of Accenture. And when I say the power of Accenture. We have got the world's largest client account lead who serve who are a trusted adviser who had orchestrate us in more than 4,000 clients across the world. Largest, I think, the intelligence as a trusted adviser, which is what we kind of bring.

Now that was a lot that I covered on this. I talked about how we organize for growth think budget holders to reach our key buyers and the budget holders. I talked about our core business, which goes through last year reinvention, driven by AI. And I finally talked about nine fit-for-purpose network of businesses to capitalize on large, fast-growing markets.

One of the most important points you have heard me say, and you will hear repeated throughout today is this. And I want to bring back to this chart, which is we have expanded our addressable market by 1.5 trillion to roughly $3 trillion. That is the math, and I kind of -- I was giving you a lot of times, but I wanted to summarize this again for you. And if you even need any more details, very happy to engage this is one of my favorite topics. The impact of AI is undeniable.

Now to help me bring this all to life and to dive also deeper, I would like to invite a few folks to join me on the stage. Lan Guan, who is our Chief AI and Data Officer; Tracey Countryman, she's a reinvention partner lead for supply chain and reengineering; Rob Lee, he is the CEO of Dragos. Our network of businesses function focused on OT security; Marc Warner, CEO of Faculty.ai, another network of businesses and now also the Chief Technology Officer for Accenture.

So I will try to moderate the panel, but these guys are a bit unruly. So sometimes a go on its own way, but I will try to do my best. I think one of the things I always find about Accenture even after 3 decades is just the incredible talent we have. And for me, when I just there's four leaders walking and I get these pumps like wow, we see who can -- who in the planet can have this kind of leaders.

So I'm going to start with Lan. I'm going to put you on the spot, Lan, on this because I talked a lot about enterprise AI stack $500 billion market and all the good stuff there. What does it really be in kitchen English? Well, otherwise, and I talked about it being the new systems integration work, but just explain to our investors and analysts. So what we are talking about in this

Sure. By the way, you did great. So in plain English, everybody can write code. Every company has cooked up some kind of AI pilot. But as what we talk to client every day, AI scaling is really hard. So Accenture builds an enterprise AI stack, and you can think of that as the industrial kitchen to scale AI. So since ChatGPT launched, we have done -- our team has done 18,000 projects. So we know where companies stand. Most of them have baked the cake in their grandma's kitchen, so to speak. So now it's time for them to get industrial kitchen. So that is equipment, repeatable processes, quality controls so that all of them can make the cake consistently and safely for millions of times. So that's what we mean by enterprise AI stack.

So this is not a quiz. Manish showed a 7-layer cake earlier. I -- this is what I do every day. 7-layer cake has data knowledge, models, agentic core, governance, security and safety, scale AI operations and then user front end, all of these need to be built on top of robust AI infrastructure. So my point here is made is only 1 ingredient. You need everything else in the kitchen to bake the cake. So this is a lot of work for us.

Also, I want to call out the work is durable. The work will never go away. Why? Because as new tech get launched every day, what does that mean to us? We have to do new work to integrate the new tech into the stack. Plus, Manish mentioned the AI-managed services before. The stack needs to be run all the time.

Why? Because model would drift, data will get out of shape over time. Also, we believe that redness drive demand. Because Manish, I hope you agree with me, the industrial kitchen cannot be filled on top of some kind of a crumbling foundation. So you talk about companies who do cloud migration modernization first. I will add to that. They also need to get their data AI ready as the new foundation.

Last but not the least, why this is so important to us? Because this role is very hard to replicate. I'm not bragging about it. I actually think we are the only one who can do this work because we bring a lot of the industry knowledge, a lot of AI expertise that is a perfect combination plus we have done this hundreds of times.

Thanks, Lan. It's always, I think, to demystify this enterprise stack, and that is why I got Lan to -- Lan, one client example where you can illustrate where you're seeing the -- how we are deploying this?

Yes, very quick client example. Actually, this is a personal project that I personally led. I think this is something that everybody can relate to. We built AI travel companion for a large airline. So Manish, I don't know about you, but to me, when I think about personal tree planning is what I -- is full of what I call moments of anxiety. There just in the air. So the system that we build needs to be able to handle billions of customer inquiries with a messy prompt just like this one. Oh, hey, I'm now your customer of your airline. I think I have $500 credit from the past cancel trip. And I want to take my family for the first time to Paris to stay in a great hotel, but I want to use that credit. How should I do it?

So this is an everyday question, but we have to build a stack to actually get the work done. So just initially, just like everybody else, we rush to the model. We connect to the very powerful models, but then the out-of-the-box LLM, doesn't know anything about the customer, doesn't know anything about airline specific things. So you have to give model the contact that is very, very important. We integrated a lot of data from airline. We also leverage a lot of airlines knowledge documents, lots of them are standard operating procedures, policy document, very long, very messy. So I have to do a lot of work to clean them and actually bring them into AI.

And we also build a lot of semantics. You probably heard the term before, that's basically giving meaning to data so that AI will actually disambiguate a fuzzy term like what I said earlier, a great hotel. Because great hotel means different things to different people. So this is the kind of work that we do behind the scene and then we build agents. Think about agents just like a team working in the kitchen and you have the orchestrator agent, that is a head chef, managing a bunch of specialist agent. And while we're building agents also is very critical for us to make sure that we do governance and safety and security, right, which I know my friend, Mark, is going to expand on that later. But we want to make sure that agents are following policy systems are secure and user funding, another layer that I talked about earlier, it's very important because many people don't realize.

Now users for agentic AI actually include both humans and agents. So humans interface there's so many choices. I got asked by my client every day, how do I get not get locked in. And then agent interface also needs to be highly operational. And then we need to put all this together is almost like the industrial kitchen needs to have a command center that everything is running smoothly. And all of this need to be built on top of a very robust infrastructure.

So I think the key takeaway I want you all to have from this example is enterprise AI stack is the link shipping to scale AI. Back to you.

I think you just -- and for me, like whenever I go for any discussion and people say, can you just quickly do some AI and get things out very quickly because it's like a really hard work, right? On this --

I mean -- we see this if you look back at history, always is the technology change to get the value goes hand-in-hand with the process change, whether that's like steam to electricity or containerization or computers -- original computers into enterprise, it's just bringing both together is the only way you ever get the value.

That's why I love this -- Let me go to Rob. New in Accenture is like -- 6 months, you are like 3 weeks -- so happy to see you here. For all of us. Yes, first of all, like IT, everybody is like grinding will go away OT. For you, you develop every night, you are worried about, oh my God, what's going to happen of us like really think about this. And it's going to become a $59 billion market. Tell us, Rob, more about this.

I mean, OT is the physical world. And no one notices it until someone hits the light switch or and talk and the lights go up and down, you go, oh, something is going on. And that's OT. It's the physical aspect of data centers, water, electricity grids, pipelines. It's our world. And all of the money has been spent on the enterprise IT side to secure it. and that's not effective in OT at all. And no CEO is confused about where they generate revenue. It's not the website, it's the operations side of the house. And so for governments and others to figure out that it's not protected, they're free cut out.

And we see it happening. We see 700 supermarkets across France in the last week, refrigeration units, turning up to heat, spoiling food, fires and supermarkets, people are scared. They're looking for partners. They're looking for answers. They're looking for solutions.

So for Dragos to have the technology for Accenture to have the reach, it's hitting a time in place that everybody is taking this seriously and needs an answer and it simply hasn't been done before. And you can't have an AI revolution or space revolution without energy and infrastructure. And you can't keep it very long without OT security. So I think it's just wonderful that we're doing it today.

I first met Rob in Dragos. It is pretty quick. Actually, it looks like it was yesterday, right? And it was -- I had a good tutorial on OT from Rob. He came to meet and was and I we together and we were really having a time. But it's just -- I think just shows again a massive opportunity that we have and how we are going to build on this. And with that, I'm going to go to Tracey. Now that every time he gives me this example and I start wondering, getting worried. But -- you are right in the middle of all the factories and all the plants and the maintenance and everything else going on. How has AI and security got to be designed as you are building this stuff?

So first of all, I am so glad you're with this. Because my best friend in what I do and what our teams do. So we are seeing AI in supply chain engineering across the patch, right? So it is early days. But if you think about it, R&D and engineering, right, core part of our clients' business, we're deploying AI with clients now to figure out new discoveries, formulations, new products. I mean an engineer spends 40% to 70% of his time depending on industry, looking for data, doing documentation, redoing engineering drawings and then trying to get it certified out the door, right? So agentic is brilliant for that space.

Second, once we've designed a product, we have to make with the source the product and the materials. So we're seeing AI and how we look at alternative materials and sourcing. We're seeing how we can leverage it for optimizing spend and used to be we just cover the top categories of spend. Now we can cover all the categories of spend, thank you, AI, right? Then once we have the materials and we've designed this product, we got to make it. manufacturing right, in facilities. I couldn't agree more. It is the heart of the client's business. It's literally the crown jewels.

And we're seeing AI being deployed with our clients, with our help and driving optimization, production, yield, quality, energy usage, right? Almost every client is looking at this on how they can save money but also create growth. And then the last part of our chain, supply chain, how do we forecast better? How do we plan better? How do we respond more to the customers' demand and the signals tie-up together to drive more resiliency. But we can't scale any of this without what Lan said, which is we got to have our OT data modernization in order. We've got to have the data foundations available. We got to have it secure. And we got to bring our workforce, our engineering and operations workforce and supply chain along in this journey, right, to be able to do this.

Let me give you an example of -- from a leading industry. So one of the top industries we have in automation is automotive. One of our clients, the Renault Group, they're a European car manufacturer. They've spent 7 years working on data foundations. So the core data foundations. And what does that mean? Well, it's across 20 plants. It's connecting 25,000 machines, 25,000. And I love the example of the billions of data for airline, but this is 5 billion data points a day, right? That's real-time operations and production, right, so that we can have cars.

Now that's interesting for the visibility and how that all comes together. But they've already saved by doing that and being able to look at what's happening in the lines, they've been on to save EUR 120 million a year, and they've been able to improve by 50% the ramp-up time for new cars, okay? So that's growth.

Now that they have this amazing data foundation, they're figuring out how do they do an agenetic layer on top to create an entirely new operating system for factories, right? The agentic layer that can figure out and anticipate disruption around energy, supply and anything that might change the output of those facilities from a network perspective, right? That's unbelievable. But the reality is what Julie said earlier, and that is, most of our clients are not there yet, right, on this journey on how they can bring that data set together, right? We're talking about decades old plants and equipment. We're talking about the fact that 75% of our companies are still doing manual operations. We can't talk about agentic over here when we're talking about stuff that's not digital yet, right? So the whole future is in front of us and how we're going to scale and help our clients get there is absolutely in the forefront in the very, very beginning. So to answer the second part of your question about OT security and body over here, it's absolutely imperative. It's not a nice to have. We cannot do the dial in the AI in these critical parts of our clients' business without OT security.

Rob?

I mean I love hearing this. I think it's the critical part of critical infrastructure. And again, people understand that outside these rooms and in the field. And no CEO wants to have a loss of human life, right? No national security leader wants to have velocity of life. And that's what we're talking about when we talk about cyber attacks on OT. It's catastrophic, it's power grid failures. We've already seen people try to kill people through cyber attacks.

Nobody wants to talk efficiency in comparison to loss of life. And everybody wants to secure those facilities. So I just think it's absurd sometimes that we spend so much time only focusing on one aspect of security when our entire world and our communities and our families depend on this other aspect. And again, I think that's only when you have trusted brands and partners like Accenture, you have all the expertise in one place. You have unique proprietary data models and insights that no one else has access to. And you can bring that into an outcome that not only takes businesses and allows them to transform, allows them to take advantage of AI, but it also protects people. And the combination of those two things is a good mission -- but of course, it's a huge market. And the combination of those is just going to be something that I think we're going to be talking about for the next 30 years.

Every time you give me with all the stats, you just make me worried actually.

I always come off with the doom and gloom guy at the panel. Yes.

And the thing which Rob was talking about not many people really understand the implications. And that is why I said I got my first tutorial from him in January in Davos. But then let me move to my next tutorial that I got, which was with Marc. So I met Marc in London when we were in the first discussion. We were near the white board, and we were fighting with each other who will take the pen on this. But Marc, we first met you and what I really liked about Marc and the team was -- they are a team of like 400 PhDs and all the good stuff, but their humility and being on the ground is incredible. And they talked about safety 10 years back when it was not very cool. Now everybody is suddenly looking up about safety, safety, oh my God, oh my God. But like these guys were talking about it 10 years back, but Marc, tell us about what does safe mean in the world of models?

Yes, I guess, well, we've sort of always cared about safety because it was always obvious to us that if you anticipated AI playing a much bigger role in society, failures that it would also just be more impactful. But I think sort of similar to what Rob and Tracey are saying, the thing people still miss is that safety is going to be the unlock to doing AI at speed and scale for exactly the sort of similar reasons that Rob is saying about OT security.

Safety, no CEO wants to bring AI into their organization that they can't trust and can't control. We think of it much more like -- imagine a car you have your brakes, which are sort of fundamentally a safety system. But if you want to do the fastest times growing around the track, you don't throw away your brakes. It's getting the safety systems, the brakes and the gas working together effectively to get us the fastest times. We think AI will be basically identical to that. And we're seeing it in practice. So in the U.K., we did one of the first agentic system builds in the finance sector for new bank called Tai, a growing business bank. And actually spotting the fraud there is to help them do for detection.

Spotting the fraud was quite easy it was building all the systems around that, many of the things that Lam was talking about actually that really took the time to get it right. And then once you do that, the -- like the value is just obvious and the organization can adopt it much, much more effectively.

No, like, again, -- this is where I tell you, like you can get anything away from the discussion, but just see the talent and the wisdom that these people have. So talk to us about applied AI. We talk about this, but you are not only safety, you are at the forefronts of Apply solving large business problems for life sciences clients and others. Talk to us about the launch of this new lab that we are talking about.

Yes. So I mean we think of ourselves as an applied AI company. So we started in 2014, back when there was only really people doing sort of more academic foundational research. But we could see AI was going to be incredibly important, but we thought that it mattered that it actually affected people's lives in a meaningful way. So whether it was making education better or health care or products cheaper, all these kind of things. So we actually coined the term applied AI to sort of illustrate what we wanted to specialize in. And right now, there is basically a sort of open problem which is AI is this extremely dynamic technology. Every day and week, months, certainly, there are new releases and the capabilities are just increasing and increasing. And organizations don't know how to bring that into their businesses. And so together with Accenture, we are going to invest in something that we're calling faculty lab, which is an organization that's going to sit at the intersection of from problems and real enterprise context, so that you can act like organizations can actually start to understand how to bring this into their businesses.

So think about something like software development. If you want to use coding agents as an individual, there's a ton of different blocks you could read. If you're a startup or a scale up, there's small amounts of good insight. But if you're an enterprise that wants to use it in a regulated environment, with tons of legacy systems across multiple geographies, multiple time zones, different teams, there's basically nothing on how you should manage that, how you should integrate these coding agents.

And that's a disaster when it's about 3x the number of software engineers that operate in enterprise as compared to start-ups. And so we're just not able to get the value out of the technology at the moment. But soon, we will be able to. I mean our -- the big deal that you were talking about with Anthropic basically came off the back of faculty investing in safety, very, very small amount over the last 5 to 10 years. We're going to have 10 or 100x that budget in the lab and a much broader remit. So I think we'll be able to see some equivalently exciting things come out.

you got what excited as usual. Now Rob, I'm going to come to you. You are running a very, very successful, very fast-growing business. Accenture also is doing this OT security and we are also the large cybersecurity business. What is the power of coming together here, right?

Yes. I mean for first, like Accenture has already been doing this. When I started this company 10 years ago, nobody was really doing a lot of OT security. It wasn't really a market. Investors weren't looking at it. They thought it was crazy. I remembered getting laughed at plenty of time in Silicon Valley. And now it's the biggest market people are chasing. But Accenture saw it 10 years ago. Nobody else was investing in the services and the expertise and the talent. A lot of your competitors, they were there, they left it. They sort of flooded with the idea, but they didn't stick with it. And nobody in manufacturing, nobody in oil and gas, wants to see somebody whose first OT environment is their OT market. That's just not going to work.

So for Accenture to have that level of commitment and talent, it resonates with the customers. For us, we have the technology and the expertise and the credibility as well. We don't have the reach and scale. By partnering with Accenture, we were able to immediately start opening up new geos and markets. We had customers come to us that trusted the Accenture brand and knew they could be successful. And these are huge projects. These are massive multimillion dollar deals, huge undertakings, and there are things that people haven't done before. Every CISO knows how to evaluate the next security technology in an IT market, they just need help. But not if they've never done the journey at all, they need a huge partnership. And they needed somebody to unlock the value, to operationalize it, not just to deploy the software, but many times to manage the security services after the fact. And to be there, unfortunately, when it's the worst day. Everybody's worst day is that incident that takes place. It's scary, something is going on. It's not anything that you've been through before. You need somebody to turn to.

And so for Accenture and Dragos to combine that credibility, that authenticity, the expertise the technology stack, it's just something that everybody has been waiting for. And I hear from a lot of our clients that it's now time for them to accelerate I hear a lot of CEOs going to the CISOs who don't understand OT historically, saying this is our #1 board priority. We will give you new budget. This isn't taking away from your IT budget. We will unlock new budget. Just please go fast. And it's going to be an Accenture Dragos combo that gets us there fast.

Thank you. I think what Rob just described, I think IT has been like focus and everybody is kind of talking about IT for a long time, OT is going to be one of the biggest, I think, expense that we are going to really see.

If I could say no President and head of state has ever talked to me at Davos or anywhere else about the printer. Nobody has never said like, "Hey, can you help me with my cell phone?" It's always been in a conflict scenario, in major attacks, how do you help secure our country. And all of that is just OT.

Yes. My answer is firing. Now I'll go to Tracey, back to you. Tracey, I think for the investors, I think they see us as an IT and we're doing all the functions and everything else. People with hard hats running around pace, right? Give us a window into like AI doing into this world of yours?

I love that hard hat. And steel...

I want to be the picture there.

Yes, a bit. Multiple things. So one really interesting example that you guys might find fascinating is that we were selected as a sole engineering partner for an aerospace company to do a lightweight transport 19-seater airline. And we did everything from early concept to structural design through digital replicas through documentation and certification. And we did that using AI in every part of that process, right? So one.

That's kind of the R&D example around manufacturing, a little different than Renault. That example is really with a company called Unilever. You guys might use Unilever products. You'll be happy to know that when we deployed digital twin in a North Carolina plant with Unilever, you probably got more deodorant. Kidding aside, we did deploy a digital twin, and that effectively is looking at line stoppages, right? And how can we drive a 95% reduction in line stoppages on our deodorant line? How do we drive improvement in capacity of 10% on existing assets? And how do we reduce the waste by 20% as one example. We have other examples with Manas. You'll be happy to know that you get really good, consistent manage as well using a digital twin.

I use those examples a little bit jokingly to bring it home. But the reality is we're working with clients to deploy AI on the shop floor to optimize their production lines, both for cost and safety, but also for throughput and growth.

And then the last example is then the hottest topic, sizes OT of core and besides AI and all these other things that it's really physical AI, right? One of the latest things and we're helping our clients figure out how they move things with robots and other capabilities. And one of the challenges with doing physical AI is that often to deploy it, you have to stop production, right? We don't like to stop production. If you go into a warehouse, we're talking millions of packages that are running 24/7 to go out the doors, right? So we partnered with a company called Kion, who is a leading provider of software and warehouse optimization, to simulate to do a physical replica of a thing like this room, to then simulate thousands of scenarios exactly as it was built, to train robots and models to be able to figure out how that's going to work with humans on a shop floor.

And then once we have the right layout, the right setting and the right design, then we go deploy it, right, facilities. That is the future of physical AI and where things are going, doing things before they physically get put in. So those are just some examples.

Our clients, frankly, need help, right? This is messy, it's complex, it's mission-critical for sure. And now they're looking and saying, how do we help people, how do we help our people on the shop floor, go on this journey and how do we help them transform with data, AI and cyber in a very fragmented and critical set of operations.

I would just link what Tracey just mentioned to the graphics we're showing earlier, $300 million grown to $1.9 billion, 55% compounding rate of growth. That is why it is so exciting. Thanks for sharing.

Now Lan, I'm going to end this conversation with you. Everyone here, we get new models coming out. There's open bet models coming out. What is so much confusion around this? And people are -- you explained how we make it work, but tell us a bit more about the model selection and the open-bid model, what does the clients need?

SP-6 Absolutely. So it's really hard for me to follow the race car example, airplane example. I don't know what I'm doing here. But to me, Manish, this is pretty straightforward. I think the logic is this model are super powerful, but they are not easy to use. So what we are seeing is companies not getting value and they pull back. So our job is to put those models to work. So the business get value and also model partners that you and I work with every day, they get consumption. So Manish, you and I see this in the earlier days with cloud. Companies were signing a very large significant consumption commit, but buying capacity is only the beginning. So we were the ones who actually help them move the application, connect the data, change the way they operate it so that those spend come in actually turn into productive use. So we're seeing this again with AI. Same logic.

When companies are not getting value, they retract from their AI investment and our model partners are not getting consumption. So that is the dilemma.

How do we get them on stock? Three things: First, we may model work. We work with a lot of companies to build repeatable industry solutions, and we also help clients build all kinds of harnesses and supporting infrastructure to make the model work. So our business clients get value. Second, we help companies select the right model for the right work, so that the total consumption will go up. If I ask 5 people here, what are your favorite models? I will likely get 50 different answers because there are 100 of models and new models are coming out every week.

So in fact, Manish, the tokenomics research that you and I did a couple of months ago, actually prove that there's only 10% to 20% of the enterprise workflow justify the use of the frontier model capabilities. So many companies just started realizing that not every model, every workflow deserves the most expensive model. So that is a work that we are helping them with by selecting the right model for the right work so that they get value and our model partners will get consumption.

Third, we also work with companies to utilize open wave models so that they can have better control over their data. Also, as you know, we -- just a few weeks ago, we launched our full life cycle services of helping companies onboard, manage and run open wave models in their secure environment. Yes, you heard it right, full life cycle services. So this will give companies many more options for them to use the models that they want. And this is even more critical in regulated industries such as health care and banking because they cannot lead their data and also the cost of managing their model is extremely critical. And regulatory compliance is nonnegotiable.

So we got -- too excited about talking about models here, but as a veteran in this business, I've been doing this for 20-something years, I cannot have thought and thrilled to be leading this business. And as Julie mentioned earlier, AI is the tailwind to our business, and I firmly believe in it.

Thank you. I think always good to chat. And when she says we did the economics, paper, you did it. We see an incredible team. I think they kind of did it. I think just wanted to kind of end this with kind of saying that we see a massive opportunity. We see a lot of white space in everything that we do. We see millions of hours of work, that work that needs to be done in deploying some of the technologies that we have just been talking about. I think the -- again, the model for us is the same, right, which is identify the opportunities early, invest, build organically, acquire and kind of scale with the Accenture's power. But that's kind of what you kind of got from the team here. But thank you for listening, and thank you for the discussion. I think we will continue the discussion over lunch. But now it's back to Julie.

Thanks, everyone. Great job. We now have a short break. So can join us and be back in your seat at 10:45. Our next section, we do have a live interview at one point with one of our clients. So it would be great if everybody can take a short break and be back here at 10:45. Thank you.

[Break]

Before that, we're going to hear, again, directly from some of our key ecosystem partners, many of whom we both use at Accenture and implement at our clients. And so first, we're going to roll that video, and then we'll go directly into John and Mauro, and then we're going to have a client join live from London. Let's roll the video.

Accenture and SAP have been serving customers together for more than 50 years, from the early days of enterprise software to the shift to the cloud and now into the age of AI. What makes this partnership unique is not simply its long history. It's that both companies share a fundamental belief. We know that real value is created when enterprises use innovation to reimagine the way they want and core. For decades, Accenture has been one of SAP's most strategic and trusted partners. Accenture combines industry expertise with large-scale business transformation. They understand how value is created across supply chains, finance organizations, customer operations and workforces. They understand how technology can transform those businesses in practical and measurable ways. And they deliver change at an enterprise scale that few firms can match. This becomes even more important in the age of AI to succeed today, companies must unlock the value of their data, essence and accelerate decision-making.

Accenture and SAP helping clients to do exactly that. batting SAP business AI across their core operations to create the autonomous enter points. as SAP goes all in on AI with our full focus on the business AI platform. We look to Accenture to build the agents on this blood to support our customers on this journey with change management and help them unlock the maximum value out of AI for their business. And as we do this, our partnership will only grow stronger. Accenture doesn't just advise clients on SAP transformation. They live it. They won many of their own mission-critical systems on SAP Technologies. And Accenture is one of the first to apply SAP's latest innovations to create efficiency and business value in their own business.

This experience makes them both a trusted adviser and practitioner. Looking ahead, the opportunity in front of us is significant. Every enterprise wants to realize the vision of the autonomous enterprise and transform for long-term growth. That's what SAP and Accenture deliver together, 50 years of partnership. And this is just the beginning.

For nearly 5 decades, Oracle's help organizations run their most critical operations. We understand their data, their industries, and what it takes to deliver results. Today, we're bringing that experience together with our infrastructure and applications to help customers put AI to work to deliver real, measurable outcomes. Accenture shares our focus on customer success and has been a leading global partner for 35 years. Together, Oracle and Accenture have certified tens of thousands of consultants worldwide. Accenture brings industry knowledge and implementation experience to help customers turn AI ambition into everyday practice. They've already built more than 300 custom agents in Oracle's AI agent studio, including agents for finance and procurement. And with our advanced intelligence engine for Oracle, they have delivered sovereign AI solutions across the globe. That work has helped make Accenture our #1 partner globally for Oracle ARR 1 in fiscal year 2026. What matters most is what we help customers accomplish together, moving faster, simplifying operations and giving their teams more time to focus on the work that drives their business. Thank you to Accenture for 35 years of partnership and commitment to our customers. I'm excited about what we'll deliver together next.

I'd like to say a few words about Workday's partnership with Accenture. It's a partnership that means a lot to us. Earlier this year, Accenture was named Workday's 2026 Partner of the Year for business impact in North America. That award reflects the huge role they play in guiding some of our largest customers to their toughest transformations. What sets Accenture a part is simple. They understand Workday for both sides as one of our most strategic partners and as one of our largest global customers. And that matters more than ever right now.

Every organization is trying to figure out how to move AI from experimentation to doing real trusted work inside the enterprise. Accenture brings scale, expertise and reinvention mindset to help make that happen. Better don't talk about it, they live it. Accenture is one of our most advanced and for leading customers when it comes to Workday AI, running more than 30 AI and agentic capabilities across their entire global workforce. That matters because when they advise clients is based on real-world practice, not theory.

They are also true co-innovators with us, often helping to pressure test new capabilities like Workday Data Cloud and higher score before we bring them to market. And that co-innovation translates directly into real impact for joint customers like GE, Venova, Salesforce and Question Energy. At the end of the day, great partnerships are built on shared values. Together, Workday and Accenture are grounded in trust, driven by customer impact and committed to shaping the future of work safely and responsibly. We are proud of what we built together, and even more excited about what is ahead.

It takes an ecosystem to change an industry, and that's why we are so proud to partner with Julie Sweet and Accenture. What makes this partnership so special is simple. We dream really big together, and we execute at mass scale. We're living through the greatest technology shift of our lifetime. CEOs don't need to know the pilot. They're done with that. They need growth margin expansion and stronger security. ServiceNow and Accenture closed the last mile of AI from technology to business reinvention. At ServiceNow, production AI deployments have increased ninefold in 9 months, and Accenture has been instrumental in helping our customers turn innovation into impact.

We're only just getting started. Accenture became the first partner in our joint forward deployed engineer program to really drive exceptional results. We're bringing our engineers closer to customers while unlocking over 300 prebuilt agent skills to accelerate the journey from experimentation to adoption. Every day, we come together to turn our customers' biggest challenges into their greatest opportunities. Together, we're combining ServiceNow's end-to-end cybersecurity platform with Accenture's AI capabilities to help our customers defend the enterprise at machine speed.

That's why Accenture is our #1 part globally in every market. Also our AI customer value and AI domain award winner for 2026. Congratulations, Accenture. The next chapter of AI will be defined by who turns intelligence into action. That's what we do together. And together, Accenture and ServiceNow will change the world.

Julie, I just want to thank you so much for 20 years of great partnership between Salesforce and Accenture. It's just been incredible over the last two decades, what we've done together, customer success, innovation, incredible. And I would just have to say that it's all built on the foundation of trust that both of our companies value so highly. We saw at Dreamforce when both of our companies came together to pioneer the work of Claude to use Slack with our largest and most important customers. And even some of this incredible next-generation work with our new AI Force platform. Well, the results were incredible. So I can't wait to see how this is going to come together over the next year.

It's been an incredible moment to be partnered with you and I cannot be more excited about the future. All the best, and thank you so much again for this great enduring partnership.

We deeply value our partnership with Accenture that we've built over the last 20-plus years. It's a global partnership across all 5 regions. And Accenture has been our Customer Experience orchestration Partner of the Year for 6 years now, and we're really proud to partner with them with hundreds of clients -- joint clients across the world.

When you think about the work that we have delivered for clients helping them deliver personalized experiences at scale to all of their customers, millions of customers around the world, over 1 trillion experiences that we deliver every year. What Accenture does of bringing that content and data and journeys together for our clients is key to helping our clients get the full value of the investments they make in Adobe technology.

And as we go into the world of AI, what's really important is helping our clients get the value of AI faster, more cost efficiently, securely and accurately. And that's what, together with Accenture, we are doing for all of our clients around the world as we deliver agentic software now for creativity, productivity and customer experience orchestration.

Accenture is a partner that has all 8 of our specializations. So they really cover the entire range of Adobe technologies across these 3 critical areas of creativity, productivity and customer experience orchestration. And I look forward to continuing to strengthen and build our partnership well into the era of AI. Thank you.

Okay. Good morning, everybody. My name is John Walsh. I am CEO for the Americas. It was really great to just hear from all of our ecosystem partners. And with me, I'm joined by my good friend, Mauro Macchi, who is the CEO of EMEA. As you might imagine, Mauro and I talk almost every day as we coordinate with all of our global clients as much as I wish the best for my good friend, Mauro, and his team in Europe, I'm always wishing America does just a little bit better.

Well, thank you, John. And well, I can also say that I can share this competitive spirit all the time with you, but they're really enjoying and it's great to be here.

So as Julie laid out our new foundation for growth and also Manish, a few minutes ago, show you what our invention partners bring to markets. Now John and I will take you to the client level. So we really want to focus on the demand environment in our two largest markets. How we build relationships and serve our clients and the white spaces that we see. And I believe that we come at this from different vantage points. So John, I mean, you and I talk to CEOs all the time. What is one thing you keep hearing?

If there's one thing that we're consistently hearing from CEOs is that right now, the cost of standing still is greater than the cost to change. And that's in spite of the fact that our clients are operating in one of the most complex environments we've seen in decades. Geopolitical conflict, changing trade relationships, rising sovereignty requirements and a higher cost capital is reshaping how companies invest and operate.

Supply chains are being reconfigured, as you heard from Tracey and cyber and OT have become board-level priorities. Every major investment right now is facing greater scrutiny for ROI. And at the same time, AI is creating extraordinary opportunity for growth and productivity. Across the Americas, we're seeing this translate into investment around five major themes. And I mentioned these because these are not short-term initiatives. These are cyclical spending -- or cyclical spending priorities. These are like long-term structural investment cycles that we expect enterprises and governments to invest in the next decade and beyond.

Together, they represent like hundreds of billions of dollars of technology, operations and transformational spending initiatives. I highlight them because as you heard from the panel today, Accenture really sits at the intersection of all 5. AI-powered reinvention to drive both growth and productivity. This is where the enterprise AI stack that you heard Lon and Manish talk about play such a critical role. Reindustrialization. Here in the U.S., everybody knows that's a huge priority and supply chain resilience, as Tracy talked about, absolutely critical. AI infrastructure and energy. We're involved in both the build of the data centers as well as the energy infrastructure, and you heard from Rob about securing it with OT. And then cost and productivity transformation and cyber, of course. As you heard from Manish earlier, these are the exact areas that we're investing in to grow our core business and our network. And while these themes might manifest differently across different industries, geographies and clients, like one thing is consistent. CEOs recognize that the combination of AI and new technology represents a once-in-a-generation opportunity, which is putting them under enormous pressure to act.

John, these are the same trends. These trends are also very important across Europe. And I think particularly as our clients really struggle with long-standing challenges like that we've been knowing for many years, slower productivity growth, higher, let's say, energy cost, fragmented cost, fragmented markets and slower adoption of technologies such as cloud and AI. I mean you remember the slide that Manish showed a few minutes ago about cloud 72% of further potential. This is particularly true across Europe. So those pressures are creating a new urgency to invest. And I see this really every day. So we see a big movement around modernization of Europe's infrastructure and industrial base with energy becoming a critical topic and public services becoming more efficient. So talking about Europe, I'd like to draw your attention to 3, let's say, major forces that are really shaping investments, and they are sovereignty, defense and security. And allow me to start with sovereignty. This has become central to the strategy of many of our largest public and private sector clients. More than 60% of European organization today actively seek for sovereign AI solutions. And this is because they're making fundamental choices and fundamental decision about where the data resides, how AI is going to be governed, which technologies they will use and especially how they can have greater control over critical capabilities. And it is clear that Europe's AI advantage will not come from outspending other markets, but it will come from putting its own strength to work like its deep industrial data and expertise in regulated industries. So they need the right data and the right partners who can help them navigate both global technology ecosystems and European providers. And that is why we are actively working across the full spectrum of our ecosystem. It was great again to see all these videos with all the leaders, and we work with their teams on a daily basis. And we work with the hyperscalers, but we also work with the European native players such as, for example, Mistral. And this is one of the new AI partnership that Julie mentioned early on. And we really help clients to make choices, make decisions because they need to find the right balance between sovereignty, between resiliency and global innovation, access to global innovation. So this was sovereignty. The second force is defense. I mean it's clear that Europe's military capabilities and need to adapt to the current geopolitics. And we see that there is an acceleration to close the gap, to modernize the technology infrastructure and manufacturing. So as defense spending increases, the focus is increasingly shifting from equipment alone to the technology, the data and operational capabilities. So this is also required for these players to deliver readiness and resilience. So for Accenture, this creates a lot of opportunities across the entire value chain. So modernizing the digital infrastructure, securing critical systems, for example, transforming engineering and manufacturing operations, strengthening the supply chains and helping defense organizations to adopt AI responsibly at scale. So after sovereignty and defense, the first topic is security. And security extends well beyond defense. And clearly, regulatory requirements, geopolitical tensions and a growing number of cyber threats are now driving major investments in cyber resilience. And this create a lot of large opportunities for transformation. So at the same time, we heard earlier on, protecting operational technology and critical infrastructure is becoming a top priority across all of the industries. So John, the urgency to invest differ by market. But I mean, see Europe is focused on scaling AI responsibly. The Middle East, you see clients that are using it to leapfrog older technologies and diversify their economy. So in all of our markets, this combination of traditional modernization and new technology adoption creates a very broad opportunity for Accenture. But at the end, the underlying imperative is the same. Our clients need to reinvent and they need help turning ambition into execution. And that is where knowing the client really matters. So you need to understand not just the technology, you need to understand the client strategy, need to understand the industry in which it operates. I'm glad that I saw in all the videos of ecosystem leaders talking about industry depth. We need to understand the core processes. We need to understand how the enterprise really operates on a day-by-day basis. So this is going to be extremely important and it's a great potential for us to develop in this context. John, does this resonate?

It absolutely resonates with me, and it brings me to kind of what I wanted to talk about next, which is how do we grow our client relationships and how do they compound over time. As Julie said in her opening, 85% of our top 200 commercial clients have been clients of ours for 20 years or more. Our client model starts with our client account needs, and we believe our client account needs are the best in the business. These are senior leaders who live and breathe every day inside of our clients' businesses. They earn the seat at the table after years of successfully delivering results at those clients, that work builds trust and deep, deep knowledge of the company. Their job is to bring in the reinvention services capabilities that Manish talked about this morning as well as our ecosystem partners to really solve any business challenge that our clients have and to help them drive growth. You heard from Stefan at Este Lauder this morning around how this works. And our client account leads, the first thing they do is they actually start with our client strategy. And from there, they determine what needs to change. Like we use data-driven analysis with our clients to help them understand the costs and the benefits of different transformation opportunities so that our clients then can prioritize around what creates the most value. The second thing that we do is we really help modernize the technology and the data foundations underneath the business. Like this is something you heard Rob talk about with the enterprise AI stack, and what a critical role that plays in helping to do that. And you have to recognize that when we engage with these large enterprises, we're generally dealing with decades of accumulated tech and process complexity. And the stakes are particularly high. Rob talked a little bit about it, but if you just think about it, it's mission-critical transactions in the infrastructure space, certainly, but financial transactions, health care records, personal information, often in regulated environments where reliability, security are absolutely essential. So moving pragmatically is not like a sign of weakness or a lack of appetite on the part of these clients. It's really responsible leadership. So scaling AI to drive sustainable competitive advantage is absolutely possible in these environments, but there is like no easy button to do it given the level of complexity. So once we've kind of like looked at that, we then begin the process of redesigning the enterprise processes and operating models. Again, this is not easy work. It's complex. It's time-consuming as the technology alone does not create the value. Organizations have to adopt the technology and then fundamentally change the way the work is performed to get the value. And then we work with our clients to deploy the changes at scale while helping their employees adopt the new ways of working. We do this through a combination of our consulting and managed services capabilities.

So let me give you an example of how this works in practice at Dell, which is a client that I spend a lot of time with. And as you might imagine, Dell has been a client and a partner of Accenture for many years. But with the rise of AI, our relationship has become more strategic than ever. What makes Dell unique is that for us, there's three things at once, like we call it a 360 relationship. So they're a key go-to-market partner. We work with them extensively in private and sovereign AI environments, as Mario described. They're a critical supplier to Accenture, where their products actually power our business. And then finally, they're a key client, where we work with them to use these technologies to reinvent their own business. So if I start with the reinventing, a few years ago, Dell launched an enterprise modernization program focused on increasing automation, intelligence and speed across the enterprise. We started with a focused data transformation, consolidating fragmented data environments across the breadth of Dell into a unified data platform, which created the single source of truth. That foundation we put in place was critical, and it gave Dell the ability to run their business with real-time visibility into operations, supply chain, performance, customer demand and ongoing decision-making, fundamentally changing the way they were able to run the business. That success like built trust and opened the door to the next phase of reinvention. And today, we're helping Dell scale AI across every part of the enterprise. Now at the same time as we were doing that, we also expanded our go-to-market relationship through the Accenture Dell Business Group. This is where we help governments and enterprises around the world deploy private and sovereign AI infrastructure in order to support their futures. And the results speak for themselves. Over the last three years, our Dell 360 relationship has more than doubled on each of the three dimensions. And Dell isn't unique. Many of our strongest client relationships follow the same pattern, which is how our relationships compound over time.

I think this is the key point about building client relationships, John, over time. And that progression is important. It's very important because we understand what AI means for our business. It makes us more efficient and give us a significant upside. So in other words, those efficiencies create the capacity to do more. So a client -- and I see this every day, client might start by asking us to take cost out, okay, take cost out of an existing operation. The savings then can help fund something that they wanted to do but could not afford in the past. So for example, modernize another system, improve customer service, build a new capability. So -- and that is a big part of how we expect to grow in an AI-enabled world. So create productivity, reinvest with our clients, expand the relationship and reinvent again. And for example, we've seen this with one of our global life science clients. I am an executive sponsor and proud executive sponsor of where over more than 10 years, our presence has been consistent. And that has allowed us to work across the entire enterprise. So we started with the research and development program in the business unit. We created success and success created trust. and that enabled us to expand well beyond the original scope in other business units and in Europe than worldwide. So we've been with this client through many technology cycles. And when Gen AI emerged a few years ago, we decided to invest and bring it into our managed services, reviewing our contracts and our commitments and share the value with the client. And that has created further trust to expand across other parts of the organization. And the productivity gains help fund a broader multiyear reinvention across commercial operations, manufacturing and R&D. So today, together, we are helping accelerate trials, reducing time to market for some of the most important and impactful medicines and therapies of our time with beneficial outcomes for treatment and recovery. So AI gave us efficiencies in how we serve the client, but it created even more opportunity for us to expand. And this is what I think you see in many of our clients.

And Mauro, I think we've demonstrated that compounding model over decades. We now have over 317 Diamond clients globally. These are our biggest relationships, and they're by no means transactional. The trust and depth of technical and functional understanding and our industry expertise is very, very difficult to replicate, which is why I have confidence that there is a lot of opportunity for us to continue to grow in our existing large enterprise clients. But there's also a significant amount of white space across the board. At the same time, emerging large enterprise and mid-market represent one of the largest growth opportunities that we see in Americas, and I think this is true globally.

I agree that this is true globally. And if I speak about Europe, specifically, this company, these segments are the backbone of the economy, and they face many challenges, many of the same challenges as the larger enterprise, but with less access to capital and skills. So our ambition is to bring them solutions that are ready to deploy, faster to implement and tailored to their needs. So our client strategy is simple, combine our industry-led approach with our ecosystem-led model to scale repeatable solution and serve a much broader client base. So I think for all these reasons, we are really very well positioned to win. And these are many of the considerations we shared today that give us the strength and the confidence because we understand the clients' environments, we understand the technology, and we are the bridge between the two.

Okay. So now I will invite back Julie for a conversation with Simon Roberts, the Sainsbury's CEOs to bring to life our long-standing strategic partnership. So Julie to you.

Thank you. Thank you. All right. Well, Simon is joining us live from London. So this is when all the tech guys in the room are like, please make it work, right? So I'm really excited to have Simon join us. I'll just wait until he comes on camera. Hi, Simon.

Hello, Julie. Julie, good to see you. All the technology is working from here in London. Good afternoon, everybody.

Great. I'm highly relieved. So Simon and I actually became the CEO within a few months of each other. And I've had the privilege of getting to personally sponsor our work at Sainsbury from the beginning back in 2020. And I've gotten to see what Simon's done up close, which is pretty amazing. I mean he's had a really huge impact he and his team, a very clear strategy. They've moved at a very fast pace. And so we feel very privileged to be a part of Simon's strategy, and I personally feel really privileged because it's been just fantastic to work with Simon. So Simon, thank you again for joining us. And because we do have a global audience, not everybody may know about Sainsbury and your strategy. So I thought we could just start there and you can talk to us a little bit about who -- what Sainsbury is and the strategy you've been driving.

Julie, thank you, and really great to be joining you this afternoon. And look, thanks for the introduction. Just a headline. So Sainsbury's is the second in the market here in the U.K. in grocery. We've been serving customers actually since 1869. And obviously, we work in a very intensely competitive industry, just as the same across the world in grocery and food retailing. And so we've been really focused on how we make sure we deliver for our customers a really differentiated experience. And to do that to what we were just hearing, making sure we create the efficiencies and the capacity as Mauro was just saying and I was hearing to actually be able to achieve more. And so we've got a plan really to make sure that we deliver for customers more reasons to shop with Sainsbury's and putting food first in our business is absolutely front and center, making sure we're creating the technology road map to really drive the change we need, and that's what we call our Save and Investor win plan. And then putting personalization and what we call Nectar, which we'll come back to Julie, right at the heart of how we connect with customers. And we're about 6 years into our turnaround plan now. And look, we've made some progress, but I'm sure as everyone in the room will be reflecting to there's just so much more opportunity as we look ahead. And that's, I think, really all about this conversation, which is how we partner together to find the solutions to really unlock that value.

Well, thanks, Simon. One of the things that's been so important is that our partnership began in one place and then has grown as your strategy has grown and evolved. And so maybe you can talk a little bit about the scope of our partnership and the role we've played as you've evolved your strategy and kind of moved from one success to the next success as you've driven this change.

Julie, look, it's such an important point. And I think just picking up on the previous conversation I was listening into, I think this has absolutely been a really clear example of our strategy and our relationship with Accenture working hand in hand. We've worked haven't we from a kind of services support partner to now a real enabler of business transformation. And that's what I feel really excited about. We've really shifted up the way our teams are working together. We've become much more about trust, transparency, partnership, much less transactional. And we've got, I think, real alignment on the strategic outcomes we are looking to achieve. We're just coming to the end of our kind of current 3-year plan, which has been all about putting food front and center in our business. And we're now imagining the next 3 to 5 years, and I just see that partnership mindset, really thinking about how our teams are coming together to imagine the possibilities and the value really stepping up. And I think it's not just about cost, right? I think the big theme that we certainly feel in our business and the way we're working, it's about building capabilities and building that continuous improvement. And more and more, our teams are talking about how Accenture are helping us unlock that so we can build that capacity, we can build that new vanguard of capability, and that's giving us real confidence as we look ahead.

And so Simon, we started with managed services, we started building our technology backbone. We expanded the different functions that we were doing managed services and now super exciting in our next extension of our partnership, what we're going to be doing in AI and helping you use it to drive growth. Maybe just talk a little bit about the business outcomes that we've delivered together because it is together. This is a real team. And so just to get a flavor of like what we've already done, and then we'll talk about kind of where we're going.

Yes. Super clear, Julie. Look, I think if I reflect on our partnership and how we work together, I really kind of think about the pyramid, right, which is we're really leaning in with each other kind of 45 degrees to understand the shared outcomes that we want to achieve. And that means from our business and our teams, we've got to be really open and clear with your teams about what it is we need to achieve. And also, let's be honest, facing into the change we need to accelerate. And so when I think about the outcomes, there are many that have just improved the way the business works today. We employ 140,000 colleagues right across Sainsbury's here in the U.K. And the work we've been doing in some of our HR self-service, for example, has just unleashed a totally new efficiency and friction-free environment for our people. We're working right now on a complete replatform of our core commercial capabilities in the business. It's right at the heart of a grocery business, how does the commercial function work. And it's the work the teams have been doing to really deliver that big ERP change is just coming through really strongly. And then as I think about how we're looking at the way grocery stores have been working, need to work, need to accelerate the change that's happening in there. It's just a really kind of joined-up mindset about what is it to deliver a really differentiated customer experience. What is it to create more capacity in our operating model? How do we need to rewrite our processes such that we can become the first choice of food here in the U.K. And look, just to everyone there in New York, we feel pretty ambitious and pretty excited actually about what we can do. We think we've come quite a long way over the last period of time. But as we all know, there's just such an imperative to accelerate the pace of change and to make sure we take our colleagues and our customers with us as we do that. And so right at the heart of this partnership, as I say, it's about becoming more efficient, but it's about unlocking that capacity, that capability, that confidence to really drive forward.

And Simon, part of what we've been able to do is use AI to actually give you more productivity and more efficiency, which you've been reinvesting in the business, and we're excited that we've just extended our partnership to 2033. Is there anything specifically like you're most excited in what we're going to do in this next chapter? And then I'll get to our last question.

Yes. Julie, it's such a good question. And look, I'm really conscious because we're going to be updating our kind of investor audience in a couple of weeks' time. So I'll talk a little bit about some of the things that we're working on, but also just to kind of really sort of reflect the fact that this is a very focused program on this year, the next couple of years. But I want to see the next decade. And we're feeling very engaged in the long-term partnership that we've built together because when we think about, as I say, customer experience in this industry, customers are going to be expecting businesses like the one I'm part of to really change and adapt more quickly as people want to shop differently. They want to engage in the physical experience in store differently. They want to be able to shop online and on demand more quickly. They want different recommendations of the kind of products they want to buy. And so we're really thinking about how we're going to get in front of that curve and giving ultimately our customers more reasons to shop with Sainsbury's. And what's this all about in the end? It's about creating competitive points of difference that we can stand really apart in what is a very competitive industry. And the work we're doing together, and we've now started to map out for the next kind of 3, 5 and 10 years, I think, is going to really face into how does AI unlock not just efficiency and cost, but in my mind, a real gear shift in the customer experience. And that's what I feel together, we're really excited about, right? Because it is about improving the way we serve millions of customers every day. And in an industry, we're serving 20 million, 25 million customers a week. It's a lot of people to have a more positive impact on than we can with today's capabilities. And so when we look ahead, the kind of thought leadership, the capacity we're building in our partnership, and the way we're starting to imagine that. I would just pull up actually, Julian, we had a fantastic experience in India at a midway point of last year when I just felt the whole ambition of what we could do together really step up. And it's one of those shared experiences when you're kind of staring at the opportunity, but you're also facing into the challenges that are out there in the market and really working out together how you're going to unlock that. And that's why as we both know together with our teams, we've committed to this partnership because we really want to become the first choice of food here in the U.K. with our customers, and this is going to really underpin that.

Well, great. Maybe just as a final wrap-up in your words, why have you chosen Accenture for all these years?

Yes. Well, look, I think right at the heart of this conversation, and we've discussed this many times over, is about trust, isn't it? It's about trust and commitment and credibility to drive powerful change. When I think about a retail business, retail businesses across the world, they involve thousands of decisions every day that affect customers and stores and colleagues and range and promotions and pricing and the way our supply chains work. And so at the heart of this, we're very excited to partner with you to create the next generation of how the retail operating model, the grocery operating model is going to work and to unlock those solutions together powered by technology. We look at this opportunity as a challenging one, but one we're really engaged with because you can see the flywheel effect, which is we create the capabilities, we create the capacity. That means we can invest in a way that drives the returns our shareholders expect, and that in and of itself creates the competitive advantage that is so at the core of winning in this industry and ultimately of serving customers every day. So that trusted partnership, Julie. Thank you for all that's happened to get us to this point, and we're really excited as we take it forward into the next exciting chapter.

Well, Simon, thank you for allowing us to be part of this journey. We feel really grateful and appreciative. Thanks for joining us from London, and I look forward to seeing you in London soon. Thank you. Take care.

See you soon, Julie. Thanks, everybody.

So as they're bringing on another chair where I'm going to be joined by our Chief Leadership and Human Resources Officer. One of the things I hope that you've noticed is that every one of the leaders you're hearing from, right, is working directly with clients, right? This is such an important part of our model. We stay on top of trends. We're close to our clients from the very top to the very bottom because clients are at the center of everything we do. And the only way to serve clients in that way is, in fact, to be at clients. So it's an exciting part of our model. Our leaders at Accenture get the most energy when they're together with their clients.

So Kate, can you please come join us.

So Kate might say she does the best job at Accenture because she gets to work with our people. And you've heard a lot about our people from our various clients and our great talent. But I'm going to start with the #1 question I get, which is, why do you have still so many people in the age of AI? So Kate, over to you.

Well, thanks, Julie. And the question I get is you have so many people and are you going to continue to grow our headcount, right, as you already have 800,000 amazing reinventors across the world? And the answer is yes. I do expect that we're going to continue to grow headcount. You heard from Manish, and I think he even bookended with we're going to double our TAM, or we have doubled our TAM. And with that incredible growth, we will need incredible people. And my job is to ensure that we are always fulfilling our client demand, that we have the right talent at the right time to meet that demand. And I really think about it in 3 ways, right? First is our core. We will continue to add in our core, and that includes entry-level talent. We will also continue to add talent in our new businesses. You heard from Rob, you heard from Marc, Accenture Construct with Tracey. Those are brand-new businesses, brand-new jobs. and we will add talent to fulfill that incredible double-digit demand. And then finally, as we acquire new companies, we'll bring on that talent as well. But we will continue to manage our supply and demand with the rigor that we have. You talked about in your opening 93% utilization in Q4. And we've been running in the low 90s for the last 23 quarters, and we will continue that same rigor over our future. Now I do expect that we'll slow the pace of bringing in talent a bit, but we will always have the talent that we need to fulfill our demand.

So let's talk a little bit about the new jobs that are being created, both the jobs in the new businesses, you've talked about Tracey, but how about the new jobs in the core business, and how we're preparing people for those jobs?

Yes. It's a great question because I do also believe, in addition to bringing on the talent to fulfill our clients' need, my job is to create the workforce of the future. I was reading something from the West a while back that said that there's going to be 170 million new roles in the next 5 years due to AI. And don't worry, I'm not creating 170 million new roles. But we are going to systematically change our jobs. We are re-architecting the jobs for everyone who works at Accenture. And it's really anchored in 4 key skills. It starts with data and AI. We expect every single one of our reinventors to have a data and AI foundation, industry skills, and that's the industries of our clients. So whether it's banking or retail or life sciences or the industry of our clients, functional skills, so whether that's HR or finance, sales, marketing, they deeply need to understand the functional work they're doing for our clients. And then finally, our technology ecosystem. And we're architecting new jobs with those four skills as the foundation. Now the depth of the skill will change based on the work that our people do every day, but our competitive advantage is that people will have skills across all four. And we have a long history of investing in learning. You've talked about it before. We invest $1 billion in learning. How we spend that money changes, right? We change and evolve where we spend those dollars. So for example, we've spent a lot of time talking today about ecosystem partners. We have over 134,000 unique certifications across our AI and data ecosystem partners.

And one of our aspirations is to be really seen as the elite university among corporates because the skills are changing so much, right, that we have to like really, really keep up for them with them. And so how do you think about how we're doing the skilling, and how you're giving -- sort of taking the skilling and then creating these new jobs and maybe give an example of really how we've gone from new job to skilling and what that looks like.

Yes. I'm happy to. And again, I think about it two ways. There's brand-new jobs, I'll talk about an example of that. And then we also have jobs that aren't brand new, like we've had them for a while, but they're really changing. And so an example of a brand-new job, and you heard it in some of the videos this morning, is this forward deployed engineer, this FDE. And 18 months ago, we didn't have any FDEs at Accenture. It's a brand-new job not only for us, but it's a brand-new job in the market. And that job is -- I think about it, it's the intersection of a software engineer, a solution architect, a product manager and a consultant, all wrapped up into one who's solving real client problems to drive business outcomes. Again, a brand-new job. And whenever we have a new job, I always go out and scan the market, okay, we've got this new work. How am I going to go hire for this brand-new talent. And when I went out and scanned the market, I saw there was not that many people who had this skill set, right? I think it was like roughly 2,000 across the market that had this skill. So we knew we had to build it. So to your point, how did we build these skills? And we partnered really closely with Lan and with Marc, who you heard from earlier, to build a 14-week program that takes a software engineer and makes them into or upskills them to become a forward deployed engineer. And in that 14 weeks, it's not just training. It's real practical application of those skills in a client environment. It's hackathons, and it culminates in a certification. So we don't call someone a forward-deployed engineer unless they have a certification in one of our partner -- AI and data partners, so that's a brand-new job. Another example is just jobs that are changing. You heard from Tracy earlier, she talked about supply chain and engineering. And I think about a supply chain process consultant. A supply chain process consultant used to spend time process mapping, and they were really deep in the function, but that was really where it stopped. And so we're now focused on upskilling those process people to understand robotics, to understand physical AI, right, work that they haven't had to do before because it's not just about mapping out the process, it's really re-architecting the work.

Now we're very proud that we are #4 on the Great Place to Work global ranking, which is across industry. Can you talk a little bit about why it's so important and why we look at that metric?

Yes. I'm also very proud. And one of the reasons I'm very proud is because that's directly from the voice of all of our people, right? That's not something we can apply for. That comes in feedback from our global reinventors, and we hear the things that they love about working here and the things that we need to work on. And we welcome both of those things so that we can continue to take action to be the #4 and even better than #4. But the reason that we should care about it is really two things. One, it drives real outcomes. Engaged teams drive higher profitability. Engaged teams are better with clients, and our clients tell us this through their NPS scores. We see client scores come in 8 points higher with engaged teams. And then leaders are 2x less likely to leave if they're engaged. And if they're engaged, they're engaging their teams. So there's real outcomes that matter when it comes to engagement. And then second, I talked about being out in the market and attracting new talent. As I'm talking to candidates, whether they're very senior or junior, they often proactively bring this up to me. I say, why did you pick up the phone, Accenture called, why did you pick up the phone? And they will say, I want to work at a great place to work. I want to work in your culture.

Well, now it's not a competition, but there's a little bit of competition at Accenture for who gets the resources to embed AI first. And as you can imagine, with over 800,000 people, our talent supply chain, there's a lot of value. And so you have been one of the leaders in using AI to deliver business outcomes for Accenture. So as a final question, why don't we just wrap up with a little bit about how we're our own best credential and you're leading the way.

Well, thank you for that. And we are our own best credential. What we're out doing with our clients, we have to experience ourselves. And oftentimes, we are architecting it with our practitioners together. And what Julie is referring to is what we've done in recruiting, where we've changed, we call it frictionless recruiting, which is really our Agentic recruiting solution. And it started from a place of we knew our recruiters needed to spend time with candidates. They needed to spend time sourcing, but where they were actually spending their time was scheduling interviews, where they were actually spending their time was typing information into a system. And so we put in an Agentic recruiting solution where agents now do all the interview scheduling. Agents can screen a resume. We have assessments that deeply understand the skills of the candidates to help that process move much, much faster. Now a human always makes a hiring decision. A hiring manager always makes the hiring decision, but the Agentic process has allowed us to reduce our time to bill by 28% and improve candidate quality by 30%. Now those are real outcomes. And so it allows our recruiters to spend more time outsourcing. It allows them to spend more time helping candidates navigate this process. And it's allowed us to increase our time to productivity. So it used to take 6 months for an entry-level hire to become productive. Now it's 6 weeks, which is 5 more months revenue for you, Julie.

Well, thank you, Kate. We're very fortunate to have you leading our leadership and HR function. We could spend a lot more time on this, but I know that people are anxious to have a break and to have lunch. So thank you for joining us today.

We're now going to take a lunch break at the back of your name tag, you'll have an assigned table. Please be back in your seats at 1:00. And if you just go out the doors, there'll be people to tell you where to go, but it's basically where you registered in the room next to it. So thanks so much. We'll see you during lunch. Take care.

[Break]

Hi, everyone. Angie Park, CFO of Accenture. We're going to bring her. Ready? All right, so you heard today about our strategy where the growth is coming from, and how we have positioned Accenture for sustained long-term growth. My job is to tell you My job is to show you the financial blueprint. I'll walk you through three things today. What we've delivered; second, where we are going; and third, how are we going to get there. So let me start with what we've delivered. In '26 -- in FY '26, we achieved or exceeded every measure of our guidance that we provided at the beginning of the year. Revenue was $74.2 billion, up 5% in local currency, adding $4.5 billion of incremental revenue. Our adjusted operating margin expanded 20 basis points. Our EPS grew 8% to $13.97. Free cash flow was $11.6 billion, a 1.4 free cash flow to net income ratio.

And we returned $11.5 billion of cash to shareholders. And at the same time, we deployed $4.9 billion across 17 acquisitions, and we invested over $3 billion in operating investments, R&D, learning and acquisition-related costs.

Now taking a step back, over the last 4 years, you heard Julie mention, we added $12.6 million of revenue. We expanded our operating margin 60 basis points. Our adjusted EPS grew at a 7% CAGR. We invested $13 billion in our business. And we deployed approximately $19 billion in acquisitions, which includes the capital related to the cyber OT transactions that we closed in September.

And we returned $35 million of cash to our shareholders. So we grew the business. We expanded margins. We invested at scale. -- and we returned significant capital to our shareholders.

This is the financial model that we have successfully executed over a over again. So turning to where we are going. As Julie mentioned, we have 3 financial ambitions. First is growth. Our ambition is to deliver at least mid-single digit constant currency revenue growth in FY '27 and in FY '28 and to set up our growth trajectory to mid- to high single digits beginning in FY '29.

And remember, this ambition takes into account that we will be absorbing AI efficiencies while delivering that growth. At the midpoint of the range for FY '27, our implied organic constant currency growth is approximately 2% to 2.5%. And if it happens in the range, it's 3.5% to 4%. Second, sustainable margin expansion.

So we expect to expand our operating margin 10 to 30 basis points per year in '27, '28 and '29, while absorbing significant investments in our business and our people. And over this time period, we will use our efficiencies from AI-enabled productivity in 3 rates. Passing value to clients, reinvesting the majority of what we retain into growth and continuing to expand operating margin every year.

We believe that, that is the right balance for long-term shareholder value creation. Third, disciplined capital allocation. So our business consistently generates strong cash flow.

This matters because it gives us the ability to significantly invest in our business and our people. both organically and inorganically and return cash to shareholders. So through FY '29, what we expect to do is to maintain a free cash flow to net income ratio of at least 1.1, grow our dividend with a payout ratio in the mid- to high 40s as a percentage of prior year net income. We're going to repurchase more shares than we issue.

And of course, we will invest in our business organically and through acquisitions that are ultimately to fuel organic growth. So let's now talk about the financial model that gets us there. So our financial model has 3 components. You know it, revenue growth, margin expansion, disciplined investment in capital allocation.

So let's start with revenue growth. So you've heard today how our strategy positions us to capture the significant opportunities that we see ahead. An our path for stepping up growth in FY '29.

I do want to highlight one strength as you think about our financial resilience over decades. We have a broad and diverse business. We operate at scale across 3 geographic markets and in 13 industries we have a balanced business across consulting and maintenance services.

You heard today about our new growth model even reinvention partners, our new network of businesses and the expansion into the mid-market, all strategic news that both position us for more growth and strengthen our base. This diversity has allowed us to perform through different market cycles and gives us the confidence in our long-term growth ambition.

The next component is our financial model and is margin expansion. So our model is designed to do 2 things: create investment capacity and expand the margins at the same time. For levers, 4 things that we look at. First, pricing and contract profitability. We define pricing as the contract profitability or the margin on the work that we sell.

Our focus is to maintain and improve pricing, and it can fluctuate based upon several factors, the competitive intensity, the mix of work that we sell and the size of our deals. Our FY '27 operating margin expansion, it reflects continued competitive intensity, and we work to maintain attractive contract economics.

Second, how we deliver. So of course, is making our delivery model more productive. In FY '26, our revenue per person increased approximately 5% and which was in part due to the efficiencies and how we deliver our services. We measure this on a rolling 4-quarter basis with revenue adjusted for FX using our average quarterly headcount and it excludes some subcontractors, whose contribution has been consistent.

As Kate mentioned earlier, we don't expect to grow our revenue headcount at the same pace as we have historically. And our goal is for revenue per person to improve again in FY '27. Growing our revenue faster than our head count.

Third, how we run Accenture. So over the last 4 years, we have reduced the cost of our corporate functions as a percentage of revenue by more than 200 basis points. And we've reinvested those savings. We will continue to deploy AI across our enterprise, reducing costs so that we can reinvest in the business.

So let me give you an example. Contract to Cash. This process spans legal, sales and fines and attaches 9,000 clients 60,000 contracts, 900,000 invoices. We're reinventing this process and using an identic solution. from contract review to sales bookings to billings and ultimately to collections. We expect it's going to create significant value over time, including freeing up capacity.

And the financial point is simple. Productivity creates capacity, capacity funds growth. Fourth, supply and demand management Julie and Kay talked about this earlier today, 93% utilization rates, it creates operating leverage.

So we're deploying advanced AI and reinventing processes to both better match our skills and capacity with demand and to shorten recruiting times. Together, these 4 levers, they create the capacity to invest in our business and expand margins.

So let me turn to the last component of our financial model, which is disciplined capital allocation. As you know, it begins with strong cash flow. For FY '27, we expect our free cash flow of about $11 billion to $11.8 billion, representing a free cash flow to net income of 1.2 to 1.3.

And we expect to return $9.5 billion to our shareholders, which represents 75% of our operating cash flow. This includes a 5% increase in our dividend per share and $5.5 billion in share repurchases. We do expect that this will reduce our weighted average share count by approximately 3%, which is a meaningful driver of our EPS growth. .

And we are evolving on mix towards share repurchases because they support EPS and they give us more flexibility in the context of the current valuation environment. We'll also continue to operate within clear balance sheet guardrails. So we intend to maintain strong investment-grade credit ratings and gross leverage below 2x.

In addition to the roughly $3 billion for our cyber OT acquisitions, we do expect to deploy an additional $5 billion in acquisitions in FY '27. And at that level, we expect gross leverage to be about one turn well within our guardrails. And as always, if we see the opportunity to do more, we can.

So our strong cash flow supports both parts of our capital allocation strategy, returning cash to shareholders and investing in future growth. So I want to spend a moment on the second piece because acquisitions are an important part of accelerating our strategy. .

As Julie mentioned earlier, we do acquisitions for 3 reasons. Many of you know this, the scale in high-growth areas to deepen our industry and functional expertise and to expand into new growth areas in new growth areas. So our acquisition strategy is ultimately to fuel organic growth.

We use our strong balance sheet and our acquisition experience as competitive advantages to accelerate our strategy. And because of our strong cash flow and our strong balance sheet, which reflects it at and we can flex down based upon what we see.

And we apply consistent financial discipline against the targets that we evaluate. We recognize that different types of acquisitions can have different return profiles. So for acquisitions that extend our core capabilities, our core services capabilities, we require returns meaningfully above our cost of capital and a path to accretion over a relatively near-term horizon.

For investments that expand our addressable market into higher growth areas, which could be product platforms for solutions. The growth opportunity is higher. The returns investment horizon can be longer. Will we pay a higher valuation, we expect a higher return over time.

And our track record supports that discipline. Our current acquisition portfolio over the last 3 years is generating returns of more than 2x our cost of capital. And the discipline continues after the deal is closed.

For the first 3 years, our capital committee reviews acquisitions semiannually against the original business case, including both financial performance and talent retention. And we applied this same discipline across our business.

Exiting businesses offerings, countries, contracts that no longer meet our strategic or return thresholds. So how are we tracking our progress and how should you measure whether the strategy that you've heard today is working.

Of course, we will measure ourselves against the financial ambitions that we've outlined today. In addition, I want to share some of the other key metrics that we're tracking as key indicators of our progress against our long-term growth ambition.

We look at the number of clients with quarterly bookings over $100 million and the creation of new diamond clients. These metrics reflect the debt and durability of our trusted relationships and our ability to grow with the largest organizations in the world.

We also look at the performance of our key 18 ecosystem partners. This is a direct measure of the technology and AI leadership and our ability to capture growth with our ecosystems and market share gains, a clear indicator of our competitiveness and whether or not we're winning in the marketplace.

And finally, our great place for we're ranking. Our people remain fundamental to our success in attracting and retaining top talent is a critical competitive advantage. Our goal is to remain in the top 5 globally, and today, you heard we're #4.

There is one additional metric that I want to highlight because it reflects how our business model is evolving. Revenue per person which we expect to increase over time. As AI creates productivity and our business mix continues to evolve -- this is the metric where you will be able to see how we are creating even more value and increasingly decoupling revenue growth from headcount growth is one of the clearest indicators of how AI is changing our business and strengthening the scalability of our business.

So to wrap up, our financial model is designed to compound. The productivity we are driving is creating that investment capacity. As we invest, it's going to create new growth opportunities. And our strong cash flow and balance sheet allow us to invest and return cash to shareholders. We have demonstrated this model, and we will continue scaling it. This is what gives me confidence in what we've laid out today, and our commitment to yield is that we will continue to create shareholder value. Thank you.

Okay. I think now, I'd like to invite I think Julie Manish, John, Mauro and Alexia are going to join me on stage. So just give us a second 1 we get organized.

Good afternoon. Well, we're excited for 30 minutes of questions and answers here. Andy just told you who's on stage, but it's Julie Marrone John Angie and Alexia Quadrani, the Head of Investor Relations. So if you -- I'm going to call on you guys just raise your hand, please wait for the mic to come over to you. And then if you don't mind, just stating your name and your firm, that would just be helpful to ask.

Yes. And just our other leaders are also in the room. So if you do have a question for someone else who was on a panel on that, please just let us know, and they've got a mic, and they'll be able to answer as well. All right.

Green. Good day. My question is -- Tien-Tsin Huang from JPMorgan. I'm just thinking -- what's giving you all the confidence to go ahead and issue this to your outlook that is a little uncharacteristic of Accenture, but it does speak to the confidence in the business. .

What are the building blocks do you think to ultimately get to that acceleration in '29? And I've gotten some questions around just absorbing the AI efficiencies. That's a pretty common question I know in the sector, but can you maybe just give us the mental model of how that might play into the outlook and how you see it .

Sure. Why don't I take that, Alexia. So I also got the same question. we wouldn't have given this guidance probably a year ago. And the reason for that is that we've been building this foundation as part of building the foundation.

We've been doing more and more work with our clients. And so our confidence comes really from 3 things. First, we are the leader in implementing AI. And over the last years, we've started to see our clients be able to take them from pilot to production.

And we have a much better view of both what that takes and what that pace is going to be. And that's why we have such a clear view of how we're going to grow. And that visibility has come from both doing the projects, but also that we've been doing them long enough that we have enough examples to feel confident about how we're seeing that develop.

The second is that we've also been testing our view of how we absorb efficiencies. And so we've been absorbing them in our business from day 1. And so we have more confidence in what that's looking for, looking like we see 2 places that we're absorbing efficiencies in how we deliver as well as a software -- the software we implement and has more and more AI, it does impact the amount of our efficiencies.

At the same time, I just take you back to the beginning, our strategy is to put more AI into all of our delivery. And so we have to work through some of our existing contracts, but the way that we will win is by helping our clients have more efficiencies that they can reinvest for the enormous agenda they have ahead of them.

Remember, we said that only 5% of organizations have the enterprise AI stack that would even allow them to have AI be repeatable. There's a lot of investment that has to happen. And so we build relationships by using the latest technologies, delivering efficiencies and being the partner that has the breadth and the talent to be able to help them achieve their ambitions.

And in addition to that, we have been very quietly going after the new markets that we've seen. And the network that we've built, which is now 6% of Accenture, also gives us a lot of confidence as we see our long-term growth. And so we have a clear strategy around how to grow the core -- we have a lot of visibility into what our clients need. And we've now built a network that we can scale that also gives us confidence. And so those are the building blocks that really allow us to feel comfortable laying out an ambition for the next 3 years game.

It's Jamie Friedman at Susquehanna. Great day today. So thanks for the additional disclosures, the detail, the thought process I -- this may be somewhat mundane, but I had an investor relations related question.

When I look at the KPIs that you've had over the years, okay, you had the strategic priorities. And you had that for many years, and then you reduce that disclosure prior to that, under peer, yes, the old and the new is pretty straightforward. You're introducing the revenue per head count. I appreciate that. But how do we measure how you're doing on the 6 and the 94% going forward?

Angie?

Yes, I think that we laid out today how we see the next few years. And so we'll the measures that I gave you relative to the metrics that we're going to be looking at, it will include, of course, how we are on our way to doubling that over the next 3 years. So that's our ambition. .

We'd expect that to be an annual disclosure because as you can imagine, these things are -- they going to fluctuate up and down over a quarter. And so you should anchor it on our annual guidance. .

Stefan Slowinski from BNP Pariba. Thank you so much for joining us today. Really helpful and clearly see the opportunity that's ahead of you working with your customers to implement AI. Just wondering around the free cash flow implications of that.

When we think about a move towards outcome-based pricing, and the investment in the networks business, is there any change in your free cash flow conversion? I see the 2029 outlook has, I think, $1.1 billion for the net income to free cash flow that's below the $1.2 billion to $1.3 billion you've guided for this year. So just wondering if that's just a cautious kind of look a couple of years out or if there's anything changing in the business model that will impact the free cash flow conversion?

Sean for us, we think that a 1.1 free cash flow to net income is quite excellent. Really, like we know what 27 looks like, the level of investments, et cetera, that we make will fluctuate depending on what we see and where we believe we need to invest. .

But at a minimum, we look to deliver at least 1.1. And so nothing to read into that a bit is something changing here. It's more about believe 1.1 is super strong, and that's what we're -- that's our ambition, and we'll see how things flow from there.

It's Bryan Keane at Citi. Thanks for doing this. Great day. I just want to ask about the AI efficiencies. I guess the fear in investors' minds is that as AI advancement continues, those efficiencies will get greater -- so how do you get comfortable that you're not going to have to give further efficiencies?

And then kind of attached to that is thinking about competitive nature of the business. I guess another worry is that competitors will come with AI led to price more aggressively than maybe a sensor has to try to win some business. So they're kind of related. But how do you think about those 2 things? .

Sure. Why don't we first hear from net, you can give you a little color about what actually happens on the ground and then have 1 of our market CEOs talk a little bit as well? .

So I think this -- the stuff around efficiencies is been a topic for discussion for probably more than 3 decades, right? Because when we did offshoring, we did RPA, some of the other waves that we had. We always had this question coming up. And I would say that 2 things on this.

One is that every time something gets with lower cost with faster the demand always increases. It comes back to a very old and a boring paradox which is the GoPros, which was there, but it has been proven, and we kind of typically disregard that piece.

But I always feel like I said, I've lived through those 3 ways, and I've always seen the demand kind of goes up this point one. Second point is typically, what happens is if the clients have 100 units of work. In 2001, they were giving us 5 minutes of work out of what they had.

In 2010, they were giving us 20 parts in 2021, they were giving us 25, and now we are seeing they're giving us increased amount of work because it has become cheaper and with lower costs.

So that's kind of the second point. The third point, I think, which we are seeing is just the emergence of the new services, which is kind of super exciting, right, you did not have AI managed services. We did not have data managed services.

We did not have -- you heard today the OT managed services you did not have any of this. So you are seeing new kind of services emerging in the AI era, which we were not even looking at earlier. We talked about the growth arenas earlier, but look at agent e-commerce.

Nobody talked about agent iCommerce on this slide. So that's kind of just the third point. The last point I'll just say is -- as AI has come, I know we all love AI and the deployment of AI, any organization before they deploy AI the debt that they have, we talk about tech debt, which is the digital core and the enterprise AI stack. Process deck is much larger than even the tech that it is very uncool and nobody wants to focus on that.

Maybe what the data get because you got to get the data organized and that is why the data managed services in the architecture. And then you have got the operating model and access to talent. These 5 things we continue to kind of ensure that there is so much of work to be done post which you can deploy AI actually. So that's kind of just a bit of in those 4 points.

So that was a very complete answer, so I won't have John and Mauro add to it. I will add one thing, however, which is you did ask about the AI-led and that is actually what we're doing today. You heard every one of the clients talk about how we're using AI and embedding it Manish talked about reinvention.i, which is our proprietary platform that we're using to help lead because a lot of the reason why in managed services, for example, clients are turning to us is they don't want to build the platform that has the agents and all of that.

And so we are already selling with an AI-first lens and you have to do that now. Great. I think that's Jason back there.

That is Jason Kupferberg from Wells Fargo. I just had a 2-part question about the '28 and '29 outlook. I mean if we think about this current fiscal year, we're expecting about 2 to 2.5 points from M&A. Is that the right proxy to think about for the next couple of years and then just if you can hone in on fiscal '29 a little bit more, the acceleration, you're obviously pointing to assuming that that's organic, what's driving that? What's the most likely scenario for that acceleration?

We've historically been in sort of the 2 cash from inorganic, and that's like a good sort of model to kind of go forward. Obviously, it flexes up and down depending on opportunities. But that's a good model. kind of going forward.

And then when you think about the building blocks of the step-up, maybe I'll have Mauro just give it a little more color as to how you see that happening in his market.

Well, first of all, it's linked a bit to the point that Manish was covering. We're really earn in the cycle. I mean, we spend every week at clients. And we didn't understand where are those clients in the cycle.

And if I talk about Europe, and you saw the numbers globally in Europe, we are even much earlier in the cloud journey, for example, many clients are not talking about global clients, but national champions are still discussing how to approach the jury to cloud.

And cloud is a big enabler of AI. So what I'm saying is that, as Manish was saying, there is a lot of work to prepare and transition into real capturing value from AI. And the point of which I was going to make on the data debt, the process that is very visible.

So clients really need to navigate in this moment, and then to navigate understand how to make choices. And one point that I see very valuable is they learn more and more how to deal with ecosystem partners.

They need to play and make the right alliances like partnerships with ecosystem partner. And I think they find always in Accenture the preferred partner, if I can say, in Europe, to help them find the right balance across the different ecosystem partner to try to have certainty of the future steps because many CEOs tell me, I want to have certainty of future cost.

And today, we need to make choices. We need to decide who to partner with and how fast you can afford to this transition because then maybe we didn't discuss a lot about change management, change an organization takes time. and it's not a magic button of putting an eye and then everything works prepay. So our jobs need to navigate together with them in their transformation journey.

And then the other building block is the network, right? It's 6% today, but has the potential to be 12% in FY '29 and it's growing double digit. So you can start to see how that becomes a much more material contributor by FY '29 and helping us achieve our ambition. .

Kevin from UBS. Helpful date for sure. As you think about the business mix consulting relative to managed services as clients start to scale their AI adoption. Does that make stay constant? And with the efficiencies clients have on the managed services side, do they redeploy that more into the consulting or more on the managed services?

I'll sort of keep this short. I mean, for about a decade, we've been approximately 50-50, and much of our managed services also now includes consulting type of work and then the consulting type of work leads to managed services. So we don't see right now some big change happening and you can see that even in our guidance for '27, where both are growing about the same. So we don't see some big catalysts right now for a big mix shift.

James Faucette from Morgan Stanley. I wanted to ask about ventures and acquisitions. I feel like -- this is probably an underappreciated component of the strategy and how you're able to stay on technology cycles. I'm just wondering -- how do we -- how do you think about sustaining the growth rates of these businesses as you acquire them?

How should we think about how that evolves over time? And then how that contributes to the expected acceleration, especially as we're seeing an uptick in acquisitions right now? How important of a driver and the expected acceleration is that?

I'll have Manish comment, but one of the things just to reground everyone in is that the acquisitions are both in our core business and in the network. And in all cases, the belief and the way that we look at the business cases are that they have to grow more because they're part of our family, right?

So that's kind of like our mental model in terms of as you think about maintaining and actually accelerating growth. maybe you should give a little bit of color.

Yes, we have done, as you have seen plenty of acquisitions, right? I think they are all in terms of where we believe there is a massive opportunity for growth. But if you look each 1 by one. The reason why we turbocharge all our acquisitions is the availability of our client account lead network, the deep trusted relationships of being like a complete trusted adviser.

The; biggest orchestration engine on this planet, which John and Mauro spoke about, is that excess is why everybody and all the ecosystem partners and all the acquisitions, that is the biggest strength of Accenture. [indiscernible] And can you just imagine at 4,000 clients, I got people were sitting there walking the corridors talking to the client every single day and understanding what the key challenges are in deploying the available that is what they are already. That is why we feel very confident what Julie just said about rotating the business as we move because 6%, they will come -- they will get access to the world's biggest distribution channel, and then we just have exposure of growth around that. SP27915630 Correct. Excellent. .

Rod Bourgeois here with DeepDive. Anish, I've got a question for you about managed services, given your background, particularly in that area, -- when you IPO-ed, your managed services business, I think, with 17% of revenues.

It's now 50%, and it's on a very large base. And all of the concerns about AI floating around impacting stocks, but it's growing 7%. So can you give us some color on where the growth is coming from and how much of that is in newer areas of managed services that we may not have grown up understanding from a historical perspective.

Thanks, Rod. I think this is the question which keeps on coming in services, what is going to happen here on fire on that managed services and as you rightly anointing that out that we have grown this in a massive way.

We are doing this in a consistent way over the years. I think there are 2 parts to this, which I said earlier. One is every time something gets at lower cost, it's just the demand increases -- and this is where I always give the example, right?

Automation will destroy all jobs in human kind. -- we think it is a new term that has come now. It was frustrated in 1851 during the industrial revolution, right? That is the beauty of this and people kind of think about this, so that is point one. So explosion because it is becoming much more cheaper.

AI is able to get. I think give us -- it has given us more higher value-added work to be done in the managed services. In fact, part of the reason you're seeing per the revenue per headcount increasing is because we are doing the higher end work with the clients are not tasting us with.

That is point one. And the second one, which is big is all the new managed service. And they are like just at a great scale that they're increasing now, but we become -- that will become even bigger part of our service, which is the -- whether AI managed or the data managed services.

And I keep on coming and saying AI is beautiful, but the data cleanup in the process cleanup and the digital core not I think quickly, and I would add that this I've seen visible with the business cases of our clients have significantly improved compared to 3, 4 years ago.

So the business case for the client on the value that we can capture is much bigger. So now they're much more ready because, okay, maybe the business case was so soft 4 or 5 years ago. Now the business case is probably sometimes even double.

So there is more openness to do managed services from mid life.

This is [indiscernible] from HSBC. I want to extend on the previous question that was asked. So the managed services was supposed to be the area where you see a lot more pressure. -- but you said it was growing at 7%. So -- but your total overall group revenue was at 5%. So which is the area that you're seeing some more pressure on because the other subsegments that you had mentioned or all growth years.

The second question that I had was on your delivery model, considering that more and more delivery is going to fix the outcome base. How do you see this evolving into your outlook?

Great. But it was actually 7% in Q4 and 6% for the year. so a little bit less of a gap. What I would say, and maybe, John, you can comment a little bit on this in terms of like kind of where we see the squeezing of the legacy while kind of a rotation to the new, of course, Americas is half our market, and you can comment a bit on the delivery model.

So I think this is a little bit of a question that we've been talking about with managed services overall. And so we do see managed services continue to grow. But at the same time, there's different parts of managed services that are in there.

And there are some legacy managed services that we have in there that are under more pricing pressure right by being able to squeeze those, right, it's creating an opportunity for us to sell into some of those other areas that had not been outsourced previously.

And so it's the balance between these things where we're driving the incremental growth. As a company over time, like we've succeeded by leaning in to innovations in the industry, like just the offshore that Julie talked about at the very beginning or innovations and technology, which has created value, speed, value for any of our clients and that was true with offshoring that was true with cloud. That was true with SaaS, and it's true with AI.

And so with each one of these things, like we're kind of leaning into it, even in cases where it's reducing the total revenue that we would get in that contract, the savings that we create gets reinvested somewhere else.

We're in a great position to help them figure out where they can apply that savings in order to create more growth or more differentiation elsewhere. So it's kind of this revolving circle as we go around the legacy parts of the business that are getting squeezed, create savings that gets reinvested into the parts of the business they're trying to grow.

When we help them save, then we also are helping them grow or differentiate in another area. So like how it works with the client and how it works for us. These are relationships we've had, as we talked about, in cases for decades, like we sort of know both sides of this equation and are working with them on this transition like partnering.

And that's why our delivery model is all about we want to use as much AI as we can in all types of work because that's called putting the client's needs first and then positioning to be able to do more.

And our strategy with so important building the expertise to be relevant to all these other buyers or, in some cases, decades. Right? And those budgets are the ones that are now starting to expand in addition to the tech budget because of the opportunity. And that's why we're so well positioned, and the breadth of our business really matters. If we were a much more narrow company in absorbing AI efficiencies, that would be a challenge, but we are -- really have a broad base of business that's super exciting.

Let's go to Darrin

It's Darrin Peller from Wolfe Research. Let me just start quickly with Andy and then a very quick one for Julie as well, but it's really first on margins. If you could just help us in the context of the environment we're in around some more interesting dynamics around pricing.

How do you think through the building blocks for the conviction on the 10 to 30 bps, especially all the way out a few years.

And then maybe just quickly, Julie, when we think about the areas of your business that the new data infrastructure and the core of being advanced opens up for you that maybe you couldn't sell as much of 3 years ago. what are a few of the areas I'm thinking about song or areas like that, that maybe can accelerate now given the infrastructure of your customers today?

Okay. So let me start with -- I'll start with the margin expansion and how we're thinking about it because I do know there's been a lot of conversations around pricing throughout the day. There's the 4 levers that I went through earlier today, right?

Pricing is absolutely one of those factors, which is the margin on the work that we sell. We work hard -- our goal is for it to be maintained or improving, right? And so that's the first. The second piece is how do we deliver our services [indiscernible] like everyone up here has talked about how we're leaned in, in terms of how we deliver our services to our clients to be more productive in how we're delivering that.

Second big lever. Third is how we run Accenture, pretty significant, and you guys have seen that in our G&A, for example, right, in terms of the efficiencies that we're building in all 3 of those things, along with our talent and supply management, all of those are in play that allow us -- while absorbing.

By the way, while absorbing significant investments, and you saw our investments in our materials that we set and still growing off 10 to 30 basis points. And so it's a formulaic piece that we think through that those levers are in place for us to deliver the 10 to 30.

Right. And we're 4 years in now. Right? So obviously, things continues, but our confidence comes from. We've been operating in this environment of more competitive intensity. We've been operating in the environment of giving more productivity, right?

We've been taking out costs in our corporate functions and reinvesting it. So again, I would have said we wouldn't give this to your guidance 1 or 2 years ago, but we are now 4 years into this big transformation. And so we have more confidence in how this develops. And then the other question was?

[indiscernible]

Yes. I think that probably the biggest kind of new area is the enterprise AI stack. You heard Manish talk about where in FY '23 that we had 4 deals that were $50 million or more that were mostly AI and data as opposed to other deals. And last year, we had 50.

Now we want a lot more than that, but that's where we're starting to see companies that have built enough of the digital core and they're willing to spend the money because you don't get immediate ROI when you're building out the enterprise AI stack, and that's part of the challenge, right, in terms of adoption.

If you really do need that full stack in data is absolutely critical. But I would say that in building out the digital core, one of the things that we talked about earlier is that there are so many areas of the digital core that are actually new areas.

So the mainframe is one of them. I mean, IBM Z product is only a couple of years old now. And so now it has the ability to like embed the AI to make different choices about what goes to the cloud, what makes sense to stay there.

That's a big growth area -- it isn't about like something brand new, but the tech is there, and they absolutely want to use AI, and it's very important to be able to use more AI that you modernize it.

And so it's very important that the depth of our understanding of each of these aspects means that we're capturing new growth in each of the components. It's not all about , but again, you have to -- it takes time. You have to have investments. Our clients, the spending environment is not changing. And so some of that just got to be worked through.

All right. I think we have time just for one more. I know you've had your hand up for a while. Let me take a few and we'll take the woman next to her.

David Paige from RBC. I was curious if you could talk about some of the emerging growth opportunities maybe in the mid-market or in emerging markets. How should we think about that over the next 2 to 3 years?

Sure. Do you want to start, John? The mid-market is one of your favorite topics along with Mauro.

I'll start. So mid-market, like we -- just to just be clear, there's a mid-market, which is an area that historically Accenture, while we do work in the mid-market isn't something that we targeted in the way that we're intentionally targeting now.

So these are companies like between, say, $300 million and $3 billion in size. And so we're going to market with our ecosystem partners with packaged solutions and a team that's been custom-built in order to serve that sector of the market.

For us, that's like I look at it in Americas, like white space upside for us. And like I think we will be able to grow there because we have a strong brand. We have super strong ecosystem relationships. And like we obviously have a scale delivery capability on and offshore that we can do this.

So that's upside. Then there's the emerging large segment, which, again, like we've always worked in the merging large. This is kind of like the $3 billion to $3.5 billion to $7 billion sized company. we are intentionally targeting like the highest performing companies like in that sector. And we have a lot to bring to the table for them as well.

We have very senior executives that have been working with the biggest companies in every one of these industry stacks like we're making some of those folks available to directly serve companies in that sector so that we can bring that 20 years of expertise, knowledge, network, understanding the industry, in addition to kind of the rest of Accenture's capabilities that we do for the biggest companies in the world today.

And so like if I look at both of those, and Manish and I have been working on this for the better part of the last 3 years, like we've seen these segments really grow. We're being more public about it now with all of you, but we have a lot of like upside potential that we're looking at as we continue to lean in here.

Let's take our last question, now.

This is Maggie Nolan with William Blair. I'm curious on the guidance, what would happen or what would you expect to be the driver behind an increase in fiscal '28 from mid-single digit to fiscal '29 in high single digits and your confidence in being able to reach the high end of that in '29.

Great. So the big drivers are that both the core and the network are going to grow. So we expect the network, our vision is to double it by 29% and to have the core continue to grow in the mid-single digits.

And we feel that if we can achieve that with what we expect to start to happen in '29 in terms of opportunities and where we AI to scale. That's when we feel like we can have this step up -- now where we're going to be in the range in that step up, we'll update you here to then.

But we feel like we're being very realistic about the opportunities, the realities that we need to work through over the next couple of years and where we think that the opportunity to scale both the network and grow the core is.

Well, I'm sorry, I would love to spend more time, but I know it's been a long day. So I just want to I thank all of you on behalf of the entire management team and our Board, you've invested a lot of time with us, and we appreciate it.

Hopefully, you are walking away with a deeper understanding of how we've become a business reinvention and technology company and why we have so much conviction in our growth and our shareholder value creation ahead. So thank you for your time and your trust, and we'll see you at earnings. Thanks.

Good day, and welcome to the Accenture Fourth Quarter 2026 Financial Results Conference Call.

[Operator Instructions]

Please note today's event is being recorded. I'd now like to turn the conference over to Alexia Quadrani, Executive Director and Head of Investor Relations. Please go ahead.

Thank you, operator, and thanks, everyone, for joining us today on our fourth quarter and full year fiscal 2026 earnings call. As the operator just mentioned, I'm Alexia Quadrani, Executive Director, Head of Investor Relations. On today's call, you will hear from Julie Sweet, our Chair and Chief Executive Officer; and Angie Park, our Chief Financial Officer. We hope you've had an opportunity to review both the earnings release and the accompanying presentation issued prior to this call.

Let me outline the agenda for today. Julie will begin with an overview of our Q4 results and step back to look at fiscal 2026 overall before following with a brief update on our market positioning. Angie will then take you through the detailed financial numbers, including the income statement and balance sheet, along with key operational metrics for the fourth quarter and full fiscal year before providing a business outlook for the first quarter and full year fiscal 2027. We will then open the line for your questions before Julie closes with a wrap-up.

Some of the matters we'll discuss on this call, including our business outlook, are forward-looking and, as such, are subject to known and unknown risks and uncertainties, including, but not limited to, those factors set forth in today's earnings release and discussed in our annual report on Form 10-K and quarterly reports on Form 10-Q and other SEC filings. These risks and uncertainties could cause actual results to differ materially from those expressed in this call. During our call today, we will reference certain non-GAAP financial measures, which we believe provide useful information for investors. We include reconciliations of non-GAAP financial measures where appropriate to GAAP in our earnings release or in the Investor Relations section of our website at accenture.com.

As always, Accenture assumes no obligation to update the information presented on this conference call. Now let me turn the call over to Julie.

Thank you, Alexia, and everyone joining us this morning. and thank you to our more than 814,000 reinventors around the world for your extraordinary work and commitment to our clients. Before Angie takes you through the detailed numbers in our FY '27 outlook, I will start with Q4 and then step back to look at fiscal 2026 overall.

Starting with Q4. Revenue was $18.7 billion, growing 7% in local currency, above the top end of our guided range. Growth was broad-based across markets, industries and both types of work and we once again took significant market share. Revenue from work with our top 10 ecosystem partners continue to outpace our overall growth. We had $22.2 billion of bookings in Q4, reflecting the relevance of Accenture to our clients and demonstrating again our strong competitive position. I was particularly pleased with our 37 clients with bookings greater than $100 million. Our focus on being relevant where our clients are spending helped us capture the strong level of bookings even though the overall demand environment, including discretionary spending did not meaningfully change. We also delivered strong margin expansion, EPS growth and free cash flow while continuing to invest in our business and our people.

In Q4, we deployed $1.9 billion in acquisitions. Approximately $3 billion of capital related to the cyber OT acquisitions, including Dragos shifted into September due to regulatory timing, and we are pleased those transactions have now closed. In Q4, we continued to use our strong balance sheet and acquisition experience as competitive advantages to accelerate our strategy and ultimately fuel organic growth. As a reminder, we use acquisitions to scale in high-growth areas, deepen our industry and functional capabilities and expand into new growth areas, which increasingly include businesses with non-FTE commercial models. In Q4, we closed Ookla, a global leader in network intelligence, competitive benchmarking and customer experience analytics, which is an expansion into a new growth area with a non-FTE commercial model. Ookla also deepens our industry skills in the core value chain of the comms and tech industries. We also closed Whalar, a leading creator and social agency recognized for its creative excellence and ability to deliver measurable business outcomes.

Whalar is part of our focus on expanding our functional skills within our fan business in a high-growth area. To help accelerate our strategy to expand in the mid-market, we closed [ Comware, ] an end-to-end technology services provider with deep SAP, CRM and manufacturing expertise serving Japan's mid-market. We also announced our agreement to acquire McCoy, a trusted Dutch SAP transformation partner for mid-market companies. Finally, we announced an agreement to acquire Industries Excellence Group, which deepens our engineering skills as part of our supply chain and engineering business. Turning to demand in Q4. Large-scale reinventions, including many driven by AI, drove strong demand. The spend transforming functions and building out digital cores. Our clients are also focused on AI and believe AI will help them achieve more than previously possible across the enterprise.

The clients remain at very different stages of readiness. Much of our growth today comes from continuing to build their digital core data foundations and the enterprise AI stack that they need to use AI at scale. And many are just starting their AI journey. Nearly 100 additional clients initiated their first advanced AI work with us this quarter, bringing the fiscal 2026 total to more than 400. Because we are investing in our platforms and new solutions, we are able to embed advanced AI earlier into large-scale reinventions. Our proprietary assets and platforms, ecosystem relationships and our ability to deliver the work through consulting, managed services or a combination of both allows us to meet clients where they are and help them move at pace. Our managed services led work also includes a significant consulting and AI expertise as many clients use this work to both reinvent and to get greater certainty around outcomes, including cost savings.

Against this backdrop, two things stand out. First, our largest client relationships continue to expand as clients take on broader more strategic transformations. FedEx is a great example of how our largest client relationships grow over time. We began by moving part of its applications to the cloud. As more deliveries shifted to homes FedEx launched One FedEx to create a more connected, flexible and efficient operation. Delivering on that vision requires coordinating the technology, data and processes that keep the business running, all while FedEx moves millions of packages every day. Today, we're helping build the digital core behind One FedEx while supporting its investment in its people through enterprise-wide AI fluency programs. Together, this work is helping FedEx make supply chain smarter and embed AI more deeply into the way work gets done. Our partnership has continued to deepen as FedEx has expanded our role with additional managed services and modernization work designed to streamline operations, enhance customer service and support future growth. That is how our relationships grow. Each success creates the opportunity for the next.

The second standout from the quarter is how our AI demand is broadening because of both the depth and breadth of our expertise in the various functions of an enterprise across industry, process, operating model, data, technology and AI. We continue to see AI being embedded in the broader transformations our clients are undertaking. BP, one of the world's largest energy and convenience businesses operates across more than 150 markets with over 10 million customer touch points every day. To turn that scale into a growth advantage, Accenture Song is helping BP build a global marketing engine that combines data, AI and marketing expertise, deepening customer intelligence to move from insight to action, faster. BP's marketing teams operated with hundreds of regional processes, making it harder to scale campaigns consistently across markets. We replaced that complexity with a single scalable model and the games compound. BP is now producing 2.5x more content with 23% less effort. That benefit is also realized in customer reach. For example, customer engagement activity has grown threefold in the last 12 months alone.

By intelligently optimizing across offer, channel and customer moment, BP has been able to put its marketing budget in the right place at the right time, helping support measurable business results. Over the past year, active loyalty customers grew 12% and loyalty transactions rose 7%. This is what it looks like when marketing becomes a true commercial driver. We also continue to see green shoots of enterprise-wide AI transformations where our clients have been investing in strong digital cores. PPC is one of the leading early examples. Over the past several years, we've helped PPC Group, Greece's largest electricity utility, strengthen its digital core and build the foundations to drive continuous reinvention. Today, we're helping PPC put AI at the center of its next phase of growth as it transforms from a traditional utility into a power tech company, using AI to reinvent and expand into new businesses and markets. AI will be infused across the enterprise from power generation and customer experience to renewables and corporate functions, bringing together larger volumes of data to get work done faster.

For example, in energy management, AI will help to cut analysis time in half, helping traders make faster pricing and hedging decisions. At the same time, AI will help deliver more seamless digital experiences to millions of customers, increasing customer lifetime value and driving top line growth. PPC Group is not inserting AI into its existing processes, it is reinventing them. As the group expands beyond its traditional utility business, it is rebuilding how work gets done around clear outcomes with people in the lead and agents on task, measuring the value of every change and reskilling its workforce in parallel. We're also seeing clients continue to move deeper into functions, particularly in areas such as customer experience, supply chain and finance as they redesign end-to-end processes and ways of working around AI. In customer experience, we're using conversational AI, including voice and chat agents to handle live interactions, resolve increase faster and provide a more personalized experience, helping organizations drive customer satisfaction and retention.

In supply chain, we're using advanced AI to improve demand forecasting across complex distribution networks, enabling more accurate forecasts and faster, more automated decisions, which can optimize costs and improve margins. And in finance, we're building AI reasoning agents that allow CFOs and finance teams to ask questions in plain English and analyze detailed profit and loss data, helping finance leaders get ahead of trends, make faster, better informed decisions and free their teams to focus on the work that drives business results. And increasingly, we are seeing AI move into the core value chain of industries. For a leading pharmaceutical company, we're using faculty's Frontier platform to improve a critical part of developing a new medicine, clinical trial planning. The platform simulates different trial scenarios, helping teams decide where to run studies, which sites to use and how to respond when patient enrollment falls behind. In an initial application, the platform reduced the time needed to create and analyze trial scenarios from 10 days to 10 minutes, improved patient enrollment forecast accuracy by 60% identified study designs that could shorten trial time lines by up to 3 months.

In an industry where each day of delay can mean up to $1 million in lost revenue faster planning can help therapies reach patients sooner and improve the return on R&D investment. It is a strong example of how we combine our technology with deep life sciences industry expertise to turn faster, better decisions into meaningful business value. You can see in these examples, the breadth of demand, which we expect to continue and the breadth and depth of our expertise. Today, we've covered logistics, energy and pharma, and they span from enterprise functions like finance to growth functions like marketing to the core value chain like clinical dials to the digital core. Turning to our full fiscal year. We had another strong year, delivering profitable growth, taking significant market share, returning record cash to shareholders and investing at scale in our business to strengthen our near- and long-term growth. We did so in a very dynamic macro environment, which we expect to continue. For the full year, we delivered revenue of $74 billion, growing approximately 5% in local currency and adding approximately $4.5 billion of revenue over FY '25.

We delivered these results while absorbing an approximately 1 percentage point impact from our federal business, which sunset at the end of Q3. We had bookings of more than $84 billion, up 5% in U.S. dollars and 3% in local currency. This included 141 quarterly client bookings greater than $100 million, 12 more than last year. These bookings are a proxy for the effectiveness of our reinvention strategy and position us well for future transformations as AI scales. We also ended the year with 317 Diamond clients, our largest relationships. We also delivered strong adjusted earnings per share growth and free cash flow, and we returned a record amount of cash to shareholders, 38% more than in fiscal 2025. At the same time, we invested at scale in our business with $4.9 billion in strategic acquisitions, $1 billion in R&D, and $1 billion in learning and development as we continue to upskill and reskill our people for the AI era. In fiscal 2026, our people completed 46 million hours of training, and we now have nearly 110,000 AI and data professionals exceeding our 3-year goal to double our AI and data workforce from 40,000 to 80,000 by the end of fiscal 2026.

As we look forward, we are confident in our future. We continue to believe the opportunities related to AI are greater than the impact of AI-related efficiencies in our business, and we expect that to continue as AI enables enterprises to do much more. At the same time, AI is making both our own delivery and the technologies we implement more efficient. Consistent with prior technology waves, we see this in 2 areas: the additional productivity we achieved in delivering our services which creates greater value for our clients, and faster, more efficient implementations as our ecosystem partners invent more AI capabilities into their platforms. Our strategy is to lean into these efficiencies precisely because they create value for our clients, while continuing to invest and rotate our capabilities to capture the larger growth opportunities AI creates. That is why we continue to view AI as a tailwind for Accenture. As we look at the AI opportunity, our strong ecosystem position is one of the reasons we are so confident. Our top 10 ecosystem partners represented more than 60% of our revenue in fiscal 2026 and revenue from that work grew 6%, outpacing Accenture overall.

For our 8 emerging AI and data partners, bookings more than tripled and revenue more than doubled compared with fiscal 2025. The tech ecosystem is incredibly dynamic with nearly daily change related to AI. This means our clients have more choices, but they also have more decisions to make and they turn to us to help them make these decisions. to help them bring these technologies together and turn them into business outcomes. To give you a flavor of this dynamic environment, data and AI are now core to all of our partners, both our largest partners and our emerging data and AI partners. Our top ecosystem partners, the hyperscalers and the SaaS players, are all introducing new AI features and functionality at speed. The model landscape also continues to evolve with Frontier, specialized smaller and open weight models. We are the #1 partner of the top 3 Frontier companies, and now we offer full life cycle services for open weight models.

Over the last 12 months, the AI ecosystem has become much more interconnected. Clients can now access frontier models directly through hyperscalers and increasingly through the SaaS applications they already use. At the same time, SaaS providers are rapidly embedding AI and agentic capabilities into their applications and workflows. Additionally, private and sovereign AI are increasingly important to our clients, and we are at the heart of creating these solutions. This is why our strong network of ecosystem partnerships is so important to our growth. Reflecting how the landscape has evolved over the past year, we will now report our top 10 ecosystem partners and emerging AI and data partners together as one group. We have also added Dell, an important partner in private AI. Beginning in FY '27, we will provide this update annually. Over to you, Angie.

Thank you, Julie, and thanks to all of you for taking the time to join us on today's call. Our performance reflects the strength of our fundamentals. We remain focused on delivering results that create value for our clients, our people and our shareholders while continuing to invest for market leadership. Revenue, operating margin, EPS and free cash flow were all very strong. And for the full year, we achieved or exceeded all aspects of our original guidance while returning a record level of cash to shareholders.

Let me turn to some of the details of the quarter. New bookings were $22.2 billion for the quarter, a 4% increase in U.S. dollars and 5% in local currency with an overall book-to-bill of 1.2. Consulting bookings were $9.4 billion with a book-to-bill of 1.0. Managed Services bookings were a record $12.8 billion after our previous record in Q4 of last year with a book-to-bill of 1.4. Managed services bookings can be lumpy, which is why we focus on the trailing 4 quarter book-to-bill, which was 1.2. Fixed price work, which includes outcome base is now over 65% of bookings and continues to grow. Turning now to revenues. Revenues for the quarter were $18.7 billion, a 6% increase in U.S. dollars and 7% in local currency. Consulting revenues for the quarter were $9.3 billion, up 6% in U.S. dollars and 7% in local currency.

Managed Services revenues were $9.4 billion, up 7% in both U.S. dollars and in local currency, driven by high single-digit growth in technology managed services which includes application managed services and infrastructure managed services as well as high single-digit growth in operations. Turning to our geographic markets. In the Americas, revenues grew 7% in local currency. Growth was led by high tech, software and platforms, utilities and banking and capital markets. Revenue growth was driven by the United States. In EMEA, we delivered 7% growth in local currency, led by growth in public service, banking and capital markets and software and platforms. Revenue growth was driven by the United Kingdom and Italy. In Asia Pacific, revenue grew 7% in local currency, driven by growth in public service, insurance, and communications and media. Revenue growth was driven by Japan, Australia and Singapore. Moving down the income statement. Gross margin for the quarter was 32% compared to 31.9% for the fourth quarter last year.

Sales and marketing expense for the quarter was 9.7% and compared with 10.2% for the fourth quarter last year. General and administrative expense was 7% compared to 6.6% for the same quarter last year. Before I continue, I want to note that results in Q4 last year include costs associated with the business optimization actions, which impacted operating margin, tax rate and EPS. The following comparisons exclude these impacts and reflect adjusted results. Operating income was $2.9 billion in the fourth quarter, reflecting a 15.3% operating margin, a 20 basis points increase from adjusted operating margin in Q4 last year. Our effective tax rate for the quarter was 27.3% compared with an adjusted effective tax rate of 27.9% for the fourth quarter last year. Diluted earnings per share grew 9% to $3.29 compared with adjusted diluted EPS of $3.03 in the fourth quarter last year. Days services outstanding were 50 days compared to 48 days last quarter and 47 days in the fourth quarter of last year.

Free cash flow for the quarter was $2.8 billion, resulting from cash generated by operating activities of $3.1 billion net of property and equipment additions of $250 million. Our cash balance at August 31 was $12.8 billion, reflecting our $5 billion debt offering in Q4 compared with $11.5 billion at August 31 last year. With regards to our ongoing objective to return cash to shareholders in the fourth quarter, we repurchased 17.6 million shares for $2.3 billion at an average price of $131.89 per share. Also in August, we paid a quarterly cash dividend of $1.63 per share for a total of almost $1 billion. This represented a 10% increase over last year.

Now I'd like to take a moment to summarize the year. We delivered bookings of $84.5 billion with a record 141 quarterly client bookings over $100 million and a book-to-bill of 1.1. These results position us well for near-term growth and the large deals, in particular, for long-term growth with work that layers in over multiple years and positions us to help our clients with the next wave of free inventions. Full year revenue was $74.2 billion with growth of 5% in local currency, reflecting approximately 3% organic growth. We delivered these results while absorbing an approximately 1 percentage point impact from our federal business. Looking first at type of work. Consulting revenues were $36.9 billion, up 5% in U.S. dollars and 3% in local currency. Managed Services revenues were $37.3 billion, up 8% in U.S. dollars and 6% in local currency driven by mid-single-digit growth in technology managed services and high single-digit growth in operations.

Before I continue down the income statement, as a reminder, the following comparisons exclude the impacts of business optimization actions I noted earlier and reflect adjusted results. Adjusted operating margin of 15.8% was a 20 basis point expansion over our adjusted FY '25 results. Importantly, we delivered this expansion while significantly investing at scale across acquisitions, learning and development and R&D. Adjusted earnings per share were $13.97, reflecting 8% growth over adjusted FY '25 EPS. Free cash flow of $11.6 billion was up 7% year-over-year, reflecting a very strong free cash flow to net income ratio of 1.4. And with regards to our ongoing objective to return cash to shareholders, we returned a record $11.5 billion in fiscal '26, a 38% increase over the prior year, including the $2 billion of additional share repurchases we executed in the fourth quarter. At the same time, we deployed $4.9 billion of capital across 17 acquisitions.

Now let me turn to the business outlook. For the first quarter of fiscal '27, we expect revenues to be in the range of $18.9 million to $19.6 billion. This assumes the impact of FX will be about negative 1% compared to the first quarter of fiscal '26 and reflects an estimated 2% to 6% growth in local currency. Moving to full fiscal year '27. Based upon how the rates have been trending over the last few weeks, we assume the impact of FX on our results in U.S. dollars will be about flat compared to fiscal '26. For the full fiscal '27, we expect our revenue to be in the range of 3% to 6% growth in local currency over fiscal '26. This year, we expect an inorganic contribution of 2% to 2.5%. For operating margin, we expect fiscal year 2017 to be 15.9% to 16.1%, a 10 to 30 basis point expansion over adjusted fiscal '26 results. We do expect to see variability in the quarters on our way to 10 to 30 basis points of expansion for the year.

We expect our annual effective tax rate to be in the range of 24.5% to 26.5%. This compares to an adjusted effective tax rate of 24.9% in fiscal '26. We expect our full year diluted earnings per share for fiscal '27 to be in the range of $14.39 to $14.81 or 3% to 6% growth over adjusted fiscal '26 results. For the full fiscal '27, we expect operating cash flow to be in the range of $11.9 billion to $12.7 billion, property and equipment additions to be approximately $900 million, and free cash flow to be in the range of $11 billion to $11.8 billion. Our free cash flow guidance reflects a very strong free cash flow to net income ratio of 1.2 to 1.3. With respect to capital allocation, we will use our strong free cash flow to both invest in the business for the near and long term and return significant cash to shareholders. We will invest both organically and inorganically. Our organic investments include R&D, learning and talent development.

With respect to our inorganic investments, as Julie mentioned, we just closed in September the Cyber OT acquisitions, which was about $3 billion of capital that shifted from Q4 of FY '26 into Q1 of FY '27 due to regulatory timing. Looking ahead, we currently expect to deploy another approximately $5 billion in acquisitions in fiscal '27 based on the opportunities we see today to accelerate our growth strategy. We're planning for this level of investment because we currently see a number of opportunities that will position us for higher organic growth over the long term. And if you look at our history, when we've seen opportunities like this, we've acted on them, and that discipline has served us well. The final amount will depend on the right opportunities and timing.

On shareholder returns, we expect to return at least $9.5 billion, representing approximately 75% of operating cash flow through a combination of dividends, growing 5% per share, and $5.5 billion in share repurchases, which exceed issuances. Given this year's commitment and elevated level of repurchases we executed last year, we expect to reduce our weighted average share count by approximately 3% in FY '27. Our Board of Directors declared a quarterly cash dividend of $1.71 per share to be paid on November 13. We now have approximately $7 billion of share repurchase authority remaining. With that, let's open it up so that we can take your questions. Alexia?

Thanks, Angie. We ask that each participant keep to one question and a follow-up to allow as many participants as possible. Operator, you may now provide instructions to those on the line.

[Operator Instructions]

And our first question today comes from Tien-Tsin Huang with JPMorgan.

I want to give a little bit more detail on maybe what drove the upside in the quarter and any surprises across the 3 months as you saw the quarter play out? And how does the that result in form the outlook here in fiscal '27. I heard that the demand environment was steady. So just curious how you're thinking about the quarter and how it landed and how it drives the outlook?

Thanks, Tien-Tsin. Look, we had a great quarter, and it really demonstrates the growth strategy that we've been executing and the fundamentals of our business. And so when you think about 7% growth in every market and consulting type of work, managed services and positive growth in every industry -- the big growth drivers for the quarter were our large-scale reinventions, the ecosystem and data and AI because it's now embedded across like all of our work, and you saw that with how the ecosystem for the year was greater than our average and where with the emerging data and AI players, we doubled our revenue and tripled our bookings.

Now as you look at the quarter, the Middle East, the direct impact of the Middle East continued to be a headwind and in fact, worsened in Q4, but we did see the indirect impacts on discretionary spend for product and resources stabilize. And when you think about the over delivery above the 5% at the top of our guided range, it was a number of factors. So we saw an uptick in small deals. We were able to mobilize faster for some of the new contracts. Our acquisition, some of our acquisitions and our federal business overdelivered, and we allowed our people to carry over PTO, which meant we had people for the demand. So we're really very pleased that this quarter really demonstrates the strong fundamentals in our business.

Tien-Tsin, I would just add that no one factor was material in the outperformance.

Okay. Good. No, it's nice to see it's broad-based. Just my quick follow-up then. Just, I heard the $5 billion that you plan to employ on acquisitions in '27, but you also bought a lot of stock in the fourth quarter. I heard the deferral on by the month on some of the OT deals. But just curious, Julie, here your appetite for doing acquisitions in '27. Do you have good line of sight here on the spend and the areas of focus? Is this more opportunistic? Or again, do you have a good line of sight into it?

Yes. No, thanks. We do have a good line of sight. And just as a reminder, right, we generate very strong free cash flow, and we use that to invest in our business and return a significant amount of cash to shareholders. We just did this last year, right? We're going to do it again. And it's part of our growth strategy when we see significant opportunities in the market through acquisitions to either grow in really high-growth areas like data and AI and to expand into new areas like we've done with data centers. We've done with data and OT security, we go after them because that's how you position for long-term growth.

And we have a good line of sight to good acquisitions, which is why we plan $5 billion additional investment. And of course, at the end of the day, it will depend on the opportunities come in as we expect and the timing, but we are positioning for long-term growth, and we invest in the business.

And our next question today comes from Darrin Peller at Wolfe Research.

Nice results, and thanks for the clarification on the quarter itself. Maybe just go a little further into the guide now just because it does imply an organic deceleration. So maybe just help us understand what you saw this quarter that you want to build in a little more conservatism around in the year ahead. If you could also just touch on the variability in the guide, what factors would be to the low end versus the midpoint or the high end, that would be great.

Darrin, nice to talk to you. Let me just start with, I think that relative to whether it's Q1, I think it's important to have the framing of how we're seeing FY '27 play out. And look, as Julie mentioned, our fundamentals are really strong. When you look at it across our bookings, our revenue, our profit, our free cash flow. But let me tell you what we see for '27. So we just posted $85 billion in bookings, and that included 141 clients with quarterly bookings over $100 million. We see backlog. We see the big deals from those large deals layering in. Our pipeline is solid, and we see continued demand for those large deals. And then as it relates to the macro overall, we expect that dynamic to continue with respect to discretionary spend at the top end of the range we have allowed for a stable to slightly improving discretionary spend environment, while at the bottom, we allow for deterioration. And so what I just covered with you for FY '27, same holds true for Q1.

Okay. All right. That's really helpful. Maybe just touch for a moment on pricing in the competitive landscape today. Are you seeing any competitors price more aggressively to win contracts, especially larger deals in the context of some of the changes happening around, obviously becoming more agentic and just in general terms?

Yes, Darrin. Look, we continue to operate in a highly competitive environment. And when you think about pricing in '26, it was overall stable and at the same time, in Q4, we saw lower pricing in many areas of our business. And so as we look to FY '27, our guidance, including our 10 to 30 basis point margin expansion assumes that we're going to have continued intense competition and sort of our current expectations are baked in.

Our next question today comes from Brian Keane at Citi.

Congrats on these definitely solid results here. I guess one thing I was thinking about, Julie is just a delivery model. we're kind of pushing for this non-FTE kind of commercial model. And just trying to reconcile that with the growth in head count that was up 5%. To be honest with you, I thought with the genetic we'd be probably decreasing headcount. So just trying to reconcile those 2 factors going forward.

Sure. So a couple of things, right? We're experiencing a lot of demand in new areas, right? So we're both upskill and hiring in data and AI. And at the same time -- and you saw that we increased revenue per person this year. So you are seeing an increase in revenue per person, which is what you would expect in part due to AI, but we're also in growth mode. So when we look at next year, we expect to hire in every market, but it will be below what we've been hiring this year.

But very importantly, we still expect to hire more entry-level which is -- because we are very focused on really changing sort of what our entry-level workers do, what our reinventors do. And then -- and so that we expect to also continue. So it came in this year as expected, pleased with higher revenue per person. And next year, we do expect to hire, but it will be at a lower rate in part due to AI.

Great. That's helpful. And then just as a follow-up, just thinking about the managed services bookings, in particular, that was a source of softness in the third quarter. It sounded like a couple of deals, in particular, were pushed and then of strength this quarter. So just wondering, timing, did any of those deals come actually into the fourth quarter? Just trying to figure out how to model how the managed service bookings since it's I know you guys say it's lumpy, but it's tough to get a handle on where it's going.

And you went to the right place, right? Our managed services number, we have more -- our strategy are these large-scale deals and those are lumpy. And so that's what we explained in Q3, and you saw in Q4, they went up. They went up in Q4, not because of the deals that got pushed, which will come through. We expect some time in FY '27. But because of like the deals that we were seeing come through in Q4 came through. And so that is just the nature of our business as we continue to have large-scale transformations drive our business. It's always been lumpy. You've covered us for a long time. It's going to continue to be lumpy, which is why we don't guide to bookings. We really focus on revenue. And so focusing on the 3 to 6 and understanding the fundamentals of our business is how we -- it's how we drive our business.

Yes. And I would just add, as you think about the 3% to 6% for consulting and managed services, they'll both be in the range, the overall range of the guide that we gave. And when you look at it, by market, by industry, by type of work, we do see broad-based and balanced growth in '27.

And our next question today comes from Jason Kupferberg with Wells Fargo.

I just wanted to come back on the point you were making around the Middle East headwind. I think you said it worsened a little bit on the direct side, but it was in line on the indirect side. So I wanted to get a sense of what is assumed in the F '27 guidance with respect to the Middle East because presumably, you will start to lap that during the third quarter.

Yes, so the Middle East, let me just remind you, it's about $1 billion. It's roughly about $1 billion on an annualized basis. Julie talked about what we experienced in Q4. We do expect that to continue, and we baked that in. Overall, the top end of the range doesn't assume any growth in the bottom of the range assumes allows for deterioration.

Okay. Understood. And then I wanted to come back to the point you were making in terms of one of the sources of upside in Q4 itself. You talked about faster-than-expected mobilization. Just looking at utilization rates, it looked like they were steady quarter-over-quarter and year-over-year. So I was just wondering if you could elaborate a little bit on those mobilization efforts that were a contributor to the strength there in Q4?

Yes. Mobilization just means that we get the new contracts started sooner. So it doesn't affect the utilization. It's actually just having the people and getting them started on new contracts faster. So it's not about having the same people work harder.

And our next question comes from Keith Bachman at BMO Capital Markets.

I wanted to ask 2 questions, if I could. Julie, could you comment on the competitive environment with companies like Palantir and their FTE model, but they're not the only ones. There's others trying to emulate that model and even in the software companies are trying to deploy more FTE engineers. And I just wanted to hear your comments on how that may be influencing the competitive dynamics. It seems like Accenture and others are sort of with Palantir, how should we think about the competitors and then I have a follow-up.

I'm sorry. Can you repeat that because you kind of came in and out?

Yes, I will. Thank you. Julie, as you think about the competitive environment, I just wanted to see if you could comment on Palantir in the role of FTEs and how that's influencing the market. It's not just Palantir, also the software companies are deploying more forward deployed engineers? And how is that influencing the competitive dynamics with Accenture and others?

Yes. It's a great opportunity. As you know, we have a business group with Palantir, and the FTE model is a growth opportunity for us because we can scale FTEs and we're doing that with Palantir and with many of our other partners, and we've made those announcements publicly. And keep in mind that FTEs are used a lot with the sort of new pioneering things, and then it becomes something -- it's a repeatable offering. And so what Accenture can do with all of these partners, right, is we can help them have more FTEs because we're training and delivering more FTEs as part of our partnerships.

And then as we create those new solutions, we have then the scale to go repeat them and take them to multiple clients. And this is why you see so many of our partners -- it's why we are the #1 partner for these emerging AI data and our top ecosystem because we have both delivery models.

Perfect. And my follow-up relates to renewals. And I wanted to hear if you could speak to the economics, the renewals and how that's changed or any changes that might be occurring? And what I'm really trying to understand is with AI, there has been some deflationary forces. And when you think about the guidance associated with FY '27, how are you thinking about the impact of AI on sort of like-for-like renewals and the amount of deflation that you're baking into those due to AI.

Let me start, Keith, I think that as you step back and you think about the productivity, that's always been a part of what we do. that's what we provide to clients, that's why they come to us. And so as we look at the impact overall, it's really been steady, right? So the pace, duration, all of those things remain steady, but what's important for us is we're there with our clients. And while we are helping them, we're also expanding our work with them.

Yes. So we are definitely giving more productivity due to AI and like overall, though the impact has been steady. And we're offsetting as we have in the past with new kinds of work, more scope, et cetera. So absolutely giving more AI efficiencies and more than offsetting that as a whole.

Okay. So the FY '27 assumes sort of steady efficiency gains. Okay. Perfect.

Yes.

Our next question today comes from Jim Schneider at Goldman Sachs.

I was wondering if you could maybe talk a little bit about some of the AI model safety work you've disclosed, in particular, your partnership with Anthropic, if you could scope anything about that in terms of size, magnitude, duration and whether in light of the recent White House activities around AI safety, whether you've seen inbound inquiries from any other frontier labs or other potential customers around this kind of work?

Thanks for the question. Well, first of all, our acquisition of faculty earlier this year was driven in part because they have deep safety experience already. And so -- and that was us anticipating both the growth of safety as part of serving the Frontier labs, but also even bigger, right? Safety is a big component of what all of the enterprises have to do. And so when you think about our growth drivers today in AI and data, a lot of that growth is us building out that enterprise AI technology platform and embedding safety.

So safety is a big part of why companies turn to us both on the enterprise side. And as you saw, Anthropic selected us for our AI expertise, our security expertise, our AI safety expertise. And that relationship is nonexclusive. And in fact, it's important that we continue to actually work with multiple labs, which is what our expectation is. And of course, we're not going to comment specifically on any particular contract, but we are investing $1 billion over the next 5 years to build out that safety business, including a safety lab and the team.

And then as a follow-up, if you think about -- and I realize you don't guide for, if you think about your gross margin profile, you turned in some gross margin expansion for the full fiscal year which is the first time, I think, in a couple of years. As you think about the 10 to 30 bps of expansion for fiscal '27, how much of a component of that is gross margins? Can you expand gross margins? And can you maybe talk about some of the levers get there, whether that be fixed-price contracts, pricing, utilization or anything else that would kind of drive the gross margin side of the equation?

Jim, as it relates to gross margins, so within gross margins, we were really pleased with the 10 basis points expansion for the quarter and for the year, which were certainly a part of our overall expansion of 20 basis points in op margin for the year as well. When you think about gross margin, it's -- we're always focused on the different levers being pricing, contract profitability, efficiencies and how we deliver our services. But importantly, our investments are predominantly embedded in gross margin. So if you think about our acquisitions that we did this year, $4.9 billion. If you think about our talent, and our talent and our learning that we do, our R&D all of that, the majority of that hits gross margin.

So what you don't see is that our contract profitability, which expanded this year, which we were really pleased with is absorb the massive investments that we make year in and year out. And so the best way to look at that is the guidance that we give for op margin, which is 10 to 30 basis points for the year.

And our next question today comes from Bryan Bergin at TD Cowen.

I wanted to ask about kind of broader enterprise IT and hoping you can talk about what you're seeing in overall IT budgets and what your guide is implying about clients' 2027 technology spend. And specifically, I'm curious what you're seeing and how AI costs and all the considerations around tokenomics are unfolding? Are clients getting a better handle whereby services spend can kind of improve or not necessarily see budget reprioritization as you move forward?

Thanks, Bryan. So we're assuming right now kind of more of the same, so in terms of overall budgets and obviously, the budget cycles for most of our clients who are calendar year-end is only starting now. So we'll kind of know more in January and February. But we're assuming right now kind of more of the same. And you are seeing token costs go down. You've all probably been tracking even just in the last few weeks -- been new models that a big part of them, the new models has been the lower token costs. And of course, we're helping a lot of our clients work on their tokenomics. And what we've seen so far is that with the increase in spending on AI, which is kind of as a net new category, we obviously just posted 5% growth.

So the way we think about the dynamic is the more that tokens go down over the next couple of years, the more it's going to enable companies to use AI in more places and at scale. And to use AI, they need to change their processes, reinvent their work, build out that AI stack in order to really do it. And all of that is what clients are turning to us. And so the more change there is, the more they turn to us. So we see that dynamic as the token costs going down, and that's going to help generate longer-term demand.

Okay. Very good. And then my follow-up on the federal business. Can you just comment on how the government demand -- U.S. government demand evolved through the September fiscal year end. And aside from lapping the headwind that you faced last year, the partial headwind, can you just comment on you expecting a recovery of growth and how it may perform relative to commercial?

Yes. So as I mentioned, one of the many factors that helped us over-deliver in Q4 was over delivery by our federal business. No individual factor was material, but that was one of the factors. As we look at '27, we expect that federal will be a significant contributor too early to talk about sort of exactly whether it's above or at the same commercial but very, very -- we consider it to be very positive. And you've seen some of our big wins that we just announced, like the data platform at the Department of War. So we're very relevant to the agenda of the federal government right now. I'm super pleased with how those things -- federal is shaping up.

Operator, we have time for one more question, and then Julie will wrap up the call.

And our final question today comes from David Koning at Baird.

When I looked at consulting versus managed services growth, revenue growth, I think this is the first quarter in about 18 quarters or so that consulting grew as fast as managed services. And you also said you're guiding to 3% to 6% for both segments, which is encouraging for consulting. I guess I'm wondering, is there anything about the business where you either have more insight into the year now or maybe more recurring revenue that's kind of allowing it to grow as fast as managed services?

One of the things that we're seeing is, David, is that in our managed services business, our clients are really using managed services to transform as well as get certainty around business outcomes. And so we're seeing a lot of consulting even in our managed services deals in addition to the big kind of reinventions, whether it's like moving to the cloud or doing an ERP or transforming functions. And that's why it all comes from our strategy that we want to help our clients really transform the way they work. So you need operating model, need change management, you need process. And that is what makes us, of course, so unique is that we deliver that both in a consulting projects as well as in managed services. And so that's where we're seeing kind of that strength is really across our business now.

Got you. And just as a follow-up, acquisition spend relative to revenue contribution is a little higher. So the cost of acquisitions seem a little higher. Is that related to revenue -- like faster revenue growth over the next few years from the acquisitions themselves. So we might not see [indiscernible] in the past?

The answer is yes. And so our spend -- our acquisition spend is putting us into new areas like data centers, our data center acquisition, DLV is growing very high single digits, right? And so I'm sorry, very high double digits. Angie is like double digits, what are you talking about? So -- and remember, like what we always are thinking about, right, is how do we scale in the areas that are growing now that if we scale inorganically it's going to drive more organic growth because we can do it faster, but we also use them to expand into these areas that start small and then grow.

So think about what we did with Song, what we've done with supply chain and engineering, what we've done with Cloud over time. And so those acquisitions really speak to the long-term growth, and they're all intended to fuel organic growth. So you're not going to see them as a bigger contribution maybe in the first couple of years. But as we scale, it sets us up for that long-term growth.

Thank you, David. So in closing, I want to thank our shareholders for your continued trust and support in all of our reinventors around the world for everything you're doing every day for our clients, our partners and our communities. We will share more about our growth strategy and the opportunities ahead and also provide time to spend with our senior executives at our upcoming Investor Day. So I hope that we can see you in New York on October 14, and thank you for joining us today.

Thank you. That concludes today's presentation. We thank you all for attending today's conference. You may now disconnect your lines.

Good day, and welcome to Accenture's Third Quarter Fiscal 2026 Earnings Conference Call.

[Operator Instructions]

Please note, today's event is being recorded. I'd now like to turn the conference over to Alexia Quadrani, Executive Director and Head of Investor Relations. Please go ahead.

Thank you, operator, and thanks, everyone, for joining us today on our third quarter 2026 earnings announcement. As the operator just mentioned, I'm Alexia Quadrani, Executive Director, Head of Investor Relations. On today's call, you will hear from Julie Sweet, our Chair and Chief Executive Officer; and Angie Park, our Chief Financial Officer. We hope you've had an opportunity to review the earnings release, which we issued a short time ago.

Let me quickly outline the agenda for today's call. Julie will begin with an overview of our results. Angie will take you through the financial details, including the income statement and balance sheet, along with some key operational metrics for the third quarter. Julie will then provide a brief update on the market positioning before Angie provides our business outlook for the fourth quarter and full year fiscal 2026. We will then take your questions before Julie provides a wrap-up at the end of the call. We're also pleased to announce that we will host our Investor Day in New York City on October 14, more details to come. Some of the matters we'll discuss on this call, including our business outlook, are forward-looking and, as such, are subject to known and unknown risks and uncertainties, including but not limited to, those factors set forth in today's news release and discussed in our annual report on Form 10-K and quarterly reports on Form 10-Q and other SEC filings.

These risks and uncertainties could cause actual results to differ materially from those expressed in this call. During our call today, we will reference certain non-GAAP financial measures, which we believe provide useful information for investors. We include reconciliations of these non-GAAP financial measures where appropriate to GAAP in our news release or in the Investor Relations section of our website at accenture.com. As always, Accenture assumes no obligation to update the information presented on this conference call. Now let me turn the call over to Julie.

Thank you, Alexia, and everyone joining us this morning, and thank you to our more than 798,000 people for your extraordinary work. Before Angie takes you through the detailed numbers, I will give you some color on the quarter and on the progress we're making on our growth strategy. In Q3, we delivered strong results with broad-based revenue growth across geographic markets industry groups and types of work and once again took significant market share, underscoring the relevance of our services and our strong competitive position. To put our performance in context, we added approximately $1 billion in revenue in Q3 over FY '25 and $3.4 billion year-to-date over the same period last year.

We also delivered strong margin expansion, EPS growth and free cash flow while continuing to invest in our business and our people. This quarter, we had 30 clients with quarterly bookings over $100 million bringing us to 104 of such bookings year-to-date, a 13% increase over the same period last year. This is 1 of the best indicators of the depth of our client relationships and the scale of the reinvention programs we are helping deliver. I also want to give you context on 2 factors that impacted our results this quarter. First, we were impacted by the conflict in the Middle East. We saw a revenue impact of approximately $100 million compared to our expectations, which is all consulting type of work. split evenly between the direct impact on our Middle East business and indirect effects outside of the region. In the last few weeks of the quarter, we saw this indirect impact globally in products and to a lesser degree, in resources, mostly in discretionary spend. In addition, sales in the Middle East were impacted by approximately $400 million and also in EMEA due to longer decision-making.

Second, a couple of our large managed services opportunities moved into FY '27 for company-specific reasons. Now on acquisitions, because of the exciting OT cybersecurity acquisitions we announced today, which I'll talk about in a moment, we now expect to deploy approximately $9 billion of capital this year based on the anticipated closing dates of the acquisitions. We are also adding important capabilities in other strategic areas of growth, including our recently announced acquisitions of Alpha Health a service-led digital health platform in Italy and Whaler, a leading creator and social agency in the Americas. I am also thrilled to congratulate our approximately 124,000 people who were promoted this fiscal year, a 30% increase over last year, including more than 900 who were promoted to Managing Director. Our people make the difference in our ability to deliver our results and value to our clients.

All in all, we are pleased with how we're executing in this environment. Now let's turn to how we're executing our growth strategy to be the reinvention partner of choice for our clients and the leader in the widespread adoption of AI and I want to provide a few examples of how we're capturing new areas of demand in the age of AI, what we're doing to expand our total addressable market and the areas where we are shifting to more non-FTE commercial models over time. We believe that AI will be a tailwind for us and our industry as it scales because it is a catalyst for reinvention and is creating new opportunities to put growth and efficiency for our clients and for us. We are building a stronger foundation every quarter for us to win as AI adoption scales. Let me walk you through some examples. We're starting to see clients who have more advanced digital core has moved to larger AI transformation programs. You can see this demand in several significant AI-focused wins across multiple industries and markets which we publicly announced with companies like British Telecom Group, Mitsubishi Chemical, NSK, Piraeus , Stellantis, TEPCO, Vodafone and the Women's Tennis Association. The major theme of all of these programs is that we are moving clients from using AI to running on AI.

We're also seeing more clients move from pilots to production and all of this is happening even as AI is still in the early innings. This quarter, we saw another 100 clients initiate advanced AI projects with us. we have announced a number of expansions of partnerships with our top 10 ecosystem partners in AI and data, and our revenue growth from these partners continues to outpace our overall growth. We are also on track to more than double our bookings from our key emerging AI and data partners compared with FY '25, including Anthropic, Databricks, GEMINI, Mystral AI, NVIDIA, OpenAI, Palantir and Snowflake. We are deepening these partnerships around specific areas of opportunity where we can combine their technology with Accenture's industry, functional and delivery expertise. Now let's talk about our big move in OT security to create a platform-led growth business with a non-FTE commercial model. This morning, we announced that we are acquiring a majority stake in Dragos a leading platform for operational technology or OT cybersecurity, and all have runZero, a leading vulnerability and exposure assessment firm and Net Rise, a leader in device security. Together, these acquisitions will create a first-of-its-kind OT security platform that lets clients see threats, find vulnerabilities and fix them before it becomes a crisis.

Cyber is a key enabler for AI. We cannot have an AI revolution without critical infrastructure, and you cannot have those without OT security, which is where today the world is most vulnerable. The urgency is real. AI and geopolitical risks are accelerating the need for cybersecurity adoption for the operational technology that underpins critical infrastructure and industrial operations, such as power grids, pipelines, manufacturing, distribution facilities and data centers. Dragos which is the anchor of our strategy, has strong ecosystem relationships with our top ecosystem partners, including AWS, CrowdStrike, Microsoft, Palo Alto and ServiceNow, which we will leverage to scale. Our expansion into the OT cyber platform business builds on our strong foundation of cybersecurity services including OT, we have grown our services organically and inorganically over the last decade from roughly $700 million in FY '16 to $10 billion in fiscal 2025. And a 35% CAGR over the period, 4x that of Accenture's over the same period. This investment more than triples our total addressable market in OT security, which is growing double digit.

We are also expanding our total addressable market by going after a new exciting customer segment, the mid-market. We estimate that the mid-market, which we look at as companies with between $300 million and $3 billion of revenue, is a $240 billion addressable market for us, growing high single digits. That is why we are launching a new business next suite called Accenture Edge. This business will embed Accenture's large enterprise expertise and ecosystem relationships in business solutions designed specifically for the mid-market. We see that companies in this segment face many of the same technology, data, AI, cybersecurity and productivity challenges at large enterprises. But they often need solutions that are faster to deploy, more repeatable and rightsized for their scale. This segment is also an important priority for our ecosystem partners which see strong demand and want to partner with us because we can bring scale, consistency and delivery quality to a fragmented services market. Accenture Edge will also include seamless integration with Accenture's joint venture with Microsoft, Avanade.

Avanade will continue to serve as the provider of Microsoft platform services to mid-market clients bringing deep cloud and security and AI expertise to help companies adopt AI at speed and scale. Together, these actions show how we are building a strong foundation for us to win in expanding our addressable market across new growth areas and client segments and evolving towards more non-FTE revenue over time. We believe this positions Accenture well for our next phase of growth. Over to you, Angie.

Thank you, Julie, and thanks to all of you for taking the time to join us on today's call. We are pleased with our third quarter results with revenue above the midpoint of our guided range with strong profitability and robust free cash flow. We delivered these results while continuing to invest in long-term market leadership and returning significant cash to shareholders. Based upon these results, we are on track to deliver or exceed all aspects of our guidance provided in September.

Let me summarize a few highlights in the quarter. Revenues grew 3% in local currency with growth across geographic markets, industry groups and types of work. Excluding the 1% impact from our federal business, revenues grew about 4% and we continue to take significant market share on a rolling 4-quarter basis against our basket of our closest global publicly traded competitors, which is how we calculate market share. Operating margin expanded 20 basis points to 17% compared to Q3 results last year. This was achieved while making significant investments in our people and our business. EPS grew 9% in the quarter to $3.80 compared to EPS last year. Finally, we delivered free cash flow of $3.6 billion and returned $2.2 billion to shareholders through repurchases and dividends. Nine months into the fiscal year, we invested $3 billion primarily in 13 acquisitions.

With those high-level comments, let me turn to some of the details, starting with new bookings. New bookings were $19.3 billion for the quarter, a 2% decrease in U.S. dollars and 3% in local currency with an overall book-to-bill of 1.0. In Q3, consulting bookings were $10.3 billion with a book-to-bill of 1.1. And in Managed Services bookings were $9.1 billion with a book-to-bill of 1.0. Turning now to revenues. Revenues for the quarter were $18.7 billion, a 6% increase in U.S. dollars and 3% in local currency. Consulting revenues for the quarter were $9.3 billion, up 4% in U.S. dollars and 1% in local currency. Managed Services revenues were $9.4 billion, up 8% in U.S. dollars and 5% in local currency driven by mid-single-digit growth in technology managed services, which include application managed services and infrastructure managed services and high single-digit growth in operations.

Turning to our geographic markets. In the Americas, revenues grew 1% in local currency. Growth was led by software and platforms, high tech and industrial, partially offset by a decline in public service. Revenue growth was driven by the United States. Excluding the about 1.5% impact from our federal business, Americas grew approximately 3%. In EMEA, we delivered 4% growth in local currency, led by growth in public service and software and platforms. Revenue growth was driven by the United Kingdom and Italy, partially offset by a decline in Germany and in the Middle East. In Asia Pacific, revenue grew 8% in local currency, driven by growth in public service, banking and capital markets and insurance. Revenue growth was driven by Japan, Australia and Singapore. Moving down the income statement. Gross margin for the quarter was 32.8% compared to 32.9% for the third quarter last year. Sales and marketing expense for the quarter was 9.7% compared with 9.9% for the third quarter last year. General and administrative expense was 6.1% compared to 6.1% for the same quarter last year.

Operating income was $3.2 billion in the third quarter, reflecting a 17% operating margin, a 20 basis point increase from operating margin in Q3 last year. Our effective tax rate for the quarter was 24.2% compared with an effective tax rate of 24% for the third quarter last year. Diluted earnings per share grew 9% to $3.80 compared with diluted EPS of $3.49 in the third quarter last year. Days services outstanding were 48 days compared to 46 days last quarter and 47 days in the third quarter of last year. Free cash flow for the quarter was $3.6 billion, resulting from cash generated by operating activities of $3.8 billion, net of property and equipment additions of $186 million. Our cash balance at May 31 was $10.2 billion compared with $11.5 billion at August 31. With regards to our ongoing objective to return cash to shareholders. In the third quarter, we continued to accelerate our share buybacks and repurchased or redeemed 6 million shares for $1.2 billion at an average price of $198.84 per share. As of May 31, we had approximately $3.2 billion of share repurchase authority remaining. Also in May, we paid a quarterly cash dividend of $1.63 per share for a total of $1 billion.

This represented a 10% increase over last year. And our Board of Directors declared a quarterly cash dividend of $1.63 per share to be paid on August 14, a 10% increase over last year. And year-to-date, we have returned $8.2 billion in cash to shareholders, which is $1.3 billion more than the same time last year, demonstrating our commitment to shareholder returns. In closing, we remain focused on executing our business and capturing new opportunities for growth while continuing to invest to strengthen our relevance in the age of AI for long-term market leadership. And now let me turn it back to Julie.

Thank you, Angie. When I look at the breadth of work we signed just this quarter across industries and parts of the enterprise, it is staggering. The reason is that we are truly the only company that can cover at scale, everything from the AI and technology foundation to reinventing nearly every part of the enterprise. We bring a track record of delivering results for decades and deep, trusted relationships as seen in the fact that 195 of our top 200 clients have been clients for more than 10 years.

We are working with McDonald's, one of the world's most iconic restaurant brands, serving more than 70 million customers every day as we support key elements of their ongoing transformation. Together, we partnered across the enterprise with a particular focus on finance and people modernization and customer loyalty. This foundation is helping McDonald's become faster, more innovative and efficient as they continue to grow and stay competitive. Stay tuned for the next reinvented with Accenture episode on CNBC in the coming weeks featuring McDonald's Chairman and CEO, Chris Kempczinski; and how McDonald's is continuing to reinvent for the future. Now let's double-click on demand in the quarter. We saw a few major themes. Clients continue to invest in the foundations needed to scale AI. This includes strengthening their digital core through cloud, data, security and operating model transformation. A lot of our reinvention work today is helping clients get ready for AI and data remains a critical enabler with at least 1 out of every 2 advanced AI projects continuing to lead to a data project.

Second, clients continue to look to reinvent faster, leverage our proprietary platforms and expertise and achieve greater efficiencies and growth, including through managed services across the enterprise. We're seeing the nature of these programs with managed services evolve with clients asking from our consulting and AI expertise within them, exactly the shift we have been positioning for. Bath & Body Works is a great example of our work in managed services. A global leader in personal care and home fragrance, Bath & Body Works is one of America's most iconic retail brands. They have a strong growth agenda built around their core product lines, brand modernization and expanded distribution. Delivering on that requires a smarter, more scalable operating model underneath it. We're expanding our partnership to make that possible, consolidating fragmented operations across critical business functions into a unified managed services model with Agentic-AI embedded throughout and humans in the lead. The result is automation replacing manual effort, faster speed to market and significant cost savings and productivity gains that Bath & Body Works can reinvest directly into growth.

Another area of strong demand are the AI enablers we've been investing in from capital projects to data centers to learn manage to cybersecurity 1 of our largest AI enablers. As I mentioned earlier, OT security is one of the hottest areas driven by AI cyber threats and geopolitical risk. We are seeing that demand in our services business as clients look to protect the physical infrastructure that keeps their operations and communities running. For example, we're helping one of the largest electric utilities in North America secure its electrical grid, protecting power infrastructure that serves more than 10 million people. As utilities modernize and connect more devices across the grid, the cyber threat landscape is expanding rapidly. Operational technology environments like substations and transmission networks have historically had little visibility into cyber activity, making that harder to detect and slower to resolve. Building on a decade-long cybersecurity partnership, we are extending our work beyond traditional IT into the physical infrastructure.

Sensors will be embedded at substations, connecting them to a centralized security operations center for continuous monitoring and automation will turn raw data into actionable insights. The result is a more secure, resilient grid, protecting the homes, hospitals and businesses that depend on it every day. Finally, clients with more advanced digital cores are starting to take on larger AI programs, exciting green shoots. These large-scale AI programs are complex. And to make advanced AI work, deep industry and functional knowledge is needed in addition to technology and AI expertise. A great example is BT Group. One of our U.K. clients where our long-standing relationship is expanding into a new AI partnership for BT business, the division which provides the connectivity backbone for U.K. businesses and public services. BT business manages networks have massive scale and a threat environment that is evolving faster than traditional operating models can keep up with. So we're embedding AI directly into the core of how they operate, building on their existing network intelligence, customer data and service management platform.

AIOps capabilities with autonomous agents will detect route and resolve incidents with cell healing that accelerates how quickly issues are resolved. The result will be fewer disruptions, faster resolution and a more resilient network positioning BT business to lead the next generation of AI-powered managed services to its customers. As we look at the opportunity to scale AI, we're a partner of choice because we are delivering tangible results. For example, Fox Communications, the largest private broadband company in the United States worked with us to drive growth and efficiency across marketing, sales and service. Together with a leading large language model provider and hyperscaler, we built an AI engine that validates and enrich it's leads, generates personalized campaign content and automates brand and legal validations. In B2B sales and marketing, conversion rates increased and drove net new revenue. Lead accuracy jumped from 13% to 97%. Campaigns speed to market improved by 55% and marketing content teams are 40% more productive with capacity free to drive further growth. This is what AI ROI looks like in practice. Not a pilot, but a production-grade commercial engine delivering results at scale.

Banco Bradesco, one of Brazil's largest financial institutions is another great example of delivering tangible ROI from AI. Competing in the country's biggest lending market, new and used vehicle financing, their ambition was to grow deliberately at scale without sacrificing risk discipline. To fragmented platforms meant slower decisions inconsistent experiences and limited ability to compete at speed in a market where dealers and customers expect instant answers. Together, we've built a single unified platform that orchestrates the entire journey from the dealer portal and customer origination through government database checks, credit validation and loan processing. All of it works seamlessly in real time across many integrations with and for the bank. Bradesco grew its vehicle financing portfolio 7.3% quarter-over-quarter with a unified platform, a key enabler of that performance. With that, over to you, Angie.

Thanks, Julie. Now let me turn to our business outlook. Given the macro uncertainty, we expect more of the guided range to be in play for Q4. For the fourth quarter of fiscal 2016, we expect revenues to be in the range of $17.75 billion to $18.4 billion. This assumes the impact of FX will be approximately negative 0.5% compared to the fourth quarter of fiscal '25 and reflects an estimated 1% to 5% growth in local currency. And as it relates to our federal business, we expect to anniversary the headwind and get back to growth in the fourth quarter.

Moving to full fiscal year '26. Based upon how the rates have been trending over the last few weeks, we assume the impact of FX on our results in U.S. dollars will be positive 2% compared to fiscal '25. For the full fiscal '26, we now expect our revenue to be in the range of 3% to 4% growth in local currency over fiscal '25, including an estimated 1% impact from our federal business. Excluding the impact of federal, our revenue is expected to be an estimated 4% to 5%. We continue to expect an inorganic contribution of about 1.5%. And with our exciting announcement to expand into the OT security software market that we have just made, assuming those transactions close this fiscal year, we now expect to invest approximately $9 billion in acquisitions this fiscal year and we continue to have a pipeline of attractive acquisitions for FY '27.

For adjusted operating margin, we now expect fiscal year '26 to be 15.8%, a 20 basis point expansion over adjusted fiscal '25 results. We now expect our annual adjusted effective tax rate to be in the range of 24% to 25%. This compares to an adjusted effective tax rate of 23.6% in fiscal '25. We now expect our full year diluted adjusted earnings per share for fiscal '26 to be in the range of $13.78 to $13.90 or 7% to 8% growth over adjusted fiscal '25 results. For the full fiscal '26, we continue to expect operating cash flow to be in the range of $11.5 billion to $12.2 billion property and equipment additions to be approximately $700 million and free cash flow to be in the range of $10.8 billion to $11.5 billion. Our free cash flow guidance reflects a very strong free cash flow to net income ratio of 1.3. We now expect to return at least $9.5 billion through dividends and share repurchases and as we continue to return a substantial portion of cash to our shareholders.

Finally, as part of our routine review of our capital structure, including taking into account our elevated D&A outlook for FY '26 and we expect to access the long-term debt market to increase our liquidity for B&A spend and general corporate purposes as we look to optimize our capital structure and reduce our cost of capital. In connection with that, we expect to maintain a strong investment-grade credit rating with a low net leverage ratio. With that, let's open it up so that we can take your questions. Alexia?

Thanks, Angie. I would ask that each of you keep 1 question and a follow-up to allow as many participants as possible to ask a question. Operator, will you provide instructions for those on the call, please?

[Operator Instructions]

Today's first question comes from Bryan Keane at Citi.

It looks like the Middle East conflict should be alleviating given the recent agreement with Iran in the U.S. How does that impact you think the Middle East weakness you saw the $100 million in weakness you saw this quarter as we get into the fourth quarter because you guys are still highlighting some macro uncertainty in the guide?

Yes. Thanks, Brian. Because the indirect impact really started in the last few weeks and mostly in discretionary spend, we do think that there will be more impact in Q4, which is why we're saying that more of the range is in play. In these areas, it's not clear how fast, right? Things will change, particularly because some of the industries are dealing with kind of longer-term issues. So think about automotive, where we have a large presence they were already challenged and now with the higher gas prices that's added to it.

So it's difficult, of course, to predict and even exactly how it's all going to play out. But we -- because we started seeing it really in the last few weeks on the indirect impact we do think that more of the ranges in play. The impact in the Middle East, again, it just depends on how quickly people start to focus on kind of growing and that. So Q4, that's how we're seeing Q4.

Got it. And then the pushout on managed services, on our calculation, it was a little over $2 billion lower than we expected. Does that bump Q4 totals? Do you get that? Has that already been signed? Is that bump Q4 totals by an additional $2 billion or so? Just trying to quantify the impact there?

Sure. No. So what we saw was a couple of deals pushing out FY '27 with that and not into Q4. So we did see some slippage overall in EMEA, right, that we're to try to make up for in Q4. But the bigger deals. And remember, managed services, like when you're doing a deal, let's say, $300 million, $400 million, $500 million, it can have a big swing, right? And so we're seeing -- we've seen a couple of the big lumpy bigger ones move out for company-specific reasons to FY '27. So I wouldn't think about it as massively increasing Q4 at all because it's pushed further out.

Our next question comes from Tien-tsin Huang with JPMorgan.

Julia I was hoping, can you give us a little bit more on the thesis of acquiring the OT security assets. I understand the great growth spaces within security, but just there's a lot of layers to this. It sounds like you're adding more non-FTE content, but is there above average risk here because you're stitching together the 3 assets. And it sounds like there's going to be some initial dilution. So I just want to better understand the risk there. And then finally, just why prioritize security as an enabler for AI versus other areas to win in AI. We obviously trust what you guys have done in the past. I'm just trying to better understand because this seems more strategic than about adding revenue per se?

Thanks, Tien-Tsin. Exactly. This is about long-term growth and really a massive market when you start to think about how it's not about even assets, everything is going to the physical world, right? Physical AI is coming. Everything is going to be connected. And so you can't have an AI revolution unless you have critical infrastructure, and unless you secure -- when you start moving into physical AI, and you can't have that without OT security. 95% spend in the past has been about IT security and OT security is a much bigger market and critical need. And we're starting from a $10 billion cybersecurity services business that we've built over the last 10 years organically and inorganically, a 35% CAGR. And we've been in OT security all along.

And so one of the things that we do really well is to understand where the technology is going to create demand in our clients. In terms of the platform itself, Dragos has an excellent platform. The addition of Net Ryzen 10 is just enhancing an already strong program, platform. And what today do is they have a bunch of fragment, they have to like contract here and they have the counter and they have to stitch it together. So day 1, just the first thing is it's one contract, right? And then we'll enhance the platform, which Dragos has a ton of experience because they've been building that platform. So we don't see risk at all in terms of stitching it. And day 1, we're already making companies a lot happier because they can have 1 buy, not the -- this is a -- we view really a massive opportunity because it's meeting such a critical need. And it is simply you cannot succeed in AI unless you've got security.

Interesting. trying to learn more about it. That's why I want to ask. Just as my follow-up then, maybe for Angie, just thinking if the entire range in play to your answer to Bryan's question there. Just trying to think about the bottom line visibility there. What are you doing to protect the bottom line to the extent that you see the maybe if we'd start to lean more towards the bottom end of the range, for example, what are you doing to protect the bottom line? Is there flexibility there given all the investments that's going on?

Yes. And I think for us, we did because of the uncertainty that we experienced, particularly in the last 3 -- the last few weeks of the quarter, we did want to make sure that you understood that more of the ranges in play. Tien-tsin, I think one of the things that you're trying to get underneath is really around our exit rate and what that looks like going forward, right? So and I know that, that's top of mind for you guys because you use Q4 at that basis. But I went in to make sure that they get a few points out for you to consider because this is what we're thinking about as well.

So if you think about the acquisitions that we have announced today and the expected closing, we do expect to enter FY '27 slightly below 2% of inorganic growth. Secondly is our AFS headwind will sunset this quarter, and we expect that it will return to growth this quarter. The third is related to the managed services opportunities that Julie mentioned and when those actually -- when they close in '27. And then, of course, this conflict that Julie already mentioned in discussing with Brian, that's a variable, and we'll see how that evolves. But at the same time, we are executing in new areas, including demand in AI and expanding our TAM. So these are the elements as we think about in terms of how we look forward into '27. And importantly, within our range for revenue of 1% to 5% for the quarter, margin and EPS, we expect strong overall margin and EPS expansion for the year.

And our next question today comes from Jason Kupferberg with Wells Fargo.

So the consulting bookings growth was actually pretty strong in the quarter. And even on an LTM basis, it's pretty solid. But the constant currency revenue growth in consulting has obviously been a bit more tepid. So I'm curious what may be causing a bit of that disconnect? Are there issues with backlog conversion? Has the mix of renewals increase? Other factors? I mean, obviously, you talked about the Middle East, but not sure that would necessarily be affecting the revenues more than the bookings in this quarter specifically. And again, if we look at it on an LTM basis, it just seems like there's a little bit of a disconnect there. So would love any thoughts.

Yes, let me just give you a little bit of context here. So in terms of consulting type of work, we did see it tick down, and it was the result of the indirect and the direct impact of the Middle East. The $100 million that we called out was all in consulting type of work. We do expect in Q4, moving forward into Q4, AFS will return to growth, and we do expect a ticket, which is comprised of both the AFS returning to growth. as well as we had 4 consecutive quarters, as you call out of consulting business growth.

Yes. And Jason, the consulting bookings growth is very much driven towards the fundamentals that we continue to see, which is clients saying really actually have to reinvent meaning we're not just in our managed services deals, looking for just efficiency. We've seen a 3-quarter trend now of more consulting work in those large programs for managed services because our clients are asking us to help them use AI and change the processes to do more change management to really embed new ways of working. And so we're seeing that consulting grow in a lot of these larger deals that also include managed services, and it's a direct result of our strategy that says this is not a technology play. It's a business play.

Understood. Okay. And then just a follow-up on Q4. So at the midpoint of the 1% to 5% range, are you assuming a similar $100 million headwind as you saw in Q3? And if you can just parse out your expectations on consulting promoting services growth for Q4, that would be great.

So, overall, so the impact that we saw was really at the latter part of the last few weeks of the quarter. And so we expect that to continue for the full of Q4. So that is factored into what we expect. And then overall for the year, we expect consulting to be in the low single digits and managed services to continue to be in the mid-single digits.

Our next question comes from Kevin McVeigh, UBS.

With the $9 billion M&A, that's obviously up from $5 billion, is there any way to think about how that settles in 2027 in terms of the dollar contribution of that? And then what type of growth is associated with those acquisitions?

Kevin, so overall, with our overall, with the $9 billion of acquisition spend as we look forward into based upon the timing of when these close and the profile of the acquisitions themselves, we look to enter FY '27 with slightly under 2% of inorganic contribution from these deals.

Yes. And Kevin, in terms of just the profile of that revenue, what you're seeing is that we are moving into higher growth areas. So we're really excited about the cybersecurity acquisitions that we just announced, that's $208 million ARR, growing at 48%. So that's just an example of how we're using the acquisition to move into higher growth areas and they have a different profile in terms of their commercial model. So one of the things that I've said consistently is that things that our clients have been buying and services for a long time, it's going to take a while to like change the buying patterns which is why we're making -- but it's much easier to go into new categories or to provide new kinds of value and switch to non-FTE models.

And so you've seen that with what we just did with cybersecurity. You saw that with Ookla. We announced Alpha Health this week in Italy. That's also a services and platform combination. And so we're going to continue to move ourselves into non-FTE in part by these acquisitions that will then drive organic growth.

That's helpful. And then, I guess, with the shift to Accenture Edge, any thoughts as to go to market? Is it similar to kind of the traditional -- should we expect similar mix of consulting versus managed services? And does that impact the Microsoft relationship at all?

No. In fact, it should amplify that because we're really going to go after. We've done a great job with Microsoft and we now are even putting more resources into focusing on the mid-market. So there's going to be a lot of pull-through across the board. So Avanade has been super successful. We are -- have been making some acquisitions in this area. The way we go to market there is really ecosystem led. But what we can now do is like say we're in -- with one system partner and there's another opportunity, it's much easier to pull them in, which is why we think it will amplify what we're doing with Avanade and there's a seamless integration to make sure that that's what we're doing.

But we're also doing it in a much more efficient and focused way because our clients in the mid-market don't need the same client coverage model that we use for larger enterprises. And that's what's really exciting about this. And when you think about it, the industry has had this challenge now for a few years on discretionary spend, which is really smaller deals. And going into the mid-market, in a big way is going to allow us to structurally kind of offset the challenge on the discretionary spend for large enterprises through this. So we're really excited. It's obviously early days, but we've got a great track record with Avanade, and we think it will amplify Microsoft and the other ecosystem partners that the mid-market needs.

And our next question today comes from Jim Schneider at Goldman Sachs.

I was wondering if you could maybe comment broadly on the and budgetary impact you're seeing from AI infrastructure spending and token spending specifically in terms of upward pressure on their budgets. And what impact are you seeing on sort of what you view as to the -- your addressable TAM in terms of services and even software? And are you seeing any kind of change that would kind of drive some moderation in that infrastructure setting to benefit you in the coming quarters?

Yes. So Jim, the one of the things we're clearly seeing, in fact, we're-- we have a practice that we're starting to grow now is on how to help clients optimize their use of tokens. It feels a lot like the cloud scenarios. Remember when people were moving to the cloud, and then they were like, oh, wait a minute, we're not -- we're spending a lot more on the cloud than we thought, and we built a whole FinOps practice on helping optimize cloud.

So we definitely think that we're seeing that with the clients, and they're coming to us because we're doing a really good job ourselves of being able to know how you use the tokens, which models you use for which problems and that's something we've been focused on since the very beginning. It's also helping because we have delivered real ROI and our clients are seeing the spend, but they're struggling with the ROI and so it's helping us there. And at the same time, there's like only -- there's a certain amount of spending that's going to happen. And so we're not seeing it be material to impact the spend on services today. And if anything, we think it's going to drive more to use services and that's how we're seeing it develop.

And one of the things that we're really focused on is expanding our TAM in other ways, right, because the budgets haven't been even with AI, they're spending it differently, but they haven't been increasing. And that's why moving into cyber security platform business, triple -- more than triple our total addressable market in OT security. The mid-market is a massive TAM that we're now going to -- and that's not been a focus of ours other than generally. So we are really focused on expanding our TAM while we're capturing more of the AI spend.

That's helpful. And then maybe just as a follow-up. In terms of your M&A strategy, clearly, you've been pivoting towards more product-based acquisitions, both with the cyber assets just announced today as well as Ookla, would you expect given software valuations are where they are today, you'll be more aggressive going into fiscal '27 and be even harder for software acquisitions in the next few quarters?

Yes. So we are definitely seeing at our clients, this convergence between services and software and specifically areas where it requires domain knowledge quite deep understanding of the enterprise. So OT security is perfect. It integrates with all of our other tech ecosystem partners, but it really requires supply chain and engineering and so many skills beyond security. So we are going to continue to look at those opportunities because particularly as the technology and AI changes so much, clients are looking for more and more opportunities to not have to build things, to not have to sort of try to figure it out themselves.

And these are areas where we're embedding expertise and data. And that's kind of like our focus in that real expertise and so in addition to looking at it for acquisitions, we're also going to be building more and more and we've already started with our ecosystem partners where we're basically going to have IP together that create solutions that also drive then our services. So -- and we're building them ourselves, like so for example, our [indiscernible] platform internally, we're now taking to clients, and that's a platform we built to optimize tokens. So the strategy is around acquiring and partnering and building. And you should expect that because those are the opportunities we see in the market for growth that we'll continue to focus there. as well as AI enablers, right? So cyber security, capital projects, data centers.

And our next question today comes from Dave Koning at Baird.

And maybe just -- there's so many crosscurrents right now, macro, I just AI impacts, et cetera. When you kind of strip everything out around macro, et cetera, are you seeing -- do you believe that there's actually underlying kind of fundamental building of the AI demand? And would you expect, even though we see numbers decelerating a bit that the actual underlying demand for newer activity is building and can drive acceleration in coming quarters and years?

Absolutely, David. And we see that building every quarter. So think about our clients with $100 million of bookings of more. It's $104 million, 9 months in, that's 13% more than last year at this time. we called out that we're starting to see the AI like large enterprise programs where they're not just use cases, but really embedding it. And British Telecom Group is a great example. Stellantis is a great example. Stellantis is across their manufacturing, British Telecom is across their operations. And when we look at the AI projects themselves, right, while still small, there's been a steady increase in the average size.

So you're seeing that, and you're seeing that we're starting to have those green shoots of where clients have matured -- much more mature digital cores where we've helped them than the next step of these bigger AI programs. And that fundamental building of every quarter is continuing. The demand is the same, right, getting ready for AI and then deploying AI. So we're really optimistic because we believe AI is going to be a tailwind as it scales for us in the industry.

And one just a numbers question for Angie. Fiscal '27 margins, should we expect anything different than the normal 10 to 30 bps? I know with the new acquisitions coming on and investments, et cetera. But is the underlying margin expansion still expected to be about the same as normal?

Yes. And David, we'll give you updates on our overall FY '27 outlook, but our goal is always to continue to drive improved gross margins, improved SG&A while we invest significantly in our business.

And our next question today comes from James Faucette with Morgan Stanley.

I wanted to just spend a moment to sensitize us to the fourth quarter. I hear you very loud and clear that there's a wider potential range. At the same time, we're getting almost 2% of inorganic contribution plus recovery in federal services. So should I interpret perhaps at the low end of your range, ongoing deterioration that you seem to have indicated, it started to materialize in the latter part of the quarter versus if to get to the upper part of the range, it would be some improvement there and maybe middle of stabilization. I'm just trying to understand the scenario that you're trying to sensitize us to for the fourth quarter.

James, yes, that's correct.

Okay. And then turning to the acquisitions, really intriguing and then, obviously, a lot of questions this morning. I'm wondering how we should think about the -- this type of product-driven acquisitions? Is this really where we should think about Accenture being focused a go-forward basis? And what are the implications that we should have in our minds about the impact to long-term margins, margin trajectory, et cetera?

Sure, James. So first of all, our acquisitions are going to continue to be a mix of services, services and products and products, right? We are skewing, however, toward where we see the demand, which is in areas where we uniquely have the domain expertise, the clients needed, it's usually triggered by AI. And that's what we're seeing. So we're skewing our acquisitions to where we see the biggest growth opportunities right now and where we see the biggest growth opportunities. On the product side, are in these areas that are being triggered by AI. And I'll talk about valuations in a minute. But we're also continuing to do services acquisitions.

But again, in the higher growth areas, and we have seen higher valuations even for the services acquisitions like the data centers, but it's also paying off for growth, right? So if you think about what we did with DLB associates, that acquisition, they're growing very high single -- very high double digits, right? So think about our acquisitions as getting into higher growth areas leading where the biggest opportunity is and shifting us to more non-FTE. So the valuations on the services we're seeing are ticking up because our end demand. And then we have different valuations for software and product. And we're being very transparent so that you can understand. And by the way, that Angie never gives you something early for '27. And we wanted to give you that on the acquisitions to kind of help you think about the impact.

Operator, we have time for one more question, and Julie will wrap up the call.

Our final question comes from Jamie Friedman at Susquehanna.

Thank you for the additional disclosures here, especially around security. I had a question about consulting. The consulting book-to-bill was solid again, it was over 1.1% this quarter. It was 1.3% last quarter. I know there could be some FX in there. But that's despite some of the headwinds you called out, Angie. So I was wondering if you could unpack some of the consulting by type of work. For example, technology consulting strategy consulting, you mentioned change management, Julie. Yes, it must be a dynamic time to be a consultant. So I was wondering what parts of consulting you're seeing particular demand for?

Sure, Jamie. So first of all, our clients really do buy solutions. And so we don't think of it as like strategy or this kind of. And so -- but I think what you're getting at is sort of like what are the types of areas. So for example, digital manufacturing, cybersecurity, change management embedded and bigger programs, marketing because it's driving growth. And so think about what's driving this is where clients can get more growth and for efficiency.

So of course, our cost facility is also going up, like how do you look across the enterprise. So it's pretty broad-based in terms of like the functions, but it's all places where you can really get an ROI. So clients are very focused on whatever kind of work, whether it's AI or not, intangible results. And look, that's why clients come to us, right? When you have clients doing $100 million bookings, you don't sign those kinds of deals unless you have confidence that you're going to get results. And that's a real theme in our competitive differentiation and why we're taking market share is across the board. Clients are focused whatever kind of work on clear ROI.

And then for my follow-up, last quarter, Q2, you had a disclosure about 2025 fixed price at 60% of work. Can you talk about the evolution of fixed price? Is that type of work in particular demand and how the margin characteristics of fixed price may compare to the other dimensions of the company?

Jamie, let me take that. We continue to see our fixed price work be over 60% and continuing to increase. There's no real difference as we look at it by type of work. It's in the similar zone for both consulting as well as managed services. And obviously, you see that play out in our margins overall as well. So margin is not a big difference that I would call out relative to fixed price versus the other commercial constructs. But it is embedded in our 20 basis points of expansion for the year.

So thank you, everyone. In closing, I want to thank all of our shareholders for your continued trust and support and I want to thank all of our inventors every day for our clients and our communities. We'll talk to you next quarter.

Thank you. That concludes today's conference call. We thank you all for attending today's presentation. You may now disconnect your lines, and have a wonderful day.

Hello, and welcome to today's Carnegie Mellon University Software Engineering Institute's webcast, Rethinking and Maturing AI Adoption. My name is Ipek Ozkaya, and I'm the Technical Director of AI Native Software Engineering at the SEI. And I've had the incredible pleasure of leading this project focused on AI adoption maturity with our team at the SEI and the incredible team at Accenture.

We want to make today's conversation as attractive as possible. So please feel free to put your questions into the YouTube chat area. And we've already received close to 200 questions. There is no way we'll be able to get through any of them in completeness, but we'll try to get to them as much as possible afterwards.

It is no surprise today that businesses are -- across all sectors are redefining themselves and going through a structural shift through AI solutions. And they are trying to redefine their operational relevance, their operational workflows as well as get ahead of the businesses through ROI. Software-driven organizations are also going through the same challenge. In fact, the software as a discipline is being redefined through AI, looking into efficiency, productivity and of course, some of the risks that come with it.

And clearly, all the organizations that deliver us the frontier models, OpenAI, Google, Microsoft and Anthropic are developing improved capabilities around the cloud, and we're receiving these capabilities around a lot faster. If we look into 2 years ago, the early generative AI models could barely solve some of the cybersecurity tasks. But today, we know the Mythos and GPT 5.5 could actually execute some of the complicated multistage attacks on vulnerable networks. So this is a very challenging act to get ahead.

And lasting AI value and ROI is at the top of every executive, every engineer and any worker or across any of the sectors. And intentionally to finance systematically manage AI practice maturity is not now a luxury. It's a key differentiator for organizations to get ahead. However, doing more AI does not define maturity. Scaling AI to every single engineer in your organization does not define maturity.

So that's actually where the challenge is in today's conversation. True AI maturity is measured by how you are able to deploy with engineering rigor, how you're able to develop resilient and transport the capabilities, how do you bring together governance approaches where it's needed and be able to evolve the technologies and catch up with the evolving technologies.

So of course, it was not an easy task to develop an AI maturity model amidst one of the most fastest changing technologies, and that presented two challenges for our collective teams. One challenge was how do you provide structure in a rapidly changing environment. And then the other structure is to really focus on maturity rather than compliance and checking the box. And we'll try to get to some of those and how we address them.

Our approach has been in [ price ], focused on the data and the reality and running some of the early adopter programs. So the results we are going to share with you, and you also have the open model available to you, that reflects the collective insights of the research, academics, industry and our private partners throughout different organizations.

So today, I am joined by my colleagues, who I have the incredible pleasure to have shared the journey. And I won't be able to do justice to their background, so I'm going to allow them to introduce themselves. So thanks a lot for joining me today. And to get us started, could each of you please briefly introduce yourself and share the lens through which you view AI adoption and AI maturity? And I'd like to start with RP from Accenture.

Thank you very much. It's been a pleasure working with Software Engineering Institute and CMU while we build the AI maturity model. My name is Rajendra Prasad, I go as RP. I'm the global technology lead for Accenture.

One of the key business challenges when I speak to a lot of leaders in the industry today is how do we measure the business benefits of AI and how we scale AI implementation across the enterprise that can deliver the promised business outcomes. For that, one of the key aspect is to kind of approach the implementation of and the benefits in most measurable, structured disciplined, methodical way. And the maturity model that we work together will help us to accomplish that thereby delivering business leaders business benefits and then scaling the implementation. Thank you for that.

Thank you. Anita?

Okay. Great. Well, first, it's great to be here with my colleagues from Accenture and SEI. It's been very exciting to work on the AI adoption maturity model with our 2 organizations. As far as introductions, my entire professional career has been about defining, researching and advancing modern software engineering practice. And of course, now that includes AI.

I currently direct the software engineering portfolio of work at the Software Engineering Institute at Carnegie Mellon University, where I lead efforts to make software a strategic advantage, especially for national security and national defense. We do that by advancing trustworthy and resilient software-intensive systems. The lens that I view AI adoption and AI maturity is to build on the many lessons that we learned when we worked with [ Watts Humphrey ] on the capability maturity model for software.

The CMM and CMMI, CMM for software and CMMI, was a structured framework to help organizations assess their software development practices, also improve their software development processes and then most importantly, improve project outcomes. At a time when there is increasing hype and pressure to take advantage of AI for business transformation and for cost improvement, we can use the AI adoption maturity model in a very similar way to assess AI capabilities and build a road map to strengthen the practices to achieve AI goals.

Thanks a lot. And we also have some voices from the trenches. Tony, could you introduce yourself for us?

My name is Tony Leraris. I'm the Accenture CIO. And I'd also just like to thank you for letting me be part of the conversation and being part of the work that's been done in the past few months. It's been a real pleasure.

For me, I'm responsible for enabling the 750,000 people of Accenture with the right technology and innovations to be able to serve their clients as well as the technology that we need to run our business. And when I look at AI, I tend to look at it from a very practical lens. And so for me, it's how do we scale that across an enterprise of this size in a way that is sustainable and responsible.

It is easy to deploy generative AI technologies. It is hard to operationalize it at scale. And so what I'm looking for is how do we do this in a sustainable, responsible way to generate value for our company and for our clients.

Thank you. John?

First, I'd like to say thank you so much for our Accenture Partners. My name is John Haller. I've been at the SEI for about 10 years in total, doing applied research around operational resilience, right? How can organizations sustain their critical services, what's the balance between protective controls and sustainment controls? And now, what are the AI implications of that? I also spent about 8 years in a large financial institution, helping with information security and the join between information security and resilience and operational resilience.

So when I think about AI adoption, I'm really looking at it from the perspective of technology risk management and how that impacts the other risk areas and also the cybersecurity perspectives, which we'll talk a little bit more about.

Thank you. Let's go to Majd.

Thank you, Ipek. It's a pleasure to be with you from Accenture's beautiful New York City offices overlooking Manhattan. My name is Majd Sakr. I'm the Chief Learning and Research Officer at Accenture. That means I worry about our workforce and their capability to make sure Accenture remains competitive. I'm in the same role at Accenture [ Learn Vantage ], which is the external facing workforce training arm of Accenture, where we train millions of our enterprise clients, those workforces.

I'm also a computer science faculty member at Carnegie Mellon University. I'm delighted to be with my SEI colleagues here, where I've been for the last 20 years. And I'm part of a large research lab that runs a lot of studies on workforce training.

From my perspective, Ipek, I spend -- from my Accenture roles, I spent multi-days with the C-suite and with the board talking about AI and workforce training and the evolution transformation that needs to happen. And a lot of the questions that we get is, are we doing this right? And where should our big bets go? And what are the right transformations that we need to think about and we need to strategize around?

And so there's a big need for a scientifically proven AI maturity model that helps guide the enterprise and all of these leaders to recognize where they are, where they're headed that is anchored in a lot of fundamental research that enables them to feel confident in terms of the outcomes that are going to come out of that and then the next steps that they can take so that they can move their organization in the right direction.

Thank you for that. And Kaveh, please.

Ipek, thank you for having me here. I have spent my entire professional life in healthcare. I serve as Senior Adviser to Accenture for the healthcare sector. I practice medicine. I spent early part of my career as an operator and delivery system and funders. But the last 2 decades, I've been focused on the intersection of technology and healthcare delivery, how do you make healthcare affordable and accessible and effective.

I have been very concerned about a particular issue that we see globally in all rich countries, which is that the demand for healthcare is rising because of an aging population while the workforce available to serve that demand is actually in absolute decline. And so the mismatch is putting every developed country in the same place, which is they're going to run out of caregivers.

And there were no good answers. And I became interested in AI primarily because I saw no other path for us to extend human capacity. If we couldn't have a technology that could take some of the tasks away from humans, give them time back so they can take care of other people. And generative AI, in particular, because of its ability to take over language skills, was really the first technology we had seen.

What was interesting to me about that and why this maturity model is so interesting is because the benefits of society did not -- do not come strictly from the existence of technology. It's necessary, but not sufficient. The societal benefits actually come from how the work has changed. And what's important about this maturity model is how it emphasizes that maturity is not just data and tech. That's 2 of 8 elements, but there are other elements, to include work and workforce-related elements, that are just as critical to AI maturity. And I think that is a really critical understanding that needs -- that organizations need to have.

Thank you all for that. And for our audience, if you realize it's by design that we have different perspectives because AI today is what in terms of industries, in terms of disciplines, and it's all around in many of the work we do.

So let's then go through -- as we were going through our data collection, we've come across a lot of misconceptions of AI adoption and maturity. So that's what I would like to ask to my colleagues here. If you have to identify the single biggest misconception organizations have about AI adoption maturity today, what would it be? And maybe Anita, if you would get a start [indiscernible] we see at the SEI?

Well, I'm going to build on all the points that you just made because I think they're right on. I think a big misconception and one that I think derails many AI programs are just the things that you talked about. People say AI maturity is primarily about tech. Okay? It's specifically about having better models, more data, all things like that. This belief really can lead organizations to investing money in some of the wrong places, millions in fancy, large language models or ML Ops platforms before they really clarify what business problems actually need AI.

Secondly, hiring top-tier data scientists while ignoring that the frontline staff really lack the training or incentives to be able to use AI outputs. Another issue, building models that work in notebook, so working in the small, but really fail in production because operational workflows, data pipelines and/or governance aren't quite ready. And one more, confusing experimentation. So maybe 1 successful use case works.

But with maturity, we're talking about something else, right? We're seeing it sustained, scalable, accountable value delivery. So these are at least some of the misconceptions that build on what Kaveh had to say.

And RP, you see organizations of all sizes and all sectors. So what are some of the misconceptions that you observed?

I think the fundamental -- I do say that more than misconception, the belief that the technology can transform in a large enterprise where people are the most critical aspect of the change is where we need to balance both. The change management, all the aspects that Anita talked about are very critical for enterprise. How does the change management and a maturity framework can help enterprises, the critical success factor for managing change is quantify and show the benefits to the people that the implementation of technology, in this case, AI, be it systems, be it software, is delivering the promised business outcomes.

So the measurement aspect, change management aspect and managing that very, very [ thinly ] is the key fundamental premises on which enterprises has to operate as we move forward. That's kind of what I get when I speak to my leaders in the field.

True. And I guess 1 of the misconceptions related to that, as we saw through is like bringing tools does not mean AI adoption if you're not measuring and if you're not building that change. And Kaveh, you mentioned it from the perspective of healthcare. So what are some of the misconceptions in one industry sector, maybe?

Because healthcare is probably a sector where you're not going to be having technology doing whole replacement, it's just going to be partial. What you're actually going to do is have to remodel the jobs. And to emphasize that, there's 3 layers that we're seeing organizations in healthcare have to deal with. Because AI maturity is not about does the AI work, it's, does the organization get a benefit.

So first layer is individuals need skills because technology is a coworker. You're now spreading the tasks out between you and technology and you need certain skills. The second one is at a more organizational level. I'll give you a hypothetical, and we model this. If I have 10 nurses in a hospital medical surgical floor doing the same thing today, the future state for the same workload is 6 nurses plus technology reallocating the tasks. And those 6 nurses aren't all doing the same thing. Some are doing highly specialized things that come and go as needed, and some are doing the same thing all the time.

So as a manager, instead of having 10 people showing up all doing the same thing, I have 3 people showing up doing the same thing, 3 people showing up doing specialized thing, plus technology. And as a leader, my work is reorchestrating all of that. That is a different job for the people and for the manager.

And then the last one is going from here to there is an operating problem. A CEO of a very large health system -- and I had this conversation. And he completely accepted the premise of the nature of the problem. And his statement to me was, "I don't know who to give this problem to. It doesn't fit in our operating model today. It's not the job of HR, it's not the job of IT, and none of my operators actually know how to take us through this journey." So we're seeing all those levels in play if you are going to get the benefit of the technology.

And it's so true because as we were developing the model, one of the areas that we focus significantly on is workflow reengineering because it's really rethinking workflows with AI, and that's actually a reflection of maturity. John, we've seen a lot of those. So what are some of the misconceptions that you and I have observed?

Well, I've heard organizations talk about reinventing cybersecurity and risk processes, right, as if something completely new is needed to safely manage AI adoption, right? And I think there's some nuances and caveats there. I mean many of the things or most of the things that your organization is probably already doing are very applicable to managing AI technology and managing those risks, which we'll get into a little bit more detail about that.

So the cybersecurity practices you have now are the good basis for safely adopting AI. Notably, this conversation is not specifically about Mythos and AI sort of in the attack, which we could probably have a separate webinar about, right? But start with the cybersecurity practices you already have. And then when you think about risk, right, it's not a separate risk function. It needs to be baked into the risk activities that you already do.

From an enterprise risk perspective, it's really about this idea of cross-domain risk, right? Like if you have a large enterprise risk function and you already have certain domains, technology, information security reputation, like those conversations between risk areas are really important. They've always been important for technology, but now they're really important, and they support the kind of things that Kaveh is talking about, how do you really bring all the stakeholders on board? How do you understand maybe what impacts and other areas might be?

I think it's very important because we're not AI does not mean you throw away all the good of the discipline of any of the engineering software and cyber. And that is, in fact, a misconception that a lot of organizations go through. Majd, I find your dual role very interesting, and you see things probably from different perspectives. So anything else to add?

Sure. I'll mention two quick misconceptions. One is this laser focus of AI as an efficiency and productivity opportunity rather than seeing it as -- yes, productivity and efficiency alongside growth and potential reinvention of what you do. Products and services are all now up for being disrupted, being thought of differently and how do you offer these things has to be thought through.

The other one is something that was already mentioned, which is if you get the tools and you train people on the tools, then you should see like where is the productivity or where is the innovation. And we recognize that there are many different things that need to happen along AI literacy building and then hands-on training.

And then you have to start going to where Tony was speaking earlier on how do you integrate and operationalize AI in the day job of the individual. So you have to start thinking about role-based training, and you have to start thinking about domain, whether it's healthcare or financial services. That's where you need to take the training. And then you will start to see that people will integrate AI in their day-to-day work.

So we -- I'm following some of the audience comments, as we like there's an audience comment that summarizes the conversation as skill sets, tool sets and mindset. And I think that's a very nice way to bring it to summary.

So Tony, I'd like to go to you next because as the CIO of a very large operation on the global scale, you have a perspective from the trenches that probably changes by the minute. So what keeps you awake at night [indiscernible]?

Yes. I mean the -- the challenge is not for me how to give people access to AI. What I really think about is how do you scale this and operationalize it. So what am I concerned about? I'm concerned about governance. As John mentioned, I think a lot about risk. And then I want to make sure that we have accountability for the outcomes that we should be driving from AI. And it's not just productivity, as Majd mentioned.

And so for me, I really do think about how do we operationalize that. And I think that for me, AI maturity is not just how we deploy this. It's how do we do it responsibly, sustainably and end up with measurable business outcomes. If we do that, like that's what I'm worried about is how do we do those things, not how do we give people the technology.

Some of your responses actually are very relevant to some of the questions we got. So here, I'm going to pull an audience question that we got during the registration. And John, this has all your name written on it. The question for goal is, what are the cybersecurity challenges when adopting AI?

No, I think it depends on where you're at in your journey. And there are certainly risks, and there are certainly cybersecurity challenges, right? But if you're starting out and you're in the exploration or experimentation stage, you need to have the basic blocking and tackling of cybersecurity, right? Because there is there is certainly a data protection risk here, right? If you say go forward and experiment, the proper controls need to be in place so that your data is essentially not going out the door and being used to train models and things like that. So that basic blocking and tackling needs to be in place.

And then as you're adopting more and more AI and your organization has a dependence on kind of the stability and the AI services and the trustworthiness, there are some risks that we already manage in cybersecurity, but that become a little bit more important, right? Like the integrity of data, the trustworthiness of data, controlling access and things like prompt injection and so forth. When you think about kind of towards the future ready and particularly agentic workflows, right, then we get more into how do you manage all those agents and how do you manage their access and how do you manage their entitlements, right?

And this is something that a lot of organizations that I've seen, due to varying degrees like service account management, for example, but it just becomes particularly relevant as you go towards agentic. But the message really is do the blocking and tackling, think about the additional risk as you implement and put those controls in place. And then as you go to agentic, there are plenty of tools and resources out there to help people.

The other thing I should mention with respect to the model, the model is the super set of practices for adopting AI. And there is cybersecurity content in the model and the specific kind of slice that is adoption-specific. But for anyone who's wondering it's not everything cybersecurity for AI, right? For example, you're not going to see specific content about vulnerability management. Why is that? Because you need to manage your vulnerabilities anyway, right? So it's the things you need to focus on that we think are very -- there's practices and processes that we think are very relevant to adopting AI.

And that's actually an important point because you need to have the practices to start with and how you're augmenting for AI and what are the AI-specific ones rather than just reinventing the wheel and some of the aspects. Unless there's anything else to add, I think it's time to start focusing on talking about the model and aspects of it. But I'd like to start with asking RP and Majd because what happened is about 1.5 years ago, RP and Majd came to the SEI with the challenge of developing a maturity model for AI adoption, so given the reality of the [ state ]. So why -- what triggered that, RP, if you would like to give us a little bit of historic background to why we started?

It's a very important and very interesting question. Thank you for asking. I think Majd will add to it. One of the key discussion point at the time when we thought about implementing AI, AI and the technology is changing at a rapid pace. The speed at which it changes, I call it as 1 hour is equal to 1 day, 1 day is equal to a month, 1 month is equal to a year of speed are you changing.

So the speed at which AI technology is changing, enterprises needs a methodology, a mechanism, a process discipline to kind of go from point A to point B. I come from what Anita mentioned is what's [ Hamre School ], where if you want to go from point A to point B, you first need to know where you are on the map. This is what's famous [ court ], right?

So we wanted to understand how do we get this methodology and maturity spectrum designed and built. Then who is better than SEI because you guys have the research, the experience and the expertise to look at systems, software engineering, multiple disciplines like security, configuration management that required project management, measurement area where Anita's expertise in there. So if you look at all of them, including the continuous evolution of the technology, we thought we should work along with the SEI. And thank you for the partnership. And that's the trigger, giving industry and enterprises model and the mechanism and the methodology to drive and implement the rapid pace of change of technology in a very structured way. That's kind of the context.

And that was actually a fun challenge for us to tackle. Majd?

Well, I mean, we've gone through many inflection points, and every one of them requires us to rethink how we do things and how we're going to approach to solve certain problems. And what are things that we need to challenge ourselves on so that we can do them better?

And one of these things that AI is challenging is how does the enterprise rethink itself, what it offers and all of these things. And when you look at what was out there in terms of AI maturity model, unfortunately, there was a lot of initial efforts that were not -- that were narrowly focused, and they were not evidenced in research. And so we wanted to head in the direction where we can substantiate the work that we're going to put out there for the world to benefit from.

And so we were driven by a lot of the agendas and strategies that were set by organizations and governments across the globe. So it was important for us that the model will be a global AI model, not necessarily North America-centric or something like that. And we wanted to make sure that it tackles all the different enterprises and organizations.

And to double down on what RP said, coming to the SEI was a no-brainer. The global credibility that you hold and the history that -- of what you have accomplished in the past and how much you have benefited the world in improving their adoption of technology and software development gives a lot of credibility to us now taking all of that and enriching it with a lot of what Accenture brings to the table, which is process reinvention, how do we think about the workforce, how do we train the workforce. So it seems like a perfect partnership. And so far, it has been. So that's how the story came together.

Well, thanks a lot for that background. So let's share with the audience, a little summary of the maturity model, AI adoption maturity model that we've developed. And we've also gone through a number of early adopter programs. But before I get started and talking about the model with Anita and John, I would also like to pull two questions from the audience that came earlier on because this is important and we're going to address them. And one of them is from Leonardo, how much data is behind this framework and what companies have been surveyed, given how novel AI adoption is? Incredibly valid and very fair question. And the other one is for Marco, who asks, most maturity models assume a fixed end stage you climb toward. What model capability is improving faster than most organizations can execute a single phase of their road map? How do you build a maturity model that doesn't go stale? And where do you draw the line between sequencing adoption deliberately versus just staying adaptive because the ground keeps moving?

Excellent questions. And in fact, we did not take those questions lightly. We ask those questions to ourselves from day 1 because there has been quite a number of successful initiatives in maturity modeling as an instrument. As we sit across these two organizations, SEI has been very fortunate and has changed some of those techniques, and we still use some of those like CMNC.

So the way we approach this was how do you rethink AI adoption and maturity. That was the challenge that we ask ourselves. Our #1 activity was to look around. Because if there were already successful models, why not use them? So we did a very extensive literature search as well as survey and executive interviews in terms of looking in the challenges and what instruments they use.

Of course, there are quite a number of techniques out there that look into AI readiness and maturities, some of those [ maleate ]. And within the next couple of weeks, we'll actually release that study. There are 136 examples. Some of them are only research efforts. Some of them are organizational techniques that are mostly internally focused, but there are other publicly available models as well. What we did not observe was the evidence of how they were developed or how they were used.

So with that, and we also looked into a number of organizations as well as industry -- large-scale industry in terms of where their challenges are. So the way we rethought the AI adoption is, first of all, how do you focus on AI relevant capability? Because at the end of the day, AI is software. As the Software Engineering Institute, we very strongly believe that. And because AI is software, if you're not disciplined in your software, guess what, your AI adoption journey will fail. Same can be said for cybersecurity.

So we try to focus on that delta that is focused on AI and AI adoption maturity. And how -- where will you provide value. Given that the model that we've developed is focused on AI-relevant capabilities, because a lot of organizations will [ die select ] to move fast. And there are a number of components that we inserted into the structure of the model that will allow us to adapt. One of them, we call context attributes, which will allow organizations to adjust where they focus as they are looking into understanding their maturity. And these context attributes will also allow us to develop industry-specific or sector-specific or particular workflow-specific aspects of the maturity.

And the other aspect is the goal is not to define and identify a level. The goal is to really get to a road map. So there is the structure of the model at the end, which allows us to recommend where you would like to start where quick wins could be or where you really need to invest to get ahead of the curve. So that was the background that we developed it. And all of that data will -- we have, and we're going to share it through other means as we go through the next couple of weeks and months.

So -- but of course, any maturity is a progression, and we did think through the progression as well. And Anita, will you please talk us through some of that levels as well?

Sure. Do you want to go to the next slide?

Sorry. There you go.

Okay. There you go. Thank you. So first, today, many organizations are pursuing AI adoption, but it's with a really vague AI everywhere mindset rather than a clearly defined way or a clearly defined strategy for approaching AI adoption. So when you look at this diagram, the AI adoption maturity model is really intended to help organizations determine the level of AI maturity to pursue and then also help them to guide management of its implementation road map.

So when you think about maturity and you see these maturity levels, it's determined by the presence of intentional, repeatable, actionable and governed approaches. There need to be clear objectives standardized practices, and Ipek, you've talked about it, measurable outcomes and continuous improvement.

Yesterday, I think Manish Sharma, he's the Chief Strategy and Services Officer at Accenture, he had made a post in LinkedIn. And he said that this is about how we create value differently. And that's what we're trying to do here is create value by applying AI, but getting to a very different set of outcomes. I think, Majd, you said yesterday, it should be a part of the new DNA of the organization. So that's another way to think about this.

But since we talked a little bit about CMM or CMMI, this is unlike CMMI in that it is not about organizational certification. So the work that we've done here doesn't go into certification purposes.

And John, would you like to talk to some of the dimensions and the capabilities that we have?

Sure, maybe we'll go to the next slide.

Yes, we'll go to the next slide.

Just to kind of segue off what Anita said. It's not a certification. And what I've seen a little bit of my professional life is sometimes when you put a maturity model in front of technology executives who are in front of, I'll just say, type A people who want to achieve, they want to get to that level. They want to get to green. Well, it's not about getting to green. It's about identifying your gaps and areas for improvement and sort of challenging yourself, right? RP referred to it as like a catalyst guidance and catalyst in the forward to the document here. And that's really important. It should be that catalyst for just for where do you want to go and what do you need to get there.

So with respect to the different dimensions, they're really divided into organizational change management and then engineering life cycle. I'm certainly not going to drill into the slide, but it's all of the areas considered essential for adopting AI, from workflow reengineering, to strategy, including how do you identify technology partners and people that can help your organization, to cybersecurity and basically to architectural aspects of adopting AI. One of the things that you don't see on the slide but that's in here as well is what we call maturity indicators. And for anyone who's familiar with CMM or CMMI and the generic practices, this is the modern or, I'll say, tailored version of maturity indicators for the model. How do you know these things will actually persist and you're really adopting that culture? Do you fund them correctly? Do you measure them correctly? Do you think about the risks of what you're doing well or not doing well in a certain area, right?

So again, we could spend a lot of time looking at each of these. But this is all of the areas in the model that are essential for adopting AI and figuring out where you need to go.

And the model is publicly available, and we're happy to answer questions online as well.

Ipek, I wanted to build on some -- I think it was a question that you received from someone named Marco. Also, we are working really hard to keep this as a dynamic model. So as we are working with early adopter organizations and we have lessons learned, we are incorporating those lessons learned back into the model. So Version 1 of this is available, but we are going to keep it dynamic and keep it current by constantly refreshing with the lessons learned and the early adopter experiences that we have.

And then the question about climbing towards a fixed state is actually a very good question because one of the constant debates we had as we were both going through our early adopter program as well as developing some of the practices was how do you measure maturity and what does measuring maturity mean. And we actually have a dimension called maturity indicators. And John, maybe if you could say a couple of words about the maturity indicators as well? Because that's essential because the goal is not to climb to a state, but the goal is to be able to understand how those maturity indicators guide you through the process.

Sure. So they really revolve around accountability for the practices that are in place, measurement of the practices and then how you're actually resourcing those practices. So at lower levels, you're basically assigning accountable individuals, you're making sure that your policies and procedures and all of those things are fully in place to support adoption.

And then this will sound familiar to people who are familiar with maturity models generally, right? As you go up the scale, you're thinking about the risks of gaps you may have in certain areas, and you're actually building that into your risk management process, right? It's not just we have some risks and we get together. It's we have risk, we formally recognize those. We figure out how important they are. As you go further up to scale, you're really assigning metrics, key risk indicators, key performance indicators and making sure that people have kind of the same picture.

And then as you go further up to scale into future ready, this is where your organization is really striving to be a leader in the space to influence the community, right? I would say, this is where you're really kind of, I would say, spreading your wings, it's built into your business. So we use all of these -- we use words like repeatable and predictable. But at the higher maturity levels, it's not -- you don't really have to think about adopting AI. It is how you do business, right? But the -- I mean with that, that doesn't mean the model is only for organizations that want to get to that level. The model is for organizations who are trying to improve and who are trying to get on the path and get on the journey.

That's very true, and it's also driven by what you would like AI to accomplish. Kaveh says that all the time. It's not what AI can do, it's what AI should do, and that really defines the guidance.

For this next question, RP, I'm going to put you on the spot because AI is a very technology-driven endeavor, and we're bringing a maturity modeling approach. So given some of your experience from the past, why is a maturity model approach is the right approach to this problem that organizations are facing with the fear of missing out versus the resources spend and the value to get out of it?

Oh, that's a lot of things in there. For example, one of the topic we discussed in the last 30 minutes is AI is a software and systems engineering. I always say, all the rules of software engineering and systems engineering applies still.

I think if you go with that premises, and the way John mentioned in the model, we have -- what are the two typical aspects of implementation of any technology? One is the engineering aspect. Another one is people aspect, which is the change management aspect. As you go through the maturity adoption of implementation of the technology, your risk management, your change management, your expectations and business alignment continues to evolve, unchanged dynamically. That's the word that Anita was speaking, right? We need to get the dynamic inputs of implementation, both from the chain management aspect and engineering aspect of the technology that we implement.

And that's the critical reason why a maturity framework, I call it as a guidebook to move forward for enterprises, to look at something industry-relevant, business aligned and that covers all the aspects of the software engineering and people aspect to drive the implementation and adoption in a true sense. That's why the aspect of maturity models is critical for me.

And we've definitely seen the value as we went through some of our applications. So I'll take another question from the questions that came previously because it's very relevant to how we thought through the -- both the development as well as the implementation of the model.

And the question from Richard is, using your AI maturity model, how did you balance quick wins against long-term government staff capability building and sustainable institutional change? Is there a road map that can guarantee successful implementation while sustaining momentum?

First of all, nothing can guarantee outcome. If you're not a disciplined organization, that's a people problem. With that, John?

Well, I mean, so with respect to quick wins. The question, I think, is quick wins versus how do we know this is going to last? How do we know this is going to be sustainable, right? So within the model and the approach is the idea of context attributes, right, that, first of all, context attributes to me really means what are the inherent risks and what are the important things to the organization. Right?

If you're in a highly regulated organization, I spent a lot of time in finance, right, your approach to these areas might be a little bit different, right? So that, along with, frankly, an assessment, helps you identify your quick wins or the things that you need to do now or in the next 6 months, right? The long term is really where we get into like the maturity indicators and kind of going up to scale. How do you know that these practices are really going to be around in a year or 18 months and that you're actually going to be able to, we kind of say, emerging property, right? But that idea that you can just -- that you can do these things and you have to think a little bit less about it. That's what I would say about it.

For our purposes, quick win is not necessarily just check the boxes for the next practice. It's really prioritizing in terms of that road mapping approach. And it actually is very true. There's an audience comment, which I will share with my colleagues. AI is advancing rapidly, suggesting that models are also rapidly modernizing. In this scenario, the company has adopted AI. But if it doesn't keep evolving, it tends to [ allude ] competitiveness over time. Absolutely. And this is, in fact, some of the areas the capability is, in fact, focused on how you evolve and how you think about evolution, modernization and to be able to keep up with the space. So it's not a state in time, but it's really how you have practices to be able to continue to reinvent, as Majd says, as well with the evolving technology.

So I would like to make sure we spend some time talking about our early adopter experience. And I'm going to turn to Tony here because Accenture Global IT graciously stepped up, and we had to convince them that it was worth their time and allowed us to do our very first early adopter pilot with their organization. So Tony, why did Accenture Global IP step up and try the AI maturity assessment out? And what was the journey and what has happened since then?

Yes. So I think for many -- just like many other enterprises, we knew that generative AI technology was going to be part of our landscape. We know and knew the kinds of outcomes that we expected to get to. But like for many organizations, you have to figure out what path you're going to take to get there.

For us, the opportunity to participate in the research-based assessments seemed like an incredible opportunity to help us assess where we are and where we need to go. We found some of the strengths that we have in terms of like technical capabilities. We also identified some areas that we needed to focus on, like governance, value measurement, workforce management, workforce transformation.

And coming out of there, I think we are able to use the assessment based on the model to create a road map for us so that we know where we're going to go. And we're using that to help guide our future now, especially with like our workforce transformation and some of the work that we're doing.

And for me, it's pretty obvious. I talked to many other CIOs in my role. They're all facing sort of similar things, but I feel like we have a head start on that because of this assessment we took and because of the things that laid out and where we wanted to focus going forward. And that is driving our road map.

Thank you. So before I turn to Kaveh, I'm going to read the question so that there's a backdrop to this because I think this will resonate with as well. But it's -- so the question is, how do you distinguish between an organization that's exceptionally mature at automating all processes versus one that's mature at using AI to enable fundamentally new and establishing stakeholders?

And I think you see this a lot within the healthcare and some of those that you actually look at that.

Great question. It actually highlights in many ways, what effectively the maturity model highlights, if you go from less mature and more mature. Because in the beginning, you're largely probing, experimenting, learning, dropping AI into existing processes and seeking optimization. When you get to more mature, the organization has made a commitment to change what it does fundamentally to get a benefit that doesn't exist today. And for that, you have to make a strategic decision that, that's actually an objective.

And what's interesting in healthcare sector specifically is that healthcare organizations, particularly, let's -- I'm going to focus on delivery system rather than insurance, which is a little bit more financial in its orientation. At the end of the day, delivery system wants to do what it's always done. It wants to provide safe care, effective care, affordable care. It doesn't necessarily want to be in a different business.

And so some of the commitments to change that you'll often see an organization -- so let's -- an analogy might be if you're an automotive company and you want an autonomous vehicle, you're not getting there without AI because it doesn't exist. There is no healthcare analog to that right now. But the nature of that commitment doesn't actually exist. And therefore, the kinds of organizational changes that you need to do aren't really an imperative.

So what you'll see is a lot of organizations will use the maturity model, but once they realize what are the attributes of the more mature, they have to make a decision as to whether that really relates to where they're at. And I think it's important to not take the position that it's axiomatic that you must change. But to the extent that you have that as an outcome, you are going to have to change things that are very fundamental to your organization.

Another way to think of it is, if -- the quick wins thing is interesting. The way I think about it is most healthcare organizations. What they say by a quick win is I'm going to drop the technology, and 80% of my benefit will be tech and 20% will be changing processes. That's quick. But most of the things that happen to have in healthcare is 20% tech and 80% work. There is no quick win in that kind of a change. And that, I think, is really a sector-specific, problem-specific decision that has to be made.

And it's also -- I think we've observed in some of the specific assessments, the assessment, in a way, helps them make that hard choice, right, that...

It illustrates to them the choices that must be made, and these are strategic choices.

Yes. Exactly. And John, any other points to a from other early adopter experiences that we've had?

I thought I would talk a little bit about scoping, and we experienced this during another early adopter experience. Right? And we could talk for a long time about scoping. But it's important to think about the problem you're trying to solve and how you're going to apply the model, right? A lot of times, enterprise leaders want to apply a tool like this at the enterprise level. After all, they're responsible for the enterprise.

There are lots of different ways that can be used. You can do organizational unit. Of course, you can do an enterprise assessment. You can think about what is your maturity relative to a specific service that you're trying to support with AI.

One of our early adopter experiences was in a safety critical industry. And it was not enterprise. We essentially assessed and appraised 2 organizational units, both of which were adopting AI for the purpose of software development. And one of the -- some of the findings where we noticed differences in their understanding of risk and also their understanding of the corporate process around risk, they were doing a great job, but they were managing risk in slightly different ways that I think was kind of a surprise to the people who were involved. So it was useful.

Some of the things that we also observed through these experiences is if you are already using AI for your product development and have that from some of the traditional machine learning AI, you actually adopt a little easier because you know some of the uncertainty aspects. And that really speaks to existing discipline in software, cybersecurity and of the other aspects which we've seen in some of the early adopter programs.

So I'd like to take a couple of questions that we had come in because some of them are very interesting and also speak to the measurement and the challenge aspect. So this one is probably at the top of everybody's mind. How is token cost management factored into maturity? So any takers? Maybe I'll start with Tony because that's probably what keeps you awake at night that you don't share with us.

I was waiting for this question. I think that this is a great example of how you have to have maturity across your enterprise and as you move token economics overall and token cost management. And I think as you move from this world of experimentation to sort of visibility and control, you're going to have to have incredible discipline to understand the right places to implement this in your organization. You're going to have to have the discipline to be able to really do value measurement. And ultimately, this discipline is going to be required in order to build this into your overall budget, to your overall business strategy, how you price your services to what your costs are. And this ultimately is what you're going to use to calculate sort of the trade-off between cost and value for what's right for your business. And so my viewpoint in general is the discipline that you're going to get from having a strong model is what you're going to need to be able to do token economics throughout your enterprise to find the right value and the right amount of AI to use.

And John, maybe if we could answer from the perspective of the model, where we do focus on hard questions like that, that might help our audience?

Right. So just to be clear, you're not going to see a section in the model that says token costs. You're not going to see a section in the model that says maturity is less than a certain dollar amount. But what you will see when you read the model is there are a handful of practices and capability areas that very clearly address token cost and how to manage token cost.

We're looking at the ROI behind automating workflows, the monitoring and some of the technology infrastructure practices. There's a slice or a down select, or for people who are very familiar with models, model scoping, right, that gets you to the question of token cost. There's not a section that says token cost, but it's certainly in there.

Absolutely. And it's a combination of where you're focusing and how you're actually evolving these. So Tony, there is interest from our audience in terms of understanding why Accenture IT has stepped up. So I'll read the question, and maybe you could -- some of it might be a repetition, but let's make sure we address it. So the question is -- right, here is the question. Seems Accenture IT did an assessment against the model. Yes, they did. And what was the objective of the assessment? And was it done at the start and our current state to determine progress and how and where the results used and useful? You did somehow talk to this, but maybe if you could...

Yes. I mean, I can confirm, we absolutely did that early stages. Our goals were both to assess ourselves and to give feedback and because we think that there's real value. I did comment that we think it helped us understand what some of our strengths are. We do think it helped us understand some areas where we need to focus.

And so I want to make sure I get the question because I feel like I'm repeating myself. But we absolutely used it to lay out our road map for where we want to go because we think that it's hard to assess yourself, right? And it's hard to innovate from -- within your own company. This kind of assessment was very valuable for us to figure out and lay out the road map for where we want to go.

And also to emphasize there were dual goals there. One was to use this as an opportunity to see how the global IT was faring in their adoption journey, but it was also very important for the model team for the SEI and Accenture team to really see whether the model works. That was, in fact, one of the biggest goal, and we actually call it Pilot 0 because we got some feedback, for example, there are repetitive areas. Do we really need to spend as much time in those areas? And we folded that feedback into the model before we released the fabric, and there were -- and before we tried the actual early adopter assessments with actual organization. So there was that big goal in addition to the assessment goal as well. So it had multiple goals there.

And we will continue to do that.

And we will continue to do that because the technology evolves organizations evolve and our understanding of what AI adoption means evolve. In fact, when we started this journey, agentic AI was not what is agentic AI today. That's like -- and this is only we're talking about 12 months.

So we have a question, and Majd, maybe this is something that you may want to chime in on. The question is, is the company's maturity in adopting AI linked to the number of models or the complexity of the problem being addressed also? I think this is a very good question. What do you say?

Can you say that one more time?

So the question is, is the company's maturity in adopting AI linked to the number of models or the complexity of the problem being addressed?

Well, it definitely has to do with what the organization is trying to accomplish and what are the processes that enable the organization to get there. There are many things that the model gets at. And one of them is what is your -- what is your strategy towards AI adoption and AI transformation. Another one is do we have the right governance, do we have the right data, platforms and access to the models. Doesn't have to do with the number of models that you're currently running internally.

Of course, when you are managing a large number of models, then there's complexity in the economics and the budgets that you need to be able to roll them out. And you need to maintain them and you need to make sure that you have a constant flow of the data so that you can update these models and make sure that their accuracy is still relevant to you. One key component that maybe we didn't touch on is the workforce aspect of this. And the other one is the workflow aspect of this.

So in an organization, you're probably visiting all of your processes and saying, how do we reinvent these processes end to end. And based on how we reinvent these processes end to end, the work changes. And now as the work changes, what kind of workforce do I need to be able to deliver on this work.

And then -- so when the work evolves, based on all of the changes that you're thinking about, then you have to say, what are the roles inside my organization that I need to have? And then how do I train people for the roles that they are in? And as we've all been saying, evolution is ongoing. So this is not a one and done. This is something that will be constantly happening.

So the power of the model is in assessing where you are as an organization. What are the strategic bets that you want to make because they are low-hanging fruit for your organization to make and to develop the capability to say, we know how to do this. More importantly, after you've done some early wins, you can start to have some medium- and long-term plans as well. Those are things that you have to think about and plan for. And you have to think about these things from the core thinking of the model so that you do them in a good way.

So another thing that we don't want to forget, and I'm sure we're going to revisit this at closing, is this is an ongoing process for any organization as they're adopting AI. And so the model will help you recognize where you are, but this is an ongoing process that you will continue to assess, you'll continue to evaluate and you'll continue to evolve your processes, your governance, your people, just to make sure that you remain relevant and competitive in this market.

And for the number of models perspective, the word model is overloaded here. Number of generative AI models that you have within your organization are yours. This is, in fact, lock-in hedge betting that a lot of organizations are going to because their capabilities evolve so rapidly, but that is really depending on how you're actually mapping to your goals, as you said. So that's not necessarily the determinant.

I would like to get to as many audience questions as possible, and we have a number of them coming. But there is one that we also received early on from Stefan. And the question is, I work with Department of War programs. And many want more AI capability. Who doesn't want more AI capability? Especially large language models, but struggle to bring them into the environment where they can keep them AI gapped and isolated. Do you see this problem getting solved sooner than later? Any additional thoughts in this topic will be appreciated. John?

Well, I think the -- I mean, there are architectures where you can do that, involving cloud tenants and so forth to operate AI in -- it's not fundamentally a different problem in terms of architecture and knowing where your data is going. So there are resources out there to solve this problem. And the DoD DOW is developing those capabilities.

So we're headed there. I think there are some various administrative hurdles and things like that. But this is a solvable problem, I think. Both -- certainly in industry and certainly in the defense establishment as well.

Yes.

And we're already seeing that the DoW, they are building their own models. And they're able to deploy them, and these things are portable. And as John said, you can host them on your internal cloud infrastructure that is [ air gap ]. So definitely these things are evolving pretty rapidly and the DoW is leading the way there.

And in fact, we see in our engagements a lot of the organizations are trying to bring some of them within controlled environments to the extent possible to help with their software engineering and software development capabilities to provide them to their software developers, which is a usage scenario. They are still not looking at from the perspective of how does AI change the work, how does AI change some of the mission aspects, but we have initiatives within the Software Engineering Institute looking into those as well. So I think we'll see an acceleration as you mentioned, John, but there are different scenarios that are already happening.

Ipek, we're seeing a lot of programs apply things in -- well, like in dual environments. They'll use the traditional ways of doing things, but also start applying some of the new AI models. So we're trying -- we're seeing a lot of the dual ways of accomplishing the missions.

True. There's a share of that. I'll take audience -- another audience question. We have a number of interesting questions coming in. So the question is, what signs are expected that AI is indeed driving evolution within the company? It's a good one. Any want to take?

When you don't think about AI.

It's just the way that you do your business.

The way that you do your business. Any other?

I think if we can -- when an enterprise is seeing the positive trajectory of the business growth -- and like any technology in the past, if technology is industrialized, institutionalized in an environment, and this is the way people do it every day, like what Majd said. It is the way of working. And thereby, the exponential growth of business, when you see that, that means your technology is in action and the maturity has come in. And the spectrum of maturity changes, and that's the way of thinking. That's how I see it.

One other dimension is that there are either strategy or tactics that simply wouldn't be possible without AI. You couldn't remove it anymore. In many cases, when you ask that question to organizations before they -- in the early stages of maturity, you ask them if there's any part of their strategy that cannot be accomplished without AI. They'll often say, no, anything can be done. A, I would make it better, but I don't need it.

When you're on the other side of that, there are things the business won't exist without AI. It's a fundamental requirement of the decision that you've made, and I think that would be another way to know.

Yes. And a progression that is also shared in literature is whether you're augmenting, adapting and autonomously changing. With augmenting, you might be just increasing already existing automation but using AI, but as Kaveh mentioned and automation, existing automation is just with AI. With adapt, you maybe have a little bit more reliance, but with autonomous when this doesn't mean everything needs to be autonomous, but you actually have really changed the process steps and the floor to it. So that's another way that you could use as a...

Ipek, there's another thought there that if you have many AI pilots and many early wins, that means your organization is AI ready. And that is a misconception that we also have to address, right? So the fact that you have several champions or several AI projects that are currently running does not mean that the organization as a whole can achieve this natural progression of an idea all the way to production in the process that RP just described.

So we have to make sure that we differentiate between these two and not force ourselves to start to say that we are AI ready and we are an AI-first organization just because look at the number of projects we have going. You really have to think about all of the different dimensions of the model that Anita and John described.

And this is actually a very important point, Majd. And thanks for bringing that up because we do see and we did see a lot in terms of when we ask, for example, how do you measure success. Or is the number of people using whatever is your favorite AI-based tool. Or we have a number of use cases that are with AI, which is an aspect of the experimentation, but it is not adoption or it's not necessarily evolution.

We have a lot of very good questions. So there is 1 that we received early on, which I would like to focus that from there. And the question is, what section of the maturity model do you see as being highly important, but most widely missing in enterprises today? So I'll look at Kaveh, maybe?

No, I think the work in the workforce. I think that's -- because it's the hardest and it's often viewed as -- this is still viewed as a technology problem. At least that's my observation.

But Ipek, to build on Kaveh's point. It's a cultural problem also. So when you talk about workforce and people, you're talking about changing the culture as well.

So if I may amplify what was just said there. All organizations are going to have access to the best AI out there. So if you think by just enabling that capability inside your organization, you're going to be able to offer differentiated products and services, you're missing the point.

So it is the people that are going to help. So it's their ideas, their creativity, their approach, their strategy. So back to where we started and how you open, Ipek, it's what can your people do with this capability. You are integrating that capability within your organization, but then how do you unlock that what your people can do with it, and that's going to be what shows up in the market against your competitors.

Absolutely.

I would also the workflow and people aspect, absolutely the most important part of the model and the differentiator. Purely from a technology management perspective, a lot of the model content around ecosystem, right? How do you actually integrate with your legacy environment and scale is sort of in the upper part of the model or the "higher maturity," very important from how do you really scale this in the long term. I mean, without a doubt, it's people first. But there are technology management pieces of the model that I think will be very beneficial.

And in fact, if you're not building the right partnerships, you're not going to be able to succeed. And that is how the AI ecosystem is evolving, whether we like it or not.

I'd like to channel Tony here a little bit and say governance requires attention. It sometimes is seen as a burden, but it's really a major unlock if you have the right governance mechanisms in place. Tony, any thoughts on that?

Yes. I mean I agree, Majd. I mean what was resonating with me when you made those comments is like the easiest thing nowadays is just giving people the technology. And when you're really thinking about what's going to help enterprises being successful. I mean, you start with like did we achieve the outcomes that we wanted to achieve.

And then you go back and you can look at -- did the technology help us to get there. And if you don't enable the workforce and you don't do the things you guys were commenting on, you're just not going to get there. So you start with the outcomes, think about the workforce, how you're going to enable people, what's going to be differentiating, completely agree. And if you don't underpin that with governance, I don't think you could be successful.

Can I amplify 1 issue? That -- I think people don't realize until they're in the middle of it. The technology arm has a technology and has a data piece. And actually, we find a lot of the organizations, their data is not AI-ready at all.

And so I'll go healthcare specifically, we have all kinds of standards around transport and other attributes. But the problem with data for AI is that it needs to be accessible in a semantically and contextually useful way. And it is a myth that language models can solve that problem. And we're discovering that right now, and it's becoming a huge source of frustration.

There was a theory that you could take dirty data, throw an LLM at it, and it will work. That is absolutely not true. And because it's not true, all the data is sitting around not being useful, and you have to go back and think about this in a completely different way. And because the semantic standards that you need will never be solved by forcing the creators to create it in a standard way, there's a lot of heavy lifting that goes between what you have and what you need to actually ingest into any form of AI.

So there is an interesting question which selfishly, I would like to take because this is something that John and I and my colleagues have had endless conversations. The question is, there's local optimization, business units, individuals, and organizational optimization. From an organizational performance perspective, how does the AI maturity model measure this? Which is a scoping question. Right, John?

What's -- so it can do both.

Well, yes.

But there's a lot to be said for having a defined scope, right? And really thinking about -- I mean, let's say, you're a multinational or a nationwide company. Yes, you could do an enterprise assessment, and that can be very valuable. And frankly, all of these activities cost money at some level. So there's a certain sense to that.

But there's also a sense that says, I really want to have an exemplar within the organization. I really think this is the area where I should focus. So there's 2 ways to approach it. But I would -- I mean if you're in a large organization, I would really counsel people to think about that sort of model speak subcomponent piece of the organization that you really want to focus on. Because you can get things that are more actionable sometimes when you really focus on what you're trying to achieve.

And the context attributes aspect of the model also help you understand whether you're focusing on the business unit or if you need to focus on the enterprise, how do you need to approach that? That might require you to do the assessment with -- especially if you're a large global organization, you might need to do that with multiple business units to be able to get at enterprise level understanding, but the way we've set the model up is you could actually get an enterprise level understanding, especially of your gaps as easily as well. And I invite the person who asked the question to look at the booklet that we shared of the model and look into some of the experiences that we'll be sharing as we go on.

So we cannot talk about AI adoption without talking about humans. So the next question is something that we've received during the registration. So most everything we hear nowadays suggests humans are expandable and will become eventually AI's waste product. On the surface, AI CEOs are starting to backpedal on this messaging, but there's some truth to it. How does CMU SEI see this whole thing going? Where are the opportunities for human in the loop? We call it human in the lead. I guess that's Anita? I have to ask that to you.

That's a lot to unpack, but I did see RP cringe when you asked that question. Yes, I mean, I guess, the SEI's work really emphasizes human-centered AI engineering, not human replacement. And for example, humans aren't just approving AI outputs, they're codesigning guardrails, detecting drift, recalibrating systems that are in production. So there's a lot of different roles there that work in tandem.

SEI's research and decision explainability helps human spot bias, hallucination or mission drift before harm can occur. So that's also an important area of research. We've also talked about humans in the loop. It's -- they're feedback loops, and we can use that as training data. So it's not just oversight. It's continuous learning. Humans label edge cases, correct model assumptions and signal when the context has shifted.

So again, there's a lot of different and important roles that work in tandem. We also have some very active work active learning pipelines where there are domain experts that curate high-impact samples to retrain the models themselves. So making the loop efficient, not just human in the loop as a check box, but really making it an efficient part of the process.

So for us, some of our research is if you're building or deploying AI, you've got a design for fallback. How does the human take over if the AI fails or stalls or is ambiguous. That's sort of one area. Measuring human in the loop efficacy, not just is there a human, but how effective is their input. Is it real time, can they improve the models as well.

And then the other thing that I thought about was upscaling teams in AI literacy. So operators need to understand model limitations, not just to code the models, but to question and even interrogate them. So there's a lot of really important roles that have to work together. So the future isn't humans versus AI, it's humans plus AI.

Right. Majd?

One thing to add here is that if we're not careful with this overpush of just use AI as is, we are also pushing human beings at some point to relinquishing cognitive capability when they're utilizing AI. And while working with our CHRO last week here, we were thinking about how do you -- as you're training your workforce and as you're evolving your workforce -- RP, you were there -- we were discussing on how do you retain cognitive ownership as well.

So as you are training your workforce to leverage this capability inside the organization, you have to be principled and intentional about retaining the cognitive ability of your workforce and what value they bring into the discussion that they're participating and when they're leveraging AI. So a little bit of a caution tail here that this reckless abandon of quick to use the tools and then the wrong incentives to amplify the use of the tools without thinking about where do we want humans, as Anita mentioned. And we want to make sure that they retain their cognitive ability and don't start just providing an average of this is what AI produced, and that's the best that we can produce. To maintain agency over all of that is critical as we do this.

Right. So there's a joke in healthcare right now that says all -- any doctor that can be replaced by AI should be replaced. And that's a real statement about...

I like that.

You're not really worth your salt if you can be replaced by AI because you're not doing enough important work anyway. I think about that through the lens of low complex cognitive test, high complex cognitive tests. And in healthcare delivery, specifically, there's also physical test. To take a job, AI can only do parts of those things. And in certain sectors like healthcare, we have a raw shortage of high complex cognitive capabilities. I need to liberate as much of that as possible by taking low cognitive abilities and moving them somewhere so I can give somebody more time. That, by the way, is rate limited because a person can't work at peak cognitive capacity all the time.

We have another issue in healthcare, in particular, which is who's responsible for the liability associated with an error. So think about the question about a patient having a direct interaction with a bot for a diagnosis. If it's wrong, whose problem is it? The patient? Is the patient going to have the liability for that? Is the tech company? Guarantee you, neither of them are. As a society, we have no liability construct that doesn't include a learn-it-intermediary. And that will be a big journey that will have nothing to do with the AI companies for us to ever get to that stage.

And it's also important that tools are increasingly having the AI capable -- the features. So that doesn't mean all of a sudden tools are taking over as well. You really need to understand it from the human's responsibility perspective. And at the end of the day, AI is a tool that actually is an exciting tool.

Well, we're fast coming to the end of the time, this really flew by. So I would like to give our -- my guess, an opportunity to close, and I very much appreciate all the exciting conversation, we could go on and on. So as organizations move from experimentation to enterprise scale AI adoption, which is still a journey, not a destination that anyone really have effectively reached, what do you believe will become the most critical differentiator for long-term success? And what is one recommendation you would have for our listeners? And Kaveh, why don't we get started with you?

Yes. Actually, I think 3 of them. One of them is you have to get the why before the how. That's a strategic question because that drives everything. The second one we're seeing is the CEO has to personally be the loudest voice with the most leaning in effort. It's not a task you hand to anybody else. I've seen this repeatedly. If it's struggling and the CEO doesn't be -- doesn't wait, lean in and put their own personal time and effort, that's a big one.

And then the third one is recognizing that the -- that you don't gain the organizational benefit without changing the work. You can prove the AI works, but you don't get the benefit until you change the work.

John?

A CEO or a leader who is unafraid to actually ask the why question. And then this is a really old-fashioned answer, but human teamwork, right? The leaders in the organization really having honest conversations and teaming together, if you're very siloed, that's not going to -- bad idea, right? It's the human teamwork that's really going to drive the vision forward for the organization.

Majd?

I would say that we have to think about AI as a capability that we are building inside the organization. And then that has to be coupled with a culture of experimentation. And what does the culture of experimentation mean given that Tony is in the audience is that we have to make sure that the token economy takes into account these kinds of things.

And the culture of experimentation is budgeting for time and money for throwaways so that you can explore and experiment into what actually is going to stick and move the needle in the right direction. The last thing I'll say is this is never a one and done. This is an ongoing process. So the organization needs to be in that mindset that we will continuously update ourselves and we'll continuously disrupt ourselves so that we can continue to lead in terms of what we can accomplish.

Tony?

Yes. I think for me, it's organizations that are going to have a structured maturity approach as they figure out how to bring AI through the organization. And you have to have balance in that structure. You need to have experimentation. You need to understand your costs, you need to think about your security. And you need to then think about the outcome, the business outcomes that you're going to try to achieve.

And a structured approach will allow you to balance all of those things. We read a lot that experimentation is very important, but you have to move past experimentation at some point. So how do you balance all 4 of those things with a structured approach as I think the organizations that master that are the ones that are going to be successful.

RP?

I think the model is out there, like you said. And there are 2 critical components that we discuss today. One is organizational competency and then engineering competency. That kind of summarizes how an enterprise need to move forward in adopting AI. I think it is simple, scale and sustainable. We talked a lot about scalability. I think it is 1 step after scalability is how do I continuously sustain the business value and the AI implementation that I put in. So I would like to see all the enterprises move from scale to sustaining the business value by leveraging organizational competencies and engineering competencies.

I also want to thank SEI team for partnering this with building the model, bringing our expertise. I think as we move forward, there's a lot to learn. We can continue to help enterprises to get trained on the model, get more expertise in the model. We can help enterprises to baseline their maturity processes and working along with the SEI to build the road map.

So there's a lot we can do. We can contribute to the industry as we move forward, and we will continue to get the feedback from the field and strengthen the model and organizational and engineering competencies, thereby we can scale and then such day. Thank you.

Thank you. And Anita, bring us home.

Okay. So I agree with all of mym colleagues. But I think I -- when you had talked about misconceptions very early on, I'll go back very quickly to that, that, I think, long-term success has to be around organizational alignment around it on AI as a strategic capability, not only a tech initiative. But everyone here talked about the learning organization. So not only do organizations have to be learning about how to adopt AI effectively, efficiently and with the AI adoption maturity model as a road map, but I think even for us as the general community to share data, to share experiences, so we can be a learning AI adoption maturity model community. So we can feed that back in to the road map and make it current and based on what actually works out in the community.

And in fact, the successes of the past in using maturity approaches have been defining practice in a consistent way across organizations globally, and that's our hope is with this AI adoption, which is model up. Well, thank you to all of you. And it's only through collective experiences like these and collaborations between industry, government and academia will actually be able to solve this AI problem because the technology moves fast. It's exciting and it's scary all at the same time.

And our goal with the AI adoption maturity model is to help serve that purpose and shape some of the broader practices to come along. We -- while this is the first phase, established the maturity model, which you are able to read today, the second phase will include building the empirical data and the basis for different kinds of use cases. We will continue to share our experiences using the model as well as some of the learnings, and we'll fold it back into the evolution of the model. And we'll expect these experiences to provide critical insights to all of us as we understand what AI adoption really means.

There were questions there, which we couldn't get to, for example. Well, there are tools out there that change by the minute. How do you give guidance to tool usage? We've talked about whether scaling a particular tool really means maturity. So all of these questions are valid questions and our goal in developing the practices and the capability area as well that you would actually find answers as you're developing your AI strategy as you go through the AI adoption experience and define your organizational or team or business unit goals.

And we extend our deepest gratitude to everyone who contributed. There are the faces you see, but there are teams behind us, and we appreciate everybody's contribution. But also, we appreciate the contribution of our early adopters who trusted us with their problems, challenges and their data and everyone who talked to us and who shared their experiences and data with us along the way. And we're looking forward to working together with Accenture and all the partners and our collaborators. And we'll be sharing our findings and the data, and we welcome your feedback as well.

Anita, John, Majd, Kaveh, RP and Tony, thank you. I know I've spent those 90 minutes with the busiest people I know. So really, really appreciate it, and I know our audience appreciate it as well. So at the end of the webinar, we ask you that you complete our survey and the link will be posted on the YouTube chat area now. And we appreciate your feedback now or later. You can send all your questions to [email protected], and you'll be able to get the links to the references that we made throughout the conversation.

Thank you for spending your last 90 minutes with us, and looking forward to the conversation as we all understand what AI can do for our organizations for our workflows and make our lives better without introducing the risks. Thank you, everyone.

Good morning. Thank you for standing by. Welcome to Accenture's Second Quarter Fiscal 2026 Conference Call. [Operator Instructions] As a reminder, this conference is being recorded.

I would now like to turn the conference over to Alexia Quadrani, Managing Director and Head of Investor Relations. Please go ahead.

Thank you, operator, and thanks, everyone, for joining us today on our second quarter 2026 earnings announcement. As the operator just mentioned, I'm Alexia Quadrani, Executive Director and Head of Investor Relations. On today's call, you will hear from Julie Sweet, our Chair and Chief Executive Officer; and Angie Park, our Chief Financial Officer. We hope you've had an opportunity to review the news release we issued a short time ago.

Let me quickly outline the agenda for today's call. Julie will begin an overview of our results. Angie will take you through the financial details, including the income statement and balance sheet, along with some key operational metrics for the second quarter. Julie will then provide a brief update on our market positioning before Angie provides our business outlook for the third quarter and full fiscal year 2026. We will then take your questions before Julie provides a wrap-up at the end of the call.

Some of the matters we'll discuss on this call, including our business outlook, are forward-looking and as such, are subject to known and unknown risks and uncertainties, including, but not limited to, those factors set forth in today's news release and discussed in our annual report on Form 10-K and the quarterly reports on Form 10-Q and other SEC filings. These risks and uncertainties could cause actual results to differ materially from those expressed on this call.

During our call today, we will reference certain non-GAAP financial measures, which we believe provide useful information for investors. We include reconciliations from non-GAAP financial measures where appropriate to GAAP in our news release or in the Investor Relations section of our website at accenture.com. As always, Accenture assumes no obligation to update the information presented on this call.

Now let me turn the call over to Julie.

Thank you, Alexia, and everyone joining us this morning, and thank you to our more than 786,000 people for your extraordinary work. We delivered another strong quarter with $18 billion of revenue, growing 4% in local currency and once again taking significant market share. We had record bookings of $22.1 billion, bringing H1 bookings to a total of $43 billion. We had a record 41 clients with quarterly bookings greater than $100 million, bringing us to 74 of these bookings in the first half, 12 more than this time last year, demonstrating the continued demand for reinvention at scale.

We delivered 30 basis points of operating margin expansion with strong EPS growth year-over-year, generating significant free cash flow while investing significantly in our business. We closed 3 strategic acquisitions, deploying $1.6 billion of capital, and we now expect to deploy $5 billion in acquisitions this year with capacity to do more for the right opportunities.

In double cooking on our revenue. Our revenue growth was broad-based across geographic markets and types of work. Revenue from our top 10 ecosystem partners continues to outpace our overall growth, and we are expanding these partnerships. And we are on track in FY '26 to more than double our bookings over FY '25 from partnerships with our key emerging AI and data ecosystem partners. And we delivered these strong results through the disciplined execution of our growth strategy as our market remains roughly the same.

Our long-term growth strategy is to help our clients reinvent and to capture other new opportunities created by AI. To accelerate this strategy, we are using 2 key competitive advantages: our strong balance sheet and our long history of successful acquisitions. Our goal with acquisitions is to more rapidly expand into higher growth areas with attractive margins, which will fuel organic growth and increasingly help us grow non-FTE-related revenue. In H1, we invested in 4 areas: first, AI powered transformation. Last week, we closed the acquisition of Faculty, a leading U.K.-based AI native services company with the Decision Intelligence product business that provides a platform for us to expand into new areas of unmet AI demand with non-FTE revenue.

We also acquired 2 companies to accelerate our growth with Palantir, an emerging ecosystem partner. Decho in the U.K., which focuses particularly in defense and public sector markets and RANGR Data in the U.S., which works across industries. Second, AI enablers. AI enablers include data centers, cybersecurity, energy infrastructure and data. We acquired a 65% stake in DLB Associates, a data center engineering and consulting firm with high double-digit growth. We also acquired CyberCX, a leader in cybersecurity in Australia, and we announced the acquisition of Ookla, a global leader in network intelligence, competitive benchmarking and customer experience analytics. Ookla, with only 430 employees, generated $231 million of revenue in their calendar year 2025 through non-FTE subscription and licensing revenue models and an 8% year-over-year growth rate and with healthy margins accretive to Accenture.

Third, high-growth secular trends. These trends include capital projects, defense and public sector and education around AI, data and tech. We acquired Orlade Group, a French capital projects firm, which expands our presence in the energy, utilities, rail and aerospace sectors including nuclear power plants and power grids. This acquisition is also part of our focus on key AI enablers because AI requires significant expansion of energy infrastructure.

We also expanded LearnVantage, our business is capturing the education opportunity through the acquisition of Aidemy in Japan, a portion of our LearnVantage business, leveraging our proprietary platforms, operate with a non-FTE commercial model growing double digits. Finally, mid-market expansion. We made 2 mid-market acquisitions, NeuraFlash and Total eBiz Solutions and announced [indiscernible] to expand our presence in the mid-market, where we are experiencing higher revenue growth and a higher volume of smaller deal sizes that convert to revenue faster.

As we delivered this quarter, we also are executing at speed on our talent strategy for the age of AI. We now have over 85,000 AI and data professionals already exceeding our goal of 80,000 professionals by the end of fiscal 2026. Thanks to our intentional talent strategy, we will hire more entry-level reinventors in FY '26 than FY '25, which is important for our financial model, just as our clients must reinvent so must Accenture. Our inventors completed 13 million training hours this quarter alone and 192,000 completed our agentic AI fundamentals program, co-created with Stanford's Institute for human-centered AI. After significant investment in training starting this year, we have made the use of the AI tools and contributions to helping Accenture become the most AI-enabled company in the world, now a formal part of our performance evaluation.

Finally, we are pleased at the number of external recognitions of our broad-based strength that we have received in the last several months. Please check out these recognitions in our earnings presentation for the quarter.

Over to you, Angie.

Thank you, Julie, and thanks to all of you for joining us on today's call. We were very pleased with our second quarter results with record bookings for the quarter, revenue at the top end of our guided range, strong margin expansion and robust free cash flow. These results demonstrate the resilience and durability of our business and continued execution of our strategy to be the reinvention partner for our clients. We delivered these results while continuing to invest for long-term market leadership and returning significant cash to shareholders.

Now let me summarize a few highlights for the quarter. Revenues grew 4% in local currency and were broad-based across geographic markets and types of work, reflecting the diversity of our business as we continue to take market share. Operating margin was 13.8%, an increase of 30 basis points compared to Q2 results last year and continues to include significant investments in our business and our people.

We delivered EPS in the quarter of $2.93, which represents 4% growth compared to EPS last year. And finally, we delivered strong free cash flow of $3.7 billion and returned $2.7 billion to shareholders through repurchases and dividends this quarter. We also invested $1.6 billion primarily attributed to 3 acquisitions in the quarter.

With those high-level comments, let me turn to some of the details starting with new bookings. New bookings were a record $22.1 billion for the quarter, representing 6% growth in U.S. dollars and 1% growth in local currency, with an overall book-to-bill of 1.2. Consulting bookings were $11.3 billion with a book-to-bill of 1.3. Managed Services bookings were $10.8 billion with book-to-bill of 1.2.

Within bookings, the percentage of our work, which is fixed price continues to increase over 60% in FY '25, this reflects the rising importance of our proprietary platforms and clients' need for cost and delivery certainty where our scale, experience and financial strength matter.

Turning now to revenues. Revenues for the quarter were $18 billion, reflecting an 8% increase in U.S. dollars and 4% in local currency at the top end of our FX-adjusted guided range as the foreign exchange impact for the quarter was positive 4.4% compared with a positive 3.5% estimate provided last quarter. Consulting revenues for the quarter were $8.9 billion, up 7% in U.S. dollars and 3% in local currency. Managed Services revenues were $9.2 billion up 10% in U.S. dollars and 5% in local currency, driven by mid-single-digit growth in technology managed services, which include application managed services and infrastructure managed services and high single-digit growth in operations.

Turning to our geographic markets. In the Americas, revenues grew 3% in local currency, led by growth in banking and capital markets, software and platforms and industrials, partially offset by a decline in public service, driven by our U.S. Federal business. Revenue growth was driven by the United States. Excluding the 2% impact from our federal business, Americas grew approximately 6% in local currency in the quarter. In EMEA, we delivered 2% growth in local currency, driven by growth in insurance, life sciences and public service. Revenue growth was driven by the United Kingdom and Italy.

In Asia Pacific, revenue grew 10% in local currency, driven by growth in banking and capital markets, communications and media and public service. Revenue growth was led by Japan and Australia.

Moving down the income statement. Gross margin for the quarter was 30.3% compared with 29.9% for the same period last year. Sales and marketing expense for the quarter was 9.7% compared with 10.1% for the second quarter last year. General and administrative expense was 6.7% compared to 6.3% for the same quarter last year. Operating income was $2.5 billion in the second quarter, reflecting a 13.8% operating margin, up 30 basis points compared with results in Q2 of last year.

Our effective tax rate for the quarter was 24.3% compared with an effective tax rate of 20.4% for the second quarter last year. Diluted earnings per share were $2.93 compared with diluted earnings per share of $2.82 in the second quarter last year, reflecting 4% growth. Days services outstanding were 46 days compared to 51 days last quarter and 48 days in the second quarter of last year.

Free cash flow for the quarter was $3.7 billion, driven by cash generated by operating activities of $3.8 billion, net of property and equipment additions of $150 million. Our cash balance at February 28 was $9.4 billion compared with $11.5 billion at August 31 with regards to our ongoing objective to return cash to shareholders. In the second quarter, we accelerated our share buybacks and repurchased or redeemed 6.8 million shares for $1.7 billion at an average price of $246.09 per share. This brings our year-to-date total to $4 billion in repurchase or redeemed shares, which is a significant step up from the same time last year. Also in February, we paid our second quarterly cash dividend of $1.63 per share, a 10% increase over last year for a total of $1 billion.

And now back to you, Julie.

Thank you, Angie. I will start with the demand environment and then turn to why we see AI as a tailwind, which we believe will shape our growth over the next few years. We saw again this quarter clients continuing to prioritize their most strategic and large-scale transformational programs, which positions us in the center of their reinvention agendas. As clients finalize their budgets going into calendar year 2026, we are seeing spending similar to 2025. Demand continues to be driven by a few major themes. First, clients are implementing foundational programs with our ecosystem partners to capture the full opportunity of AI. These typically involve cloud, security and data modernization, often combined with operating model and talent transformation.

We continue to see at least 1 out of every 2 advanced AI projects lead to a data project. Second, clients continue to look to reinvent faster, leverage our proprietary platforms and expertise and achieve greater efficiencies through managed services across the enterprise, and we see clients working with us to create more investment capacity to increase their spend in new areas. And third, clients with more advanced digital cores are starting to take on larger AI programs. We also are seeing more moving from proof of concept to production, while others are still at the beginning of their journey with another 100 clients or so initiating advanced AI projects with us this quarter.

Across many of these programs, AI and data are now central, sometimes as the destination and increasingly as part of the work from day 1. A good example of these demand trends is how we are partnering with the Estee Lauder Companies, a global prestige beauty company to advance its new one operating ecosystem and to drive a more connected, scalable and consumer-centric enterprise. Enabled by our platforms, we will collaborate with the company to transform how work gets done, leveraging AI and automation across the end-to-end value chain. Over time, this is designed to accelerate execution and enable teams to focus on driving innovation, consumer experience and brand desirability. This work supports the Estee Lauder companies and building the capabilities needed to activate new ways of working and aligning teams, technology and partners to enable the business to operate with greater agility.

We see AI as a tailwind because it's helping us win more today and take market share, and it is creating new opportunities for growth over time. We're continuing to take market share quarter after quarter because of the combination of our early leadership in advanced AI, our deep ecosystem partnerships with both established leaders and emerging players and our decades of investments to be both deep and tech and relevant across the entire enterprise from the back office to core operations to the front office.

We play a critical role in the AI ecosystem. Foundation models provide the intelligence and our role is helping clients understand what to deploy and when, how to integrate it into their systems, reimagine their processes, modernize their data and digital core help redesign their operating models and do effective change management and help build the capabilities and talent needed to scale across the enterprise.

As the technology changes even more quickly, our clients are turning us to help them navigate. They also want us to help them go faster, sometimes by building their capabilities and other times by leveraging ours. Take SaaS implementations overall. Clients are continuing to modernize their tech stacks with SaaS but they're now asking that new SaaS implementations be designed from day 1 to use processes that integrate both AI from the SaaS provider and other providers and clients are more and more willing to do end-to-end transformation, all of which requires leadership in AI and SaaS, but it also requires the industry and functional knowledge and the ability to work across the enterprise, not just in certain functions.

They look to us to bring our point of view and experience on what the new tech stack should be as well as on how their processes should change due to AI. We're seeing this across industries and across functions. For example, in retail, service is no longer just about fixing problems, customers expect a consistent experience every time even on a massive scale. We're working with retailers to reinvent contact centers and design the new processes with agentic AI. This means partnering with us to develop agentic enterprise strategies and architecture foundations which will serve as a platform to enable agentic solutions.

We aren't simply upgrading contact center technology, we're reimaging service at scale. The future workflow involves digital agents and human agents operating as one coordinated team to serve customers. We're starting to bring together the best of major technology ecosystems from day 1, such as a SaaS player and a hyperscaler and creating agents that work across both platforms, creating leading-edge, customized solutions. This simplifies work for employees, delivers faster and more personalized support for customers and creates a service engine designed to scale efficiently as the business grows.

Our combination of strength is helping us meet the needs of our clients today and AI is opening up new opportunities where these strengths position us for growth in the long term. We see a long funnel of work as we have the advantage of the biggest client base in our industry, all of which will need to reinvent.

Let's look at ERP. We have been the #1 partner to all the major ERP ecosystem partners for years. And over the last several years, we've deployed modern ERP systems across hundreds of clients. When those systems were implemented, advanced AI did not yet exist. Now those clients want to embed the new AI and data capabilities and transform their end-to-end processes. For example, with one of our largest oil and gas clients, we're seeing a clear pattern. First, we modernize their digital core. Over several years, we partnered on a major ERP transformation to implement a cloud-based platform that simplifies operations, standardizes processes and creates a single source of data across the enterprise. It was a significant multiyear investment. Now with that foundation in place, they're investing again, embedding AI directly into the systems that run the business. This is not a separate layer of technology. Its intelligence built into core workflows across finance, supply chain, asset maintenance and field operations. These capabilities analyze large volumes of data initiate routine actions and support better decisions in real time.

The impact is tangible, faster cycle times, fewer manual steps, lower operating costs and stronger operational resilience. We're beginning to see the same sequence more broadly, modernization of the core followed by AI-driven enhancement, enterprise systems are becoming the platform that allows AI to deliver value at scale. Because of the work required across every process and every industry, we are starting with early leaders who have advanced technology stacks and want to pioneer. We believe that over the next 12 months or so, this opportunity will gain momentum.

AI is helping us grow another strong business, cybersecurity. We see advanced AI as a catalyst to our cybersecurity business as the threat landscape expands and new tools emerge to protect and attack. AI is also unlocking opportunities in technology modernization, one of our key strengths. For example, for decades, parts of the technology stack like the mainframe have been considered too complex or too costly to modernize. Today, advanced AI and new hardware capabilities are making mainframe modernization feasible, which we believe will open as major services market.

We are also seeing significant opportunities in core operations where AI is enabling us to make today's impossible possible. This is one of the reasons why our custom systems integration work has been having a renaissance and is showing strong momentum. Core operations are where generally they're not as many SaaS providers because the needs are complex and industry specific. These are areas where a lot of digitizing still needs to happen, like moving to the cloud and building data foundations, but where advanced AI is going to be able to provide solutions that are not available to clients today or are too expensive. Examples include finance and risk, such as know your customer and banking, claims and insurance prior authorization in health care and manufacturing, to name just a few.

Advanced AI also is opening entirely new areas of growth for us because it is creating new opportunities for our clients and because making AI work requires entirely new capabilities. Take customer engagement. In Accenture Song, LLMs are driving the biggest revolution in retail since the advent of social media. While it is still very early, conversational and agentic commerce are changing how customers discover, evaluate and purchase products. We are seeing strong demand, and we are uniquely positioned to serve our clients because of our ecosystem relationships and expertise in marketing, sales and service.

Radisson Hotel Group, a global hospitality company is a clear example of marketing and commerce reinvention. Over the past several years, we modernized their digital and data foundation, uniting brands on a single global platform. This created a real-time 360-degree customer view to enable personalized marketing across more than 1,500 hotels and over 30 languages in markets. Now we're putting agentic AI at the center of how demand is created and converted with Accenture Media console where AI agents optimize content based on traveler intent, streamlined campaigns and dynamically allocate budgets.

We're also helping Radisson adapt to how Discovery is evolving with agentic commerce, connecting live inventory and rates directly into conversational platform so travelers can move seamlessly from discovery to bookings with an AI-driven journeys. Since our collaboration began, Radisson share of its direct bookings has tripled. This isn't incremental marketing improvement. It's AI-driven commerce and it shows how each wave of technology reshapes industries like travel, opening new growth opportunities for our clients, and therefore, for Accenture.

Our clients also are turning to us to help them build and provide the capabilities to use AI across the enterprise. For example, in one of our large clients, we're working with a leading language model provider to embed advanced models directly into how the company's IT team delivers their projects. Those models are accelerating software work across the board, new development to upgrades and some projects are already seeing delivery move 50% faster. Beyond engineering, the same models are automating complex documents, enabling real-time reporting and powering AI agents embedded directly into day-to-day business processes.

And for developers and next-generation agentic coding platform is changing how work gets done with teams building their own tools and AI agents at scale. This is what reinvention looks like, AI becoming central to how work is designed, delivered and run. We also have entirely new offerings, such as setting up dedicated AI services to either be their main execution arm sometimes in a build-operate-transfer model or to be like a factory augmenting a client's core AI team.

This quarter, Piraeus Bank SA and major bank in Greece partnered with us to set up a central AI hub to be their primary execution arm with an option to transfer to them in the future. They are leveraging our strong capabilities to help them more quickly capture value from AI and navigate the complex ecosystem environment.

Before I turn it over to Angie, I want to step back and give you our perspective on the future. AI as it stands right now, may turn out to be the most powerful technology breakthrough since electricity. We cannot even today describe all the ways in which we will use this technology, let alone the opportunities to come. But the one thing we know is the only way to realize the power of this technology is if companies can change dramatically to use it. And through every prior technology evolution in the last 5 decades, 1 constant has been that the companies have turned to Accenture to help them make these big changes, and that is why I am so confident in our future.

Over to you, Angie.

Thanks, Julie. Before I get into our business outlook, I want to share how the conflict in the Middle East is affecting our business and how we are reflecting it in our guidance. First, we have roughly 3,000 colleagues in the Middle East, a region which represented about 1% or $1 billion of revenue in FY '25. Our colleagues are safe, and we are providing them with all the support we can. Currently, we are not seeing any significant financial impact. While we know the environment is more uncertain given the conflict, we always call it like we see it. And based upon the information we have today, we are increasing key elements of our full year guidance.

Our range for Q3 and the full year reflect our best view today of the potential impact of the conflict in H2. It does not take into account a significant escalation or the occurrence of major economic disruption.

Now let me turn to our business outlook. For the third quarter of fiscal '26, we expect revenues to be in the range of $18.35 billion to $19 billion. This assumes the impact of FX will be approximately positive 2.5% compared to the third quarter of fiscal '25. Our Q3 guidance reflects an estimated 1% to 5% growth in local currency, including about a 1% impact from our federal business. Excluding the impact of federal, our revenue is expected to be an estimated 2% to 6%.

For the full fiscal '26, based upon how rates have been trending over the last few weeks, we continue to assume the impact of FX on our results in U.S. dollars will be approximately positive 2% compared to fiscal '25. For the full fiscal '26, we now expect revenues to be in the range of 3% to 5% growth in local currency over fiscal '25, including an estimated 1% impact from our federal business. Excluding the impact of federal, our revenue is expected to be an estimated 4% to 6%.

This year, we continue to expect an inorganic contribution of about 1.5%. We have a strong pipeline of opportunities and now expect to invest about $5 billion in acquisitions this fiscal year. But as Julie said, we could do more if the opportunities present themselves.

For adjusted operating margin, we continue to expect fiscal year '26 to be 15.7% to 15.9%, a 10 to 30 basis point expansion over adjusted fiscal '25 results. We continue to expect our annual adjusted effective tax rate to be in the range of 23.5% to 25.5%. This compares to an adjusted effective tax rate of 23.6% in fiscal '25. We now expect our full year adjusted diluted earnings per share for fiscal '26 to be in the range of $13.65 to $13.90 or 6% to 8% growth over adjusted fiscal '25 results.

For the full fiscal '26, we now expect operating cash flow to be in the range of $11.5 billion to $12.2 billion, property and equipment additions to now be approximately $700 million. We are raising our free cash flow guidance by $1 billion and now expect free cash flow to be in the range of $10.8 billion to $11.5 billion. Our free cash flow guidance reflects a very strong free cash flow to net income ratio of 1.3.

We continue to expect to return at least $9.3 billion through dividends and share repurchases, an increase of $1 billion or 12% from fiscal '25 as we remain committed to returning a substantial portion of our cash generated to our shareholders. Our Board of Directors declared a quarterly cash dividend of $1.63 per share to be paid on May 15, a 10% increase over last year.

As we move into the second half of the year, we remain focused on executing our strategy, investing for the future and managing our business with rigor and discipline.

With that, let's open it up so that we can take your questions. Alexia?

Thanks, Angie. [Operator Instructions] Operator, would you provide instructions for those on the call, please?

[Operator Instructions] And today's first question comes from Jason Kupferberg with Wells Fargo.

Definitely appreciate all the AI-related commentary and the client examples. What kind of quantitative evidence should investors be looking at to help substantiate the view that Accenture is a net beneficiary of AI?

Thanks, Jason. I would just start with that at this point in our business, AI is permeating everything we do because it either is driving why clients are actually doing things like moving to the cloud. But when we're doing something that isn't specific AI, they are looking at our AI credentials because everything is aimed to get to AI. And then, of course, we have direct AI. And then our managed services business is being evaluated by how good our platforms are and their expectations of building AI.

And so like to start with like your first kind of way of looking at is how is our business performing relative to everyone else. And are we taking market share, right? That is the -- because at this point, it's not isolated, right? It really is why we're winning, and it's -- you have to have it to win -- you have to be a leader to win at the levels we're winning of like $22 billion.

And then we're going to give you metrics, Jason, over time, that will change to kind of tell you. And so today, we look at market share. We look at our overall growth. And then the metrics we're giving you are the ones we're using, which is because everything is so tied to the big ecosystem is our growth with that ecosystem outpacing overall growth? And then how are we doing with the emerging players.

And then we are looking at how many companies are initiating AI with us among our client base, which are the metrics we gave you today. So the metrics will change, right, but they reflect what we're looking at as we drive our business.

Understood. Understood. And maybe just one on the numbers. I mean the consulting bookings growth has been accelerating in the past few quarters. And then just looking at the modestly upsized revenue outlook for the year. I mean even if you only deliver the middle of the Q3 range in constant currency, I think you'll be at 4% year-to-date, you've obviously got the easier compare in the fourth quarter when you lap the U.S. federal headwind. So any reason to not think the upper part of the full year, 3% to 5% range is a pretty plausible outcome. I mean, unless obviously, if there's some major economic disruption from the Middle East.

Jason, the 3% to 5% guide for the full year, which is really 4% to 6%, excluding federal, is our best view based upon what we see today. We had bookings that were really, really strong at $22 billion, which were actually a record for us this quarter in our third consecutive quarter have $20 billion of bookings or more. And so for us, our guided range, we -- we do aim for the top. We'll see how things play out, but it's our best view. And you're right, we will anniversary AFS in the fourth quarter of the headwind from that, and we expect that to grow in the fourth quarter.

And our next question comes from Tien-Tsin Huang with JPMorgan.

Nice results here, especially on the free cash flow. I'll also ask on AI if that's okay. Julie, appreciate your comments there on why it's a tailwind, but I was just thinking with these frontier models that are improving so quickly it's driving a lot of news flow and a lot of debate. And are you seeing any correlation or are you tracking this how these models and how they're improving and their capabilities improving and how that might impact your bookings growth and conversion to revenue? I'm just trying to understand if there's some kind of correlation or pattern and how that might impact your numbers here going forward as the frontier models improve?

Thanks, Tien-Tsin. It's a great question. I think it goes to the heart of kind of what's different with models which is when you release functionality in a packaged solution that we've seen in the past, right, is the models are basically just a super powerful engine. So if you think about the car, right, you've got this great engine only if it's connected to everything. If it has wheels so you can actually make it run and the transmission to guard it.

And so when the models come out, there isn't a direct correlation to bookings or new work but what it does is create the next opportunity for us to look at what are the solutions that it's going to now create. And so if you think about in earlier days, a lot of the work was focused on things like amortization and content creation the better the models are, it's able to fuel things like moving into agentic where -- and we're starting to see that. So we're starting to see more experimentation and use of agents really workflows as the models get better.

So think of the release of the models as the beginning of creating new opportunities for us to take to our clients even as, right? There's a lot of work that we have -- that we're doing based on everything that has already happened. Does that help?

No. It really helps like analogy, and it's insightful. So -- maybe as my follow-up, just I get this question a lot, Julie and Angie, just thinking about the large deal you've won a lot that trend continues. But with respect to AI, how would you characterize the mix of advanced AI work between growth or revenue-generating use cases against the efficiency-led use cases, we hear a little bit of a pickup on the growth side, but I'd love to hear what you're seeing on the ground there on the mix shift?

Yes. So I think the first shift that's happening is the focus. It is not yet in the mix. So our latest survey that we do every quarter of the C-suite and how their view of AI. The latest survey had 78% now saying we think growth is going to be the biggest value. That's not yet translating on the ground to being the biggest driver mostly because of where the technology is. If you think about kind of the early days, a lot of it is about content, summarization, et cetera, that is really an efficiency play.

And as the capabilities improve, you start to see more ability to take it into the core business and to do more complex work. So we are absolutely seeing an uptick in growth, growth focused AI programs, but efficiency is still leading the way. I will tell you that the most exciting area right now on growth is conversational agentic commerce, demand is surging there. And I think as that -- and that's what we're investing a lot. I think as that takes off, you're going to start to see real results from -- on the growth side from these new developments. And that's a whole new market. And it's a whole new opportunity for us that we're super well positioned, of course, because of Song.

And our next question today comes from Kevin McVeigh at UBS.

Congratulations on the results. If I heard right, it sounds like the acquisitions for -- will be upwards of $5 billion. I think that was from $3 billion last quarter. Is that right? And then it doesn't seem like it's translating to the inorganic growth. So is that just the timing of the contribution from the inorganic? Is that just the timing of when they -- when those acquisitions come in?

Kevin, that's exactly right. So we currently see $5 billion and have the potential to do more, based upon the opportunities that are -- become available. In terms of the inorganic contribution, we still expect about 1.5%, and that really is on timing.

That's terrific. And then just the expanding AI -- the expanding bookings with the newer partners you have, is there any way to think about how that is relative to kind of the existing pool and how that scales over time? Because it seems like they're on pace to double, which is terrific. Just any way to dimensionalize that? And I guess, the new ones, I mean, with Anthropic data bricks and NVIDIA so on and so forth?

In terms of dimensionalizing it, what we're seeing is that really across the board, we've got really strong growth, both with our large ecosystem partners and with the emerging partners. And the way to think about that is that the -- as the -- in a little bit what I was talking about earlier is the models are improving the kind of ability to deploy them in the enterprise expands, right? So think about we're now doing work like in know your customer in the KYC area in banking because we don't have great package solutions of software there. As the models get better, it solves some of the problems that are there.

You're seeing in things like the mainframe is the models are helping us do the really not fund dirty work of converting code, we are now able to kind of open up that market where clients are now going to be more willing to touch the mainframe. So I would really think about both the large ecosystem partners and the emerging partners together because all of these solutions are really working across the ecosystem.

And our next question today comes from Darrin Peller at Wolfe Research.

All right. Can you just touch on your head count growth expectations? And maybe just higher level, your headcount strategy, has there been any change in linearity that maybe you're seeing, particularly related to AI, but more broadly in the environment right now would be great.

Darrin, so for us head count, we expect our headcount to increase in the second quarter based upon the demand that we see. And really, this is a continuation of the talent rotation that we discussed at the beginning of the fiscal year, and we expect our headcount to -- we expect to add headcount in the second quarter -- in the second half.

Okay. And guys, just when we think about linearity and how that's trending, given AI and implementation for either your own use cases or customers. I'm just curious if it's impacting the strategy going forward?

By linearity, if you mean the sort of revenue and headcount, I just would remind you that we really have not had a linear relationship since around 2015 when RPA when automation really came in. And so we would expect to continue to believe that disconnecting. And that's what's baked into our guidance.

Okay. All right. Just one more quick one is on visibility. Just sitting where we are today versus perhaps this time last year, how do you think about your visibility and confidence in the remainder of this year and even the next 12 months, just given all the conversations, are there any more uncertainties in clients now given AI or anything else for that matter, that geopolitics, et cetera?

Well, what I would just say is that, obviously, there's a lot of uncertainty right now in the environment which we're not baking into our guidance. But our guidance reflects our confidence in what we are seeing at the account. And we haven't seen -- at our clients, and we haven't seen anything yet being impacted by the war. But obviously, there is a big uncertainty because of the war, but we're very confident based on the information we have right now, on what we see for the next 2 quarters.

And that's why we were able to bring the bottom up of our guide and what we see as the large deals layering in. And the second is the anniversary of AFS. So we have visibility to that.

Our next question today comes from Bryan Bergin at TD Cowen.

I wanted to ask about some of the emerging and, I guess, evolving delivery model. So as we think about AI increasingly in the future, to what extent does the broader GSI and tech service model need to pivot to an FTE model, you had an interesting announcement yesterday with Microsoft. Just curious, like if we fast forward a few years, is the majority of tech service implementation likely to be in an FTE model or just more so a mix. And near term, any important financial considerations as you lean into this model with partners.

What I would say is it will definitely be a mix, and that's because the way that the FTE model today really gives value is when you are going in and solving problems that haven't been solved typically in mission-critical areas, where in order to get the AI to work, and these are usually like the spoke problems, at least initially, you have to have deep domain knowledge from the clients, the technology knowledge, and then what we bring to the table, right, the experience, the integration, the industry and functional knowledge and you work in teams to new problems.

And then the idea from there, of course, is that we will then replicate that over time at other clients. And so there's more and more agility and how we're delivering, but the actual kind of thing that people call FTE models is really about solving those problems. We do think that delivery overall, it is changing. We're already using both more technology, but also being able to like kind of use more of the team that have all of those functions, industry technology, functional expertise. And so we do think that the way we bring our teams together will change and some of that will be more upfront and like an FTE model. But what we are really differentiating in the market right now is that we have all of those skills at Accenture, and that's helping us win more.

Okay. That's helpful. And then on free cash flow, so you've got a strong generation here trailing 12 months of almost 30%. Can you comment on what you're doing differently here in net working capital or something tax related that's allowing for that and obviously, the raise for the year aside from lower CapEx? And is it sustainable as we think about future free cash flow conversion?

Thanks, Brian. Our free cash flow, we just recorded record free cash flow on a year-to-date basis of $5.2 billion, and it is really driven by efficiencies in our operations, as well as DSO. Our DSO was roughly about 5 days below last quarter, and it was 2 days better than the same time last year. That is just us continuing to focus on getting cash and operating more efficiently. So our raise this quarter for the full year, we were really pleased with, and it's a strong result and free cash flow to net income ratio of 1.3.

And our next question today comes from Jonathan Lee at Guggenheim Partners.

Julie, I want to build on a prior question around V&A. Can you help us understand what's driving the step-up in deployment and whether this reflects a shift in acquisition strategy towards larger or earlier-stage assets, higher multiple of the market or perhaps a pivot toward IP-led deals?

Sure. I'll let Angie, just talk about kind of how we're seeing valuations in a moment, but I want to talk a little bit just about your point about what the strategy is. So our strategy that we've executed over the last decade or so, has been to use V&A often to go into new areas that are higher growth. So we did that with Song. We've done that with Industry X. You saw us do that with capital projects over the last few years. And that is all to fuel organic growth, right? So it's increasing our total addressable market by going into new higher-growth areas. And that's, again, what you're seeing us do that. And we're doing that in key AI enablers. And so those are things like data centers, energy infrastructure. We're doing that in big secular trends like defense you've seen those acquisitions over the last couple of years and public sector is another one. Education is another one. So higher-growth areas, increasing our TAM. And then increasingly, we see an opportunity to meet unmet demand in the market where you don't have solutions where we can build products, either organically or by purchasing them. So our Faculty acquisition, for example, has a really unique decision intelligence product.

And then in addition, there are new commercial models where data is one of the key enablers of AI. And so you saw our Ookla acquisition, which is really about an incredible data set. And the way that then gets into our business is the network is really core to both communications and all enterprises and so AI having this kind of a data set is incredibly powerful, and it's a completely different commercial model, licensing and subscription base.

And so we're also using our balance sheet to get into these exciting new areas that also bring us new commercial models that are not linked to FTE. And that will then mean we kind of have a different mix in terms of the financial profile and valuations and Angie just give us a quick update on how to think about that.

Yes. And so in some cases, we are paying higher multiples than in the past. So the immediate uplift is lower in those instances than prior acquisitions. So that said, we are intentionally shifting towards higher growth, higher-margin assets that are going to fuel organic growth and strengthen our capabilities, and it's really to position us for long-term growth and returns.

Helpful color on the strategic pivot there. As a follow-up, one of your partners recently highlighted the ability to reduce SAP or premigration workloads to as little as 2 weeks using AI, how do you respond to concerns that AI tools are compressing project time lines relative rate cards and reducing the TAM for systems integration work? And are you seeing similar compression in your own engagement? And if so, how are you offsetting this through volume or new service offerings?

So in general, you should think about our strategy is always that the more that we can use technology to bring more value to clients faster, the better it is for our business. And that's the strategy you've seen us execute ever since RPA like burst on the scene in 2015, because when you can actually make -- especially the technical piece of it go faster, so much work, all the process change, all the change management, et cetera, that like the SAP deals are multiyear. And those often become gating items that they're not investing in other parts of the technology landscape or other parts of the business because they have these huge projects.

And so we see this as -- whether it's in ERP or mainframe, it helps because the actual technical piece is a small piece compared to the rest of the work, and it leads to more work. And so for example, just last week, we were at AI Palantir's Conference with SAP, Palantir and Accenture on stage saying we're going to develop those products. They're really not developed yet scale in any way. But we're working together because it will be a net benefit to our clients, which means it will be a net benefit to us. That's how we think about it.

And our next question today comes from Sean Kennedy at Mizuho.

So one of the themes across the sector currently is higher-margin AI services offsetting more competitive pricing in legacy services. So I was wondering if Accenture seeing similar trends helping with gross margin? And also how much of a productivity boost is Accenture seeing internally from these AI programs?

Sean, so let me just start with pricing. For us, this quarter, we -- pricing, which is the margin on the work that we sell. We saw improvements in some areas of our business, and we continue to operate in a highly competitive environment.

Yes. Internally, we think about applying AI in our delivery where -- we're continuing to improve our efficiencies in delivery, which has also helped fueling our growth. And then in how we operate Accenture, we're deploying all the services we give to clients to us. We're often the experimentation place as well, and we're really pleased and you can see that being reflected in the efficiencies we're getting that are reflected in ROI. .

Operator, we have time for one more question, and then Julie will wrap up the call.

And our final question today comes from Dave Koning with Baird.

It seems like you're doing very well with big clients, 41 with $100 million plus in quarterly bookings this quarter. That's been in the low 30s. So that number is up 20% plus. Do you think big clients, the big -- the huge companies that we all know well, are early to spend on AI and big transformational projects in the midsized companies are kind of a little more of a wait-and-see mode. And actually, you might be at the front end of a big swell of those picking up after the big clients are starting to spend on this. Just interested in your thoughts there.

Yes. It's a great question. I don't see it developing quite that way because actually some of the smaller companies are spending a fair amount, like that's where we had a lot of growth, and it's one of the reasons we're focusing even more on the mid-market and made a few acquisitions there to fuel that organic growth. The way I would think about the $100 million bookings or more is that in the large enterprises, is -- it just is a reflection of just how much reinvention they have to do. And that AI is the catalyst for that. And they have big states that have to be modernized a lot of change.

And so those -- that's really kind of a reflection more of how much they have to do. And at the same time, I believe we are early in what will be a large funnel of work because in both the big enterprises and the small enterprises, you just think about -- I talked a little bit about this in the script, like everybody who's put in modern ERP in the last few years, and we're the #1 partner there, none of -- no advanced AI was possible there, right?

So like that's a whole wave of work that has to get started, and then we're beginning to see early momentum on, right? And that's not even then -- we've done very little still in core operations because the advanced AI isn't quite there. Like physical AI is going to be coming. Agentic AI is still early. So we just see a lot of work, but the technology has got to get to the right level and of course, factors like the macro beat into that. So we're really excited about the long term because there's so much more value that clients are going to get from AI. And we're demonstrating every quarter that they're the one that they're coming to us for it. So thank you for the question.

And just a quick follow-up. I think Angie said federal spending would be up year-over-year again in fiscal Q4. I'm just wondering into next year, does that normalize that back to the higher level that it's been into next year?

David, we'll give you an update in September on that. So we feel really good about the fact that we are anniversarying the headwind, and we're getting back to growth in the fourth quarter with our Federal business.

Go Federal. All right. Thanks, everyone. I just want to thank all our shareholders for your continued trust and support. And all of our reinventors around the world that every day are delivering incredible value. So thanks for joining, and we'll talk to you next quarter.

Thank you.

Thank you. That concludes today's conference call. We thank you all for attending today's presentation. You may now disconnect your lines, and have a wonderful day.

Hello, everyone, and welcome to today's webinar. My name is Justine Iverson, and I look after the Corporate segment as well as the AI strategy for Data and Research within Market Intelligence. I'm thrilled for you to all join us today for our webinar, Reinventing AI Strategy for 2026.

Before I introduce or let our esteemed guests introduce themselves, I quickly just want to go through a couple of housekeeping items. The objective of today's session is for this to be interactive. You'll see there's no slides in this presentation. This is an open conversation amongst experts within the AI space to help you as you're thinking about your AI strategy. But at the bottom of your screen, you'll see some widgets where you can gain access to some blogs and some other resources as well as the ability to ask questions. We want to hear from you. We want your questions. So please enter those throughout the session. And we'll do our best to answer them all. We probably won't get to them all, but we will do our very, very best.

With that, I'm going to quickly introduce myself and then pass it to my -- like I said, esteemed guests to introduce themselves. Like I said, I lead Corporates and AI for Data and Research. So very simply put, that is Cap IQ and the data feed delivery of all of that great content. So I have had the pleasure of working with the 3 on this call, Francis, Jesse and Alaina in various different roles. And also get to spend a ton of time with clients and some of our partners in the space as it relates to AI. So I'm excited to talk about that today. But before we do that, Francis, I will pass it over to you for an intro.

Hello Justine, thank you for welcoming me. I'm Francis Hintermann. I'm the Global Lead of Research at Accenture, working from New York City in a team, which is growth in strategy in charge of supporting the development and the implementation of the strategy of Accenture.

Fantastic. Thank you. Jesse, over to you.

Awesome. Thanks, Justine. I'm Jesse Kramer. I look after M&A and investments at S&P Global for the company as a whole. So supporting Justine and team thinking about inorganic growth, but also our ratings, energy, addressable MI division, our Index division as we think about how to grow the company. We spent a lot of time with emerging companies in the space, thinking about how they're applying new technologies to our clients' workflows and our workflows. And so I'm excited to be here today.

Awesome. Thank you. And Alaina, last, but of course, not least, to you.

Thanks, Justine, and really excited to be here for this discussion today with you and Francis and Jesse. My name is Alaina Tosatti. I lead our -- what is the Strategy and Business Transformation team in S&P Global Market Intelligence. Market Intelligence is one of the business divisions of S&P. It's about a $5 billion business, and it houses, as Justine talked about, brands like Cap IQ and our unique data and IP, along with other offerings in software and services, really focused on serving into our -- into the capital markets. So looking forward to the discussion today. I spend a lot of my day and time thinking about growth opportunities, how we can be more efficient and how we balance that with risk across our portfolio in Market Intelligence.

Awesome. Thank you all so much. I promised everyone I wouldn't make them give a fun factor up themselves on their intros. So sorry, you all missed that portion. With that, the basis of this webinar really came out of the hundreds of client and partnership engagements that I mentioned earlier on. Candidly, it's the favorite part of my job is to be able to be in the market, see what's happening. It's such a fun space. I say this all the time. I've never worked in something moving so quickly, particularly as we think about you all on this call, right? You all work for banks, investment managers, corporations and I think you probably all are feeling the same that we are. It's moving quickly, and we're doing our best to meet you and serve you where you are and make sure you're getting the most out of our content and our offerings in this space.

So the blog that I offered was really based around kind of a couple of key trends that I saw. So I'll hit on a couple of those and just to get the conversation started and then get some thoughts from Francis and team. So I would say a couple of the key trends, I'm not going to go through them all, but the biggest trend, I will say that I'm seeing to be completely candid, is organizations are still figuring out their AI strategy. So if you feel like it's evolving or there's something new happening every day, you are not alone. That is the #1 trend I'm seeing no matter the size, sophistication, the market cap of the organization.

And I think following that, there's a real push today more than ever to really determine and measure the ROI and the impact of that Gen AI initiative, right? I think we shifted from a time of a lot of POCs and a lot of experimentation to now a real push for understanding that top bottom line impact that organizations are seeing. I was hearing this in my client conversations and working with our partners. And so I actually did a little research based on our earnings call transcripts that we have. I went and looked from Q3 -- Q4 2023 to the end of last year just to see how mentions evolved across earnings calls. Again, this is for our whole corpus of earnings calls. So very broad swath of global companies. And super interestingly, over that time, AI mentions, right, just the mentions of AI in these earnings calls was pretty steady, about 4.5% increase over that time. So nothing totally drastic there.

But what we noticed is over that same time frame, there was a 57% increase in mentions of AI cost savings and positive sentiment around cost savings related to AI. So you can see how that trend and that management expectation and Street expectation of ROI on this investment is really starting to play out across all industries, all sectors, all geographies. So that's one trend that I really started to see evolve over the past 12 months that I think will continue into 2026.

Another quick one. I think there's a bit of speculation or where is this all going to go? But at the end of the day, money talks and money is still flowing. In 2025 alone, according to Cap IQ, there was a $95 billion raised across 143 funding rounds. And that's for AI-specific firms. This takes out chip manufacturers. This takes out data center providers, right? This talks solely about that, nearly tripling from 2024. So money is still flowing. There's still investment. There's still a lot of interest in this. Jesse, I know we'll talk about this a little bit later from your perspective.

The other thing I'll say that I've seen a big evolution over the past 12 months is the partnership ecosystem continues to evolve. We do a ton of partnerships at S&P Global. The AI firms are partnering with each other in different ways. We'll talk a bit more about that later. So that continues to be a big trend.

Two last points I'll make before I open it up, I'll say, is that the risk tolerance has really evolved. I would say when I first started meeting with a lot of clients, there was probably 2 big camps. One camp of we're all in, we're opening it up. We're letting our employees or our organization use AI, use whatever tool they want. Obviously, you can imagine some of that is tightened as there's concerns around IP and data protection of internal information, et cetera. But on the other side, we've seen firms that were a bit more slow to adopt or a bit more conservative, really evolve to really change their posture and figure out how to bring that into their organization safely, soundly, securely to really help their employees.

And last but not least, I have to say this, but it's true, it's all about the data. Data is the foundation, whether it's our data using data like S&P Global's data, proprietary data on the client side or other data, it's all about how you are utilizing that data to get the most out of it.

So those are a couple of key trends I've seen. Before I pass it to you, Francis, I'd love to ask the audience if my first hypothesis or my first observation plays out. So how many AI tools has your team trialed or adopted in the past 12 to 18 months? Would love to see, is it 1 to 2, 2 to 4, 5 plus? Or are you guys still early on your journey? And I would say, I won't scoop the audience because you all put in perspective. But from my view, we've seen a ton of optionality in this space, right? There's the hyperscalers such as [ Claude ] and OpenAI really investing in this space all the way to these last mile solutions that do something really, really well. And that's creating a lot of optionality across organizations. So let's see what you all said.

So let's move to the next. All right. So pretty well versed here. No surprise. Most are between the 1 and 4 points. That's actually probably what I've seen, right? And 1 to 4 could be maybe one's homegrown or something internal, mixed with a third-party application or even using Gen AI tooling in a solution or tool that you already have adopted and used for many, many years. A great example of that might be Cap IQ. 5 plus, interesting to see almost 90% of that. That's still a lot of different tools to trial and experiment. So definitely what we're seeing. But with that, enough for me.

Francis, what have I missed? What are you seeing? What do you think is also happening in the market?

Yes. I don't know -- I didn't know if I could answer the survey just since I did not. But just in full transparency, I would have been in the 5-plus category, so the 19%. So because we're testing tools, new tools every day. And I think it's part of what is fascinating right today is that we've got new options coming nearly every day. So it's interesting to see where your participants are.

Thank you for asking me this question. I read you on a regular basis. I read your top 10 trends with interest. And out of what you mentioned earlier on, I'd like to pick up the one on ROI because, of course, as part of my remit at Accenture, I oversee all the thought leadership that we develop and we publish and we share with our clients. And ROI of investment in AI has been a source of questions for the past 3 years. So it's definitely one that we are extremely interested in. I agree with what you mentioned around cost saving, around use cases that you call administrative that we would call horizontal ones, right? Think about customer service, think about knowledge management, think about IT and the tech organization itself. Absolutely, yes. I think what's interesting though, in last year and even more this year is that we can see the verticalization, what we said about verticalization happening, meaning development of AI as part of what is really specific to industries, part of a core value chain. And that means that beyond the cost savings, there are opportunities as well for revenue growth for companies. And actually, we survey our clients and our partners on a regular basis at Accenture, and we published just our latest survey of CXOs at Davos, so 2 months ago. And we were asking some of the CXOs whether they are -- they have a choice to make, is AI more an opportunity to grow the revenue or more an opportunity to reduce the cost. And actually, 78% should be emphasis on revenue growth in the coming years.

And to us, that's an illustration of the fact that some of the AI implementation is moving to the core value chain of companies where they can actually create more value, create more opportunities to grow. We published some of these examples in a PhotoSheet that we published actually yesterday with the World Economic Forum on the organizational transformation in the edge of AI. And you will see there lots of case studies in different industries. If I just got to mention one to make it tangible to your audience, it's about the pharmaceutical industry, life sciences. And we can see there that in the drug discovery in R&D, which is very, very specific to life science, AI can actually help not only to accelerate the discovery, but actually change the discovery process itself. So for me, it's just one example. I could add more. But in the interest of time, I will stop here. Justine, I could react on other trends as well. I keep that for later on. But I like your point on data. So maybe we can come back to that later on if that's of interest to the audience later.

Yes. No, thank you. I always -- I think everyone wants to hear real examples. So the life science example makes complete sense. Obviously, in our world, we service kind of the range from life sciences all the way to financial institutions. So we see, obviously, a ton of our use cases really around that financial services use case. So Alaina, I'm curious from your view, like what are you seeing from your seat based on this? How is this impacting how a company like us, a major financial institution that services a vast array of clients? How are we thinking about our strategy and how we evolve that over this time as well?

Sure. Happy to jump in. Francis, really like your point about the shift to the focus on revenue growth and opportunity. I think that's something we're living and seeing kind of firsthand. Maybe taking a step back first, just thinking about the market and what have we been seeing, right? The pace is just the thing that stands out the most with this landscape, right? What we've seen from a development perspective has been incredible, frenetic is a word that gets used quite often, just to add to that piece. You think to the launch of Claude Cowork earlier this year, and that's really furthering this expansion into enterprise use cases, right? So it's clear now that Agentic workflows can and are being applied today to real work for our customers within our teams and Market Intelligence, and they're capable of completing these really kind of complicated tasks autonomously. So this is real. It's happening right now, and the pace is just an unbelievable adoption level.

And for businesses like ours, I think that really shifts that expectation of how we're going to use AI, whether a year or 2 ago, when you were talking about efficiency gains and optimization, now it's all about business transformation. What are we going to look like in this new age and how will we remain relevant and how will we pursue growth with this new paradigm. When we thought about it within Market Intelligence, we really leaned on 2 perspectives. First is really how the customers themselves for Market Intelligence are going to be using or are already using AI to transform their own work. And this will vary by use case, right? We giving a specific example, thinking about the buy-side customers, right, asset managers and hedge funds that we may work with, they're leveraging AI to kind of better ingest and synthesize and make sense of really large volumes of data that they're getting from multiple sources, including their own proprietary data that they want to keep quite safe and protected. And that will really help them with alpha generation, which is the ultimate end goal. So again, as you talked about data, that really resonates because certainly, one of the core tenets of what we offer into the market as a data partner is that providing of differentiated data to these firms and making sure that our data is ready and fit for purpose for whatever new tooling we may see within the customer segments.

And then the second lens that we've spent a lot of time thinking about, of course, has been sort of transforming within our own 4 walls. How do we rethink our workflows within Market Intelligence to leverage AI better. And we could talk about examples here within customer support teams, so better enabling some of those teams to get requests done quickly and efficiently so we can focus on the higher value and more of a white glove with customers or whether that be in our data operations groups who are automating a lot of the repetitive tasks and focusing on data ingestion and linking and normalization, which again speaks to how valuable that data will be once we can deliver it back to our customers.

So the real call to action there has been just embracing AI across everything that we do, thinking about not only how it will drive efficiencies, but how it can really drive value as we go forward. And maybe just one last point to add, and I think we can dive into this later if we need to. But really, the other element that we've thought a lot about and continues to remain critical is trust. And so again, as customers are adopting these tools, as we're using them more frequently, it's really critical to have the trust and the governance mechanisms in place to ensure that we keep that high quality and really what has defined from an S&P perspective, our brand in the market for decades.

So I'll stop there for now. Same with Francis, we could probably go for a long time on each and every one of these, but we'll stop there for now.

No, I love it. And you mentioned Claude Cowork and how quickly some of these tools have come to market and how quickly clients are interested in exploring. So with that, I'm actually going to go to my second poll question because I would love to see on this out of our group, what is your firm adopted internally? Are they using Claude, ChatGPT, a workflow-specific tool, right? There's a ton of these in the market that do a specific workflow very, very well. They're obsessed with solving that specific workflow, whether that's a Rogo in investment banking, a Harvey in the legal space. Are you using a homebuilt internal solution? Or are you not using anything yet? You're still kind of in this experimentation phase. So I would love to see what's been adopted on the side of all of our attendees.

All right. I think we should be able to see results now. And again, I think this plays to our previous one. There's a bit of a mix, right? I've seen a ton where there's actually a bit of a mix of tooling. So okay, interesting. ChatGPT, no surprise. I think we're seeing a lot of that. What I've seen candidly in my engagements across our corporate base, so think technology firms, consulting firms, we see ChatGPT as one of the early emerging winners there with [ Claude ] really making a lot of adoption within that investment banking financial services space. But again, these workflow-specific tools, we're seeing it. And I think a big point that we talked about, and we'll talk about this a little bit more, I'm seeing some questions come in that play to this is there's a lot of homebuilt solutions that are fantastic as well.

One of the big questions that came in and that we talked about a little bit was around organizing internal data. That is not a new challenge for organizations, right? That's one that we've seen in the market forever. I think AI just shines a light on that more than ever. And so something we've done at S&P is we've really tried to lend our expertise to help organizations with that. That's something we do, right? We're a data company. We take messy data, we make it organized and valuable. So that's something we spent a lot of time on is just thinking about how you do that. A great example, if you try to take tabular structured data and just put it into a large language model, you're not going to get great results. But if you do some technical work such as some python wrapping with -- you apply some business logic to understand, you can start to get really, really powerful results out of that. How you mentioned, Alaina, getting information out of vast amounts of textual data, for example, very, very quickly is a great example of that.

So with that, Jesse, I want to take a little bit of a pivot here. I know you spend a ton of time observing the market, trying to see who's going to be a winner, who's not. So I'm going to ask a little bit of a cheeky question. What's your prediction for the IPO market in 2026? And then we'll put up our fun poll question that might spur a bit more conversation from you, too.

Awesome. Well, Justine, I love a cheeky question on some of this. I think heading into '26, I think people all thought it was going to be a pretty robust IPO market. I think what's happened is in patches, there's been maybe less confidence in that market and MA markets in general because kind of what is valuable about companies is starting to change, and it's changing because of the fast pace of innovation and the thought that AI sort of can do everything or at least can do a number of very important tasks in our economy. And I think that probably is true to some extent, perhaps not as true as every kind of equity research analyst believes or worries for the companies in their portfolio, but particularly around kind of more traditional client software companies, even data analytics companies like ours, I think valuations have been a little bit more uncertain.

And I think for parties who can't sort of show real kind of AI traction, there's been sort of weakening of valuations. I think it will make it harder for companies like that to go public. Some of those, I think, were in kind of the queue to potentially IPO. At the same time, there's a set of companies that are only stronger because of this. The large sort of frontier labs, I think, are kind of queuing up to try to go public at the end of the year. And there are companies that are sitting on the data that powers AI, sitting on the kind of data warehousing and cleansing that really supports this a set of companies that are building that last mile of potential application layers on the AI models.

And I think they will be kind of a robust sort of set of potential IPO candidates. Whether that happens this year or happens in the next couple of years, I think, is an open debate. But I think we will start to see a few of those companies go public and that will require a strengthening, I think, of governance, sort of a change around some of the kind of circular economy phenomenon that's happening and probably a little bit more of a shift towards profitability in those businesses. But if one does, these things tend to come in trends. And so you can see a number of others kind of flowing from that.

I love it. And I'm going to ask my last polling question for the audience and based on your response. So of the audience, who do you believe will IPO first? Anthropic, OpenAI or you don't think either will? I know those were -- we see a lot of -- there's a lot of headlines about this. So curious what the audience thinks about this one. Give it another second or 2.

All right. Let's see what everyone thinks. Oh, mix bet. All right. This is probably how I feel about it. It's pretty mixed. So about 27% think Anthropic, 39% think OpenAI and then 1/3 also think it's neither. So I guess time will tell. If we had a magic ball here, it would be great, but we will see what happens throughout 2026 on this front. Awesome.

I think, Francis, I'd love to come back to you. What surprised you the most over the past 12 months? Or what's changed the most from your perspective on the AI front from your view?

Well, many things. But if I had to pick up one, I would pick up the one on work. I mean Alaina was mentioning some of it within your own company and what you do for clients. Definitely, we see a lot happening in the market. If we just start with a question of usage of AI tools as you were pulling the audience, Justine, we can see some shadow AI in place in companies, right? We've got executives sometimes telling us, well, employees are not so enthusiastic about it. That's not what we see. Again and again, as we pull employees of large companies during the year, 3 or 4 times a year, what we can see is lots of interest from employees to the point that when they have not access to the enterprise version of some of these tools, they actually use their own personal account to use these AI tools at home and then feed that back in the work, which obviously what we call shadow AI, which obviously is not good in terms of everything you can think of, of intellectual property of responsibility of ethics and so on and so forth. And so for us, that's really the imperative for executives to actually answer to the needs of their employees and understand where it's going on in terms of the job market and how they can help their employees in terms of upskilling and reskilling.

And in more general terms, we see an evolution towards what we call a skill-based economy, more and more defining the needs depending on the skill of employees. And there is a big mismatch there. We actually built an index with Wharton University for all of you, if you're interested, it's out there. It's on knowledge at Wharton. And you can look at your own skills compared to the market trends and what is asked by employers. And even we attempted to put a monetary value on some specific skills to measure the current mismatch.

And broadly speaking, what we can see is that this mismatch is at the core of AI adoption at scale and will impact the ROI that we were mentioning earlier on. So for us, that talent reinvention is really what we started to see in the past year and what we envision as being one of the major trends over the coming months and the coming years because it will take years, but that talent reinvention, making sure that employees have got the relevant skills to perform in this AI economy is going to be the critical factor to make it a success eventually.

I couldn't agree with that more, and I have a funny, maybe not funny story from just this week is a bunch of us internally were talking about some of our future AI work that we're doing, a ton of excitement around it, and we're taking notes. And halfway through the meeting, we're like, why didn't we turn on Copilot to take notes for us, right? Like it's a very -- and someone on the call said, yes, my son would have not even thought twice about this. It would have already been on, it would have been part of the workflow. And that's just funny because, to your point, there's a change management in the current workforce that needs to happen. We have these tools, but we need to start adopting them. We've worked a certain way for so long. There's human inertia in how you do your job. So there's that.

But then to your point, there's the next generation that this is inherent. This is part of their day-to-day. They've never not lived without this very streamlined experience. So I also believe change management and talent management is an area we're seeing a lot of focus on that, that needs to happen. How do we upskill our current employees, how do we prepare for the future? And what does the future look like? I've asked CCOs at banks, the junior banker, what does that look like to you? And it's hard to predict what that's going to look like because not only do they do certain jobs that can become more efficient with AI, but we're also the bench for the next job. So how do you balance what needs to be done today, how we can be more efficient today with building the bench to continue to grow that business overall. So I completely agree on the change management point. We've gotten some questions on that. So I think one of the biggest challenges, that was one of the questions as it relates to people is change management and integrating it into workflows, really understanding how that evolves the day-to-day. With that, we'll do a couple more questions, and then we'll go to Q&A because we have a ton coming in. Alaina, what do you think will change the most in the next 12 months? I know that's a tough question, but what do you think?

As we were saying, it's the one on everyone's mind. So I'll take a stab at, at least my perspectives. And in fact, picking up Francis perspective on one of the points you were raising about shadow AI and the idea that today, if we aren't moving fast enough in enterprises, it is on the consumer side, just, again, a pace of adoption that we have not seen in any other technology of late, and it will only kind of increase in sort of complexity and speed. So that was one of the points.

As I think about the next 12 months, I think consumer AI tools will continue to move even faster. And what does that mean on our side? Well, that just raises the expectations and the strong bottoms-up pressure that enterprises are feeling to keep pace, right? We have to be able to provide these tools in a safe and controlled and risk -- our own risk environment in order to better serve our own employees, but also ultimately into the end customers. I mean you think of the recent launch actually with Google Maps, right, and how they've now integrated Gemini AI into their maps application, and that's going to transform how we interact there with new recommendations and suggestions in this Immersive 3D experience, right? So we're seeing it rapidly and now and it's getting ahead of us on that consumer side. And so that is creating the right flywheel, I think, for the enterprise as well.

Maybe one other point then for the next 12 months is I think that high-impact enterprise use cases will continue to scale as we think about this next year, right? We think about investment banks who are already leveraging AI for step changes in how they generate pitch decks or investment memos and again, aggregate all this input and take things from days to minutes. That will continue. And again, in that poll and survey that talked about homegrown solutions that may be one of those unlock enablers, especially within some of the more regulated industries and intensive areas like banks that we work with.

And then I think back to the question on some of those LLM providers and where those are going, I think they'll continue to evolve. I mean when we've seen releases of new models that sweeps and bounds each time. So again, this will continue, and we're going to need to continue to kind of keep up. And I'd also expect a lot of them to -- and this is something we've obviously embraced and you've led for many of the discussions Justine on our side, but these continued partnerships between some of the more -- the data as well as the vertical solutions alongside these incredible models and the capabilities there just to better unlock very specific use cases for the customers.

I love it. We're going to do a quick lightning round, then we're going to open it up because we have received so many inbound questions. I would love to open it up to the audience to answer some of those. But a quick lightning round. And while I'm getting to it, feel free to answer the question on the screen here. What is one prediction or outlook you have for the AI market? And who -- or who do you think is going to be the winner? And again, don't worry, we're not holding anyone to your opinion today, but we would love to see what you're thinking today. Francis, why don't we start with you?

Yes, I'm not in the business of identifying winners directly. But what I can tell you is what we can see growing. And what we can see growing is the focus on what we call sovereign AI. I mean, what is happening in geopolitics. Obviously, we see it every day in the news. And it is impacting our clients. We, at Accenture work predominantly with large companies around the world. And this question of sovereign AI about what part of the stack has got to be localized, where you operate, how you develop the interoperability between the different layers and the different regions and still keep some agency in your strategic moves. For us, that's definitely a winning topic, if I can say it this way, Justine.

Love it. Jesse, what about you?

I think the status quo is probably likely to continue with kind of different providers being good at different things and continuing to sort of leapfrog each other. I think that's going to happen for a while. It seems unlikely to me that things will meaningfully converge to one provider, my read.

Alaina, I know you answered this a little bit, but if there's anything you want to add, your welcome.

I was going to say I crept into it a little bit in my last one, so apologies. But maybe I just double down on the point. I think we're in this -- as they kind of called it an industry era of specialization, right? No more general purpose AI. We're seeing specialized models, agent skills being developed. These are solving very specific domain challenges for industries. And so I guess my prediction around this is that we just continue to see the rise of some of these more specialized models, they also offer the economic benefit and the right fit for a lot of the use cases that need to be deployed against and ultimately can help us deliver more trusted outcomes. So a little bit on that.

Love it. My answer actually plays a little bit into one of the questions we got. One of the questions we got, so I'll answer it with my prediction. One of the questions we got is there's a lot of hype, right? There's a lot of press releases. There's a lot of noise out there. Like how do we know it's real. And I do think throughout the year, we're going to continue to see some of that kind of rubber hit the road, that realness, right? There's -- again, it's kind of how I started. There's a push to really start to see the ROI, whether that's top line growth, bottom line impact. And so there's, I think, going to be a bit more challenging from clients of all these tools, like we need to see that impact. We want to see that. So I think there's going to be a continued push for that. I think I believe that there is a spot for both, for all different types of solutions, whether that's a hyperscaler like Claude Code doing something, whether that's a last mile provider doing something really well. I think the market is vast, and I think there's going to be room for them in 2026, at least we'll see how that continues to evolve into the future from there.

So with that, we're going to open it up to the audience. So I'm going to go through some questions. We've had almost 100 questions already come in. So we probably will not get to all of them, but we will do our best to answer some of these out of the gate. So I think one of the first ones I have and Jesse, maybe we'll direct this one to you. Let me just scroll to it, sorry, we have lots in here. How -- this kind of plays to what you were saying earlier on the markets, but how does the amount of debt taken on by these companies impact their IPO chances? OpenAI is heavily levered. So does that mean they would need to IPO soon to continue the funds? What are your thoughts on this?

So I guess a couple of thoughts. The first is, I think the debt markets and private markets will continue to fund these businesses as long as they keep innovating and growing. I think in order to become a public company, these businesses will need to go through sort of a more rigorous audit and SEC process. A lot of the debt, at least as I understand it, that OpenAI has taken out has been sponsoring pieces of infrastructure projects. And there is a question as to how much of that they've guaranteed and how much of it rolls up into their obligations. And in that world, I think really unpicking how much they're responsible for, are they kind of marketable from a public company perspective is a really good question. There's also sort of a related question of how much of their revenue today relates to related party transactions. So I think that's sort of an important piece as well. But getting clean financials for these businesses is going to be one of the big hurdles, I think, of taking them public.

And then if they are responsible for all the debt they've taken on to build these data center projects and sort of the infrastructure that the models need, I think there's a real question as to whether they can be public this year or whether they have to kind of grow revenue into that to kind of get to some kind of leverage ratio. I mean, today, that almost makes no sense, right, because they're not profitable. But to get to some kind of leverage ratio that starts to make sense.

There's another question that said, what is that going to mean for all of us? And it's hard to see that not eventually meaning that the price of compute and these offerings will go up, particularly for enterprises. And like we're in this moment now of the economics have been made so attractive for everybody that we can just use these tools for kind of everything. I don't think that's the world we'll be in forever. And particularly as these companies look to drive profitability because eventually they'll have to, the use of AI may become a little bit different, and you'll need to be hyper focused on efficiency to use it in a profitable way for your businesses.

I love it. Thank you. Francis, I'm going to pass this next one to you. I think it's a great question given your role. What do you foresee as the future of management and strategic consulting firms in the era of AI? And how do you see that evolving?

It's a great question, of course. And when we look back, 12 years ago, some of you may remember, not all of you, I guess, but that's a privilege of having gray hair is that I was already there 12 years ago. When cloud started to expand greatly, some -- if you were there, you may remember the prediction that we made at the time by some that the consulting industry was going to go down because there would be no need of consultants anymore in the era of cloud. Fast -- and even you had some prediction made by some researchers at Oxford University, which were saying that overall, 47% of the jobs will be automated, and that would be certainly true in the consulting industry even to a larger extent. But if you fast forward then 12 years, you can see that the consulting industry is today actually larger than it was 12 years ago. And I believe that's something of that kind, which is going to happen with that current transformation. If I believe what analysts are saying about that current transformation is that there is a need for companies to get the help of consultants to go through that transformation.

I love it. Yes, agreed. I think it goes back to what we were talking about earlier with change management, right? That's a great area where there's so much support and necessary need from that industry.

I'll answer a couple. We've gotten a couple of questions on guardrails and how to avoid hallucinations. I always joke, I never said the word hallucination at work until the past 18 months, and now it comes up in almost every client meeting. So not a word I thought I would say at work very often. And I can talk about what we've done here at S&P because I think, again, we know our clients, you all make million, billion dollar market moving decisions on the back of our data and on the back of your own expertise and analysis. So the way we really think about that is when you're using an LLM, right, you have your guardrails that you can instill on those. And the way I always explain this very simply is we turn that knob all the way up, right? So we turn those guardrails up. We'd rather tell you we can't answer that than give you a bad answer and answer that's not accurate. And all of our answers are grounded in our leading data. At the end of the day, it's about data. It's about accuracy, quality, completeness of our data, and that remains core to all that we do and what we've always done, what we're founded on. But I think that's an important lever. So one, it's that foundational data layer being accurate, clean, complete.

And then two, it's really about turning up those guardrails as you're implementing that with LLMs and other tools. So that's something that we've done here that has really helped us. And like I said, we are happy to say we can't answer that or we can't do that for you versus giving you an incorrect answer. And then we always ground those answers in those results so that someone can check it. And I think that is something really important as we talk about training and upskilling workforce is teaching that validation step, right, not taking maybe the answer that you're getting as truth without kind of digging into it. We've all seen it. We've all seen the headlines of a fake court case that makes its way into some work or things like that. And so I think as you think about how to avoid that risk, it's also a human element of checking that, auditing those responses as you are taking those answers from AI. So there's a couple of questions on that throughout. Alaina I'll pass...

Can I say?

Oh, go ahead. Sorry, jump in.

Yes. Maybe just one word on that, Justine, because I was listening to you with interest and totally concur with what you were saying. And our own CEO, Julie Sweet, said that it's not about human in the loop, it's about human in the lead. And that has a meaning because everything you said was about the responsibility stays with human in terms of setting the direction, setting the boundaries, making sure that the discipline is actually executed as it should be. So it requires more leadership rather than less leadership. And that's why Julie has been coining that time and again and again of human in the lead. And I think what you were saying, Justine, is a very good illustration of that.

I'm going to adopt that. Human in the lead is going to be my new catch phrases of human loop. I think that's spot on.

Alaina, we're going to -- I'll pass this on to you just because I know you spend a lot of time with our CFO and our team members. What is your view? Are CFOs now being asked to understand AI and the need for strategic framework? Like how does that change how you think about strategic roles and the role of someone like a CFO at an organization?

Yes. I mean, short answer, absolutely. I think as you can imagine, this is so critical from different lenses for companies. As we've talked about, it is -- has been for a long time, been talked about from an efficiency perspective and how can we optimize what we are doing today, how can we free up resources, how can we reinvest that in other places and higher value in new services for customers and new growth opportunities. And again, increasingly, we're pivoting into this new growth paradigm. And so that, of course, those 2 things are what CFOs are constantly thinking about. And so understanding what we are doing in AI and challenging us to continue to do that is definitely a big part of that piece. There's also, of course, an element about how just CFOs and really any teams within organizations, but just picking on that as part of this question, are using it within their own teams today, right? And so again, I think not only from both the role as in representing and thinking about where we're going from a financial profile for our business, but also thinking how we can really improve efficiencies across a lot of the team members that we have today and whether that be in finance or any other supporting team in the organizations. We talked earlier about this incredible need for change management. And it has to be in every team, and we do see that actually today. We see a lot of interest from colleagues across the organization. So we want to embrace that and actually encourage everybody to be innovating a bit in what they're doing. How could -- you take one tool tomorrow and just optimize a little bit of what you've been doing for several years? How can you improve that going forward? So there's some very basic building block and incremental elements that we can all be thinking about. And also, of course, at the bigger picture for the organizations, CFOs and strategy teams are hyper focused on this area.

Jesse, this question kind of plays off that a little bit because these are things the CFO thinks about out of an organization. But what is your thought on the current AI bubble conversation and how companies -- they're accelerating it, but CapEx expense is also increasing where maybe they're not seeing that return. There was an MIT study that said companies are chasing this AI, but they're not seeing that. What are your thoughts on that? How do you see it from your perspective?

Look, I think it's hard not to think there's going to be some correction at some point just because there's so much hype. And eventually, I think we'll see one or more of sort of the big named companies make sort of missteps that will make people question the value of all of this. But I think the kind of overall kind of efficiency gains from the technology and the tools feel real to me. They feel real to me in sort of the -- just use of it in sort of our daily lives. And that is the underpinning of something that's not a bubble impact. It's sort of more economic output that sort of underlies all of this. And so I think it's probably at a specific company level, there's going to be stuff that's kind of overhyped. But at an overall economy level, I think probably grow into a lot of the valuations that we've seen over time. And that makes it kind of tough to invest in the space right now. But it's definitely one that's sort of on my mind pretty consistently.

Yes. I think we've had a lot of conversation on this ourselves between the 2 of us. So I think a big topic that we'll just kind of have to see how it evolves and how valuations continue.

Francis, I'm going to come back to a bit different question or a different type of question goes to it. What's your advice or what have you seen successful as it relates to AI training internally, right? All of these hyperscalers have tools in training. There's internal firms creating it. You talked about firms like your supporting on this. What have you seen work in this space? And what's your advice for the audience?

Try it. That's the advice is that we should all try it. I don't think there is one silver bullet. I think there is an enormous appetite to actually get to some learning, and that learning comes by trying it. We call that the era of co-intelligence. We actually presenting a new research at the NVIDIA event, GTC this week on that. And we say co-intelligence because it's a complete change in the sense that we all can build AI agents. These AI agents are going to learn from you about how to best serve you and you're going to learn from the agent as well. So that's what we call co-intelligence because it's learning from the agent and educating the agent at the same time. And I think for all of us, you mentioned different tools earlier on. That's an opportunity to enhance our own job. And to the point that at Accenture, we created a line of service dedicated to training executives in this area of AI that we didn't have before. We even bought a company called Udacity doing courses because we saw that the appetite of executives across the board was enormous, and we wanted to be there to serve them. But so for me, at the individual level, it's about that. If you don't have your agent yet, build it and you will have fun. Some of it will be extremely helpful in your job.

I agree. I think one of the biggest advice I give is the same. Like you got to start somewhere. I think where we've seen the most advanced adoption is where people really think through their workflow. And instead of just trying to pick anything, they find one spot and really focus on that and then build from there. So I think that's other advice I'd give is really think about your day-to-day or your team's day-to-day or your organization's day-to-day, where is there that constant bottleneck of time and then try it. So I think that's great advice there.

Another question we got and one, candidly, I've answered a lot amongst our client base, et cetera, the question we got was specifically around maybe some of the more traditional legal providers such as LexisNexis, et cetera, and how that will be impacted. I'm not going to speak specifically to them, but I'll speak to traditional offerings or offerings that have been around. Maybe Cap IQ is a great example of that. What does that mean? What does the future look like?

And I'll give my honest view on this. I think it's evolve or die. We have to continue to evolve and meet our users where they are and how they want to work. So the example I always give with Cap IQ, for example, it was built on the foundation of making it easier for people to do their job. I think a marketing slogan early on was get you out at 10 instead of 2 a.m. okay? Maybe our marketing slogan now is get you out at 6 instead of 10. So I think that evolves the foundational basis of what these organizations do has not changed, right? It's to make it easier for our end clients or their end users do their job, but how that gets done has changed. So a big thing we've really focused on is bringing the best of that Gen AI technology to the tools you already rely on. I think we've talked about it today, governance, getting new tools in-house, like that's work, right? That's a lot of effort. You have to go through procurement. You have to go through testing and making sure these tools are accurate, et cetera. So great, let's help our users out by bringing the best to the tools that they've used in their workflows. And so I think it's all about evolution. It's all about meeting users where they are, et cetera. So that's an answer I'll see on one of those type of questions of how does it evolve.

And maybe I can add just one word on that, Justine, you didn't ask me the question, but let me just add one word on that because we've been on that journey of developing partnership with data providers ourselves for our own tools for 3 years now. And kudos to S&P, you were part of the very first companies to actually be out there to develop these new tools with us and to provide lots of data in our own tools through APIs. And we are heavy users, of course, of Capital IQ and SAP Trucost as well for ESG data and very thankful to your company to be there. And really, you were part of the early ones to be in that game.

I love it. I promise I did not plug Francis to say that, that was on his own accord. Alaina, I think you want to add something?

I was going to add because we almost made it through this webinar without using one of the most common things, MCP. And maybe just to put a point on one of the areas that you've that we've discussed and certainly has been a differentiator and as we go forward as part of the strategy as well. But as we think about unlocking, especially as a large -- a company that sits on a large and very differentiated data estate, MCP is one of the ways we're going to enable and are enabling customers today to interact with that data. And this will be key in really helping unlock those agentic workflows for the customers. So back to your point, wherever they are doing their work, whether that be Claude in the future, whether that be their own homegrown systems, as they evolve with that, we evolve and are there in advance, hopefully, and also alongside them to bring that journey together. So we're seeing that, obviously, increasingly from a demand perspective from the customer side and are meeting that too with that just as one example of a way that we're going to modernize from a distribution lens.

Yes. Thank you. We would have been pretty remiss to not mention one of our focus areas. And honestly, what we're hearing from the market, that was a point, right? MCP was something that wasn't a word in anyone's vocabulary 18 months ago, and now it's the biggest topic or one of the most innovative areas that we're seeing. And if I were to make another prediction, I suspect there'll be some new technology or word that comes out in the next 12 months that drives how we're all thinking about this and utilizing AI as well. I know we're almost at time. So I'm going to kind of wrap here. Sorry, we didn't get to everyone's questions. Like I said, we appreciate the engagement. There was more than we could have handled on this. So we'll work to get responses back to folks accordingly.

Is there any closing remarks from anyone? Anything anyone wants to say in departure before we wrap in the next couple of minutes. Francis, we can start with you if there's anything you want to add.

Well, one of the most exciting things right now for me is about AI simulation. And in the research world, for those who are interested in research, we're developing lots of AI simulation in very interested to continue the discussion for those who are interesting on Tech Stack, so we can interact there. And in terms of the business, we can see the emergence of agentic commerce. And for us, that's going to be a very interesting area to follow in the coming few months because we can see that its doubling up in that space.

Love it. Jesse, what about you?

Look, I'm going to echo something Francis said earlier, which is try it. And it kind of goes to the fact that we're in this moment of the adoption curve, which is to say that like it's kind of being subsidized by the big companies and by investors to try to increase adoption. And so it's a moment to experiment, a bit more than normal, try it yourself, get it for your teams. Obviously, the right governance has to be put around it. But even experimenting in your personal life is definitely sort of a needle mover, I think, and it helps you and those who work for you kind of learn about how to use the tools better. And I think that's a big boon right now.

Love it. Alaina?

I'll go back to just my original kind of sentiment around pace and taking a moment to reflect, again, over the last few years of what we have seen and how disruptive it has been and remembering that sometimes early disruption can look a little incomplete and inferior in some cases and make you question whether it's the right direction of travel. This is, as we have been talking about and we can all see now a few years into this, it's very real and very applicable to so many different parts of the industry. So the reference back to the cloud migration years ago, the disruption of BlackBerry with iPhone, many companies have underestimated the speed at which these kind of transitions can take place. So don't be in that camp. And as Jesse said and Francis referenced, try these tools and apply them to what you're doing today.

Awesome. And my closing, I don't have too much more to add other than what the 3 of you have seemed to have said and covered. I think I'll just hit on the last point of this space is moving so quickly, and there's such different knowledge gap -- knowledge areas with people, lean in, right? Find a partner. We're here to do that. Accenture is here to do that. There's so much going on out there that I think it really creates an interesting time just in the business world, in the markets on how you can rethink your business and rethink how you can partner and drive productivity for your firms. And again, at the end of the day, it's that top and bottom line growth. How can we do that? Well, not bottom line growth, top line growth. How can we drive that? How can we continue to push that forward? So reach out. We're here to help. And thank you all so much for joining today. Like I said, you will receive a recording of this. And if you are listening to the replay, thank you, and we look forward to continuing on the AI journey with you all.

Thank you, Justine.

Thank you, Justine.

Good morning. Thank you for standing by. Welcome to Accenture's First Quarter Fiscal 2026 Conference Call. [Operator Instructions] As a reminder, this conference is being recorded. I'd now like to turn the conference over to Alexia Quadrani, Managing Director and Head of Investor Relations. Please go ahead.

Thank you, operator, and thanks, everyone, for joining us today on our first quarter 2026 earnings announcement. As the operator mentioned, I'm Alexia Quadrani, Executive Director, Head of Investor Relations. On today's call, you will hear from Julie Sweet, our Chair and Chief Executive Officer; and Angie Park, our Chief Financial Officer. We hope you've had an opportunity to review the news release we issued a short time ago. Let me quickly outline the agenda for today's call. Julie will begin with an overview of our results. Angie will take you through the financial details, including the income statement and balance sheet, along with some key operational metrics for the first quarter. Julie will then provide a brief update on our market positioning before Angie provides our business outlook for the second quarter and full year fiscal 2026. We will then take your questions before Julie provides a wrap-up at the end of the call.

Some of the matters we'll discuss on this call, including our business outlook, are forward-looking and as such, are subject to known and unknown risks and uncertainties including, but not limited to, those factors set forth in today's news release and discussed in our annual report and Form 10-K and quarterly reports on Form 10-Q and other SEC filings. These risks and uncertainties could cause actual results to differ materially from those expressed on the call. During our call today, we will reference certain non-GAAP financial measures, which we believe provide useful information for investors. We will include reconciliations of non-GAAP financial measures where appropriate to GAAP in our news release or in the Investor Relations section of our website at accenture.com. As always, Accenture assumes no obligation to update the information presented on this call. Now let me turn the call over to Julie.

Thank you, Alexia, and everyone for joining this morning. Apologies in advance for my voice. I am getting over a seasonal cold, and my voice is not quite cooperating. And really wanted to start today by thanking our nearly 784,000 people around the world for your extraordinary work and your commitment to our clients, which enabled us to deliver another strong quarter.

Let me begin by sharing that we are very proud to have earned the #4 spot on the Great Place to Work list of the World's Best Workplaces, our highest ever ranking on this prestigious list. This recognition reflects our strategy to be the most client-focused AI-enabled great place to work for reinventors. It is especially meaningful because it is based on feedback from our people worldwide. Our ability to attract and retain great talent is one of our most important competitive advantages.

Before handing over to Angie, I will briefly highlight the value we delivered this quarter, the importance of our partnership strategy and this quarter's strategic acquisitions. We are very pleased with our results as we continue executing our strategy to help our clients reinvent every part of their enterprise reflected in our bookings of $20.9 billion, including 33 clients with quarterly bookings greater than $100 million. We delivered revenue of $18.7 billion, growing 5% in local currency at the top of our guided range with broad-based growth across markets and both types of work.

And we continue to strengthen our competitive position by taking significant market share on a rolling 4-quarter basis against our basket of our closest global publicly traded competitors, which is how we calculate market share. Adjusted operating margin expanded by 30 basis points year-over-year, and we delivered adjusted EPS growth of 10% compared to Q1 last year. We continue to invest significantly to execute our talent strategy to rotate our workforce.

We have nearly reached our goal of 80,000 AI and data professionals and our people participated in approximately 8 million training hours this quarter with a significant focus on building advanced AI technology and industry skills. Advanced AI is increasingly embedded in our large transformation programs, either enabling future enterprise use or being implemented directly as part of our solutions. Our strong leadership in advanced AI is a clear competitive advantage as clients select us to help them capture the value of this technology now and over time and to build the readiness required to adopt it effectively across the enterprise. Momentum in the adoption of enterprise AI continues. Our advanced AI bookings this quarter were $2.2 billion, nearly doubling from Q1 last year and also up from Q4. Revenue reached another milestone this quarter at approximately $1.1 billion.

As we think about the advanced AI opportunity ahead, as you know, we were the first in our industry to share our bookings and revenue from advanced AI, which we define as GenAI, Agentic AI and Physical AI and does not include data, classical AI or RPA. We introduced the metrics in Q3 FY '23, just months after GenAI burst onto the scene initially to size the reality of the opportunity and to demonstrate our early leadership. At that time, bookings were about $100 million across roughly 100 projects and revenue was immaterial. We have measured it consistently since that time. To date, we have now delivered approximately $11.5 billion in bookings across 11,000 projects with revenue of $4.8 billion. This will be the last quarter in which we share these specific metrics. The demand for AI is both real and rapidly maturing.

We've now reached a point where advanced AI is being embedded in some way across nearly everything we do, and many of our clients are focusing on moving beyond stand-alone proof of concept or initiatives. We're shifting to more scaled end-to-end solutions that integrate multiple forms of AI, and it has become less meaningful to isolate the data specifically for advanced AI as it does not reflect how the demand is evolving on the ground, the scope -- the full scope of our AI work for the value we're creating.

Now turning to our partnership strategy. Our partnership strategy is grounded in client demand. Demand for reinvention remains strong with our clients continuing to prioritize larger transformational programs focused on building their digital core and driving both efficiency and growth. Technology is front and center for every client, and the 60% of our revenue in Q1 from work we do with our top 10 ecosystem partners continue to outpace our overall growth.

Given the importance of the broader technology ecosystem to our clients, we plan to continue providing insight into the role our top partners play in our growth by maintaining the metric we introduced at the end of FY '25, the percentage of our revenue tied to work with our top 10 ecosystem partners and its growth relative to our overall growth as it provides a clear view into our largest, most important partnerships. We also plan to continue to share our partnership strategy and how we're growing new businesses with an expanded group of partners. Most of our clients operate with a network of ecosystem partners to meet their enterprise needs. They rely on us to help integrate those partners and expect us to be the leader with the most relevant players across their enterprises, including new and emerging players.

As a result, it is important that in addition to our top 10, we work with a broad set of partners that play important roles across enterprises. Many of these support specific functions such as digital manufacturing, product engineering, core banking and insurance, supply chain and finance, while others are helping clients advance their AI and data capabilities. Our partnerships are critical to our clients reinventing all parts of their enterprises. Together, they represent meaningful opportunities for growth and further strengthen our ability to deliver comprehensive end-to-end solutions.

Over the past year in response to client demand, we've been expanding and, in some cases, forming new partnerships with emerging AI and data companies. And these -- and we will play a key role in helping our clients use these technologies, including creating new solutions and integrating and leveraging the synergies with their existing ecosystems. These evolving partnerships, which are laid out in our earnings presentation are a significant competitive advantage for us.

Turning now to our strategic acquisitions. Earlier this week, we announced an agreement to acquire a 65% majority stake in DLB Associates, a U.S.-based leader in AI, data center engineering and consulting in the rapidly growing data center professional services market, an estimated $12 billion addressable market expected to double by 2030. Along with our FY '25 acquisition of U.K.-based Soben, this meaningfully expands our capital projects, capabilities and presence in the high-growth data center consulting market.

It also positions us to capture growth not only through the work we do with our -- helping our clients use AI, our primary business, but also in the opportunity created by the companies building the infrastructure to power AI. And this quarter, we also invested $374 million primarily in 6 strategic acquisitions. We're scaling our capabilities with SIPAL, integrated product support business in Italy, which brings deep defense and aerospace engineering experience -- expertise for mission-critical programs and Total eBiz Solutions in Southeast Asia, which adds AI, cloud and digital workplace innovation that strengthens Avanade's position in the region.

And we're scaling new growth areas with NeuraFlash in the U.S., a Salesforce and advanced AI leader whose Agentic solutions expand our reach into the mid-market. Aidemy in Japan which enhances LearnVantage with AI learning and reskilling capabilities to help clients build AI-ready workforces, and Decho in the U.K. and RANGR Data in the U.S. which strengthen our Palantir and advanced AI capabilities. In summary, we are pleased with how we delivered the quarter and continue to strengthen our foundation for long-term growth. Over to you, Angie.

Thank you, Julie, and thanks to all of you for joining us on today's call. We are very pleased with our first quarter result with revenue at the top of our guided range as well as strong adjusted margin expansion, adjusted EPS growth and free cash flow. These results reflect the execution of our strategy to be the reinvention partner for our clients. We continue to invest for long-term market leadership while delivering significant value for our shareholders.

Now let me summarize a few highlights for the quarter. Revenues grew 5% in local currency, reflecting nearly 4% organic growth and were broad-based across geographic markets and types of work. Excluding the 1% impact from our federal business, our revenues grew approximately 6% in local currency in Q1. Adjusted operating margin was 17%, an increase of 30 basis points compared to Q1 results last year and continues to include significant investments in our business and our people.

We delivered adjusted EPS in the quarter of $3.94, which represents 10% growth compared to EPS last year. And finally, we delivered free cash flow of $1.5 billion and returned $3.3 billion to shareholders through accelerated repurchases and dividends this quarter. We also invested $374 million, primarily attributed to the 6 acquisitions in the quarter. With those high-level comments, let me turn to some of the details, starting with new bookings. New bookings were $20.9 billion for the quarter, representing 12% growth in U.S. dollars and 10% growth in local currency with an overall book-to-bill of 1.1. Consulting bookings were $9.9 billion with a book-to-bill of 1.0. Managed services' bookings were $11.1 billion with a book-to-bill of 1.2.

Turning now to revenues. Revenues for the quarter were $18.7 billion at the top of our guided range, reflecting a 6% increase in U.S. dollars and 5% in local currency and a foreign exchange impact of 1.4%. Consulting revenues for the quarter were $9.4 billion, up 4% in U.S. dollars and 3% in local currency. Managed services' revenues were $9.3 billion, up 8% in U.S. dollars and 7% in local currency, driven by high single-digit growth in technology managed services which include application managed services and infrastructure managed services and mid-single-digit growth in operations.

Turning to our geographic markets. In the Americas, revenue grew 4% in local currency. Excluding the 2% impact from our federal business, Americas grew 6% in local currency in the quarter. Growth was led by banking and capital markets, industrial and software platforms, partially offset by a decline in public service. Revenue growth was driven by the United States. In EMEA, we delivered 4% growth in local currency, led by growth in banking and capital markets, insurance and life sciences. Revenue growth was driven by the United Kingdom and Italy. In Asia Pacific, revenue grew 9% in local currency, led by growth in banking and capital markets, communications and media and public service. Revenue growth was led by Japan and Australia.

Before I move on, I want to briefly update you on the business optimization actions we initiated last quarter and completed in Q1 as part of executing our talent strategy. This year, we -- this quarter, we recorded $308 million in costs primarily related to employee severance, bringing the total for these actions over the past 6 months to $923 million. Our business optimization cost impacted operating margin, tax rate and EPS. The following comparisons exclude these impacts and reflect adjusted results.

Now moving down the income statement. Gross margin for the quarter was 33.1% compared with 32.9% for the same period last year. Sales and marketing expense for the quarter was 10% compared with 10.2% for the first quarter last year. General and administrative expense was 6.1% compared to 6% for the same quarter last year. Adjusted operating income was $3.2 billion in the first quarter, reflecting a 17% adjusted operating margin at 30 basis points compared with results in Q1 last year. Our effective -- our adjusted effective tax rate for the quarter was 23.9% compared with an effective tax rate of 21.6% for the first quarter last year.

Adjusted diluted earnings per share were $3.94 compared with diluted EPS of $3.59 in the first quarter last year, reflecting 10% growth. Days services outstanding were 51 days compared to 47 days last quarter and 50 days in the first quarter of last year. Free cash flow for the quarter was $1.5 billion, resulting from cash generated by operating activities of $1.7 billion, net of property and equipment additions of $157 million. Our cash balance at November 30 was $9.6 billion compared with $11.5 billion at August 31.

With regard to our ongoing objective to return cash to shareholders, in the first quarter, we accelerated our share buybacks and repurchased or redeemed 9.5 million shares for $2.3 billion at an average price of $245.32 per share. Also in November, we paid a quarterly cash dividend of $1.63 per share, a 10% increase over last year for a total of $1 billion. So in summary, we are very pleased with our Q1 results, and we are focused on delivering Q2 and the year.

Before I turn it back to Julie, let me provide an update on our commercial models. Our large base of fixed price work continues to grow and is a strong foundation for how we believe our commercial models will continue to evolve. In FY '25, about 60% of our work was fixed price, which is up about 10 points over the last 3 years. This reflects the increasing role of our proprietary platforms over a long period of time and clients wanting greater certainty in cost and delivery. This is where our scale, experience and strong financials matter. And now back to you, Julie.

Thank you, Angie. Starting with the demand environment, clients continue to prioritize their most strategic and large-scale transformational programs, which convert to revenue more slowly but position us at the center of the reinvention agendas. The pace of overall spending and discretionary spend in our market is at the same levels we have seen over the last year. We are delivering strong results and taking market share in this environment because reinvention is critical to our clients and our clients know we deliver real reinvention with real outcomes.

Let me turn to 4 strategic growth areas that are essential for enterprises to use technology, AI and data to achieve these outcomes. First, the digital core. Cloud, data and platform modernization remains foundational to every reinvention. When companies tell us they want to use AI, they quickly realize that AI is only as powerful as the data underneath it.

Most organizations have mountains of data spread across systems, stored in different formats, often unreliable or incomplete. Before AI can create value, underlying data and the processes connected to it need to be simplified, cleaned, connected and properly governed. We help clients manage all their data wherever it may be and turn it into something they can access and use to make decisions, train models and uncover insights.

We modernize their data platforms and make sure the data flows securely and consistently across the business so people can trust it and use it with confidence. We also use AI to improve data quality at scale. In the age of AI, data isn't just an input, it's the advantage. That's why we continue to see at least 1 out of every 2 advanced AI projects lead to a data project, and we're the partner that helps our clients unlock it.

For example, Essity, a global leader in hygiene and health, is making advanced AI, including Agentic AI, core to how they run their business, starting with procurement and finance, setting the foundation for company-wide reinvention. We are helping Essity build a cloud-based data and AI platform that combines Accenture's deep industry and functional expertise with our ability to scale advanced AI. We're starting in high-volume parts of the business, processing hundreds of thousands of purchase orders a year, the opportunity for double-digit productivity gains is strong. This foundation positions Essity to move decisively beyond pilots and reinvent end-to-end processes. unlocking new pathways to value and long-term growth.

Security remains one of our fastest-growing businesses, growing very strong double digits this quarter. As cloud, data and AI connect more of the enterprise, the threat landscape expands quickly. We are using AI to detect threats earlier, respond faster and simplify complex environments. Companies cannot scale AI unless they can do so safely, and this continues to be an important growth engine. Building on our long-standing relationship, we are partnering with one of Saudi Arabia's leading financial institutions to build a robust internal cyber defense capability that is designed to protect the bank, meet rising regulatory expectations and enable the launch of modern, sustainable digital services. We helped the bank move from limited visibility to a far more advanced security position, expanding threat detection, reducing incident response times and are helping to improve their national cyber maturity scores. We are also helping the bank achieve the full audit -- regulatory audit readiness, a critical requirement for trust and future growth.

Now with Accenture's deep cybersecurity expertise, we will bring in specialized talent, strengthen governance and help with upskilling to accelerate their progress. With a stronger foundation and the internal capability to maintain it, the bank can now introduce new services with far greater confidence and is well positioned for its next phase of innovation. Accenture Song grew mid-single digits this quarter. Song continues to help B2B and B2C clients drive growth by improving how they connect with and shape the customers' experience, the marketing that first reaches them, the website or store where they buy, the service when they need help and the digital products they use every day by bringing together design, creative, data, technology and industry expertise to reinvent marketing, commerce, service and digital products.

One example is our partnership with Virgin Media O2, one of the U.K.'s leading telecom providers, where we didn't just modernize technology, we worked together to transform the entire customer experience and how work gets done. By rebuilding their digital core and embedding advanced AI, nearly 10,500 service agents now work on a unified cloud platform with connected data and workflows. More than 300 customer journeys have been redesigned, simplifying processes and bringing the full customer context into a single view. These changes are helping agents resolve issues faster and more accurately, contributing to a 35% increase in Net Promoter Scores in some areas and same-day resolution, improving from approximately 65% 2 years ago to nearly 90% today.

A cultural shift is also underway, upskilling teams to enhance their customer-first mindset, turning service calls into opportunities to improve customer loyalty and trust. This kind of change takes strategy, process and talent working in sync with Accenture Song bringing it all together to design experiences that resonate at scale so that Virgin Media O2 can drive innovation and set a new benchmark for customer service excellence.

We also continue to grow in the core value chain of many industries through our Industry X offerings, growing mid-single digits this quarter. Manufacturing and engineering remain early in their digital transformation journeys. Digital twins, predictive analytics, robotics and other AI-enabled technologies are creating new levels of efficiency and resilience. Those same strengths are now propelling our capital projects work where clients need us to design, build and commission critical infrastructure and extend Accenture deeper into their core value chains. Take North America's transit sector, agencies are facing mounting pressure to modernize aging infrastructure and meet growing ridership needs, efforts that require delivering multiyear, multibillion-dollar capital programs.

Partnering with one of the largest public transit agencies, we're applying our infrastructure and capital projects expertise to help transform how these critical programs are managed, spanning a vast network of subway stations, maintenance facilities, bus garages and administrative offices. By bringing together multiple data sources across their construction portfolio, we are helping to enable more informed decision-making, supported by rigorous project controls, advanced scheduling, cost and risk management, and safety and financial oversight to improve efficiency, transparency and forecasting, just as we do in manufacturing. As a result, the agency is strengthening its daily transit operations and supporting safer, more reliable service for millions of riders.

Now let me share how we're seeing demand evolve with the work we're doing in advanced AI. It is early innings, which means there is significant opportunity ahead. Technology is rapidly evolving. While enterprise adoption at scale is nascent, demand continues to grow, and IDC estimates that the total addressable market for advanced AI is expected to grow more than 40% through 2029 from roughly $20 billion today to over $70 billion. We are seeing a steady increase in demand. Over the last 9 quarters, we've seen about 100 incremental clients initiate advanced AI projects with us each quarter, but most have a lot of work to do before they will be able to scale across the enterprise, and it is still a relatively small part of our client base, over 1,300 clients to date out of 9,000. So we see lots of opportunity to help those who have initiated and to expand in our existing clients as well as attract new clients.

Clients increasingly understand that advanced AI is not a quick fix. Adopting it successfully requires foundational work to deliver P&L impact and other critical outcomes. This is why our clients, and the broader ecosystem are turning to us to help bridge the gap between powerful technology and achieving real, measurable results. The real opportunity is not proving AI works, it is making it work everywhere. Scaling AI means working with all forms of AI and means embedding it across critical processes, so it transforms outcomes.

For example, we are partnering with Bristol-Myers Squibb, a global biopharmaceutical leader, to transform how therapies move from discovery to market by embedding AI at scale across the organization. Drawing on its deep life sciences experience, BMS is using AI to accelerate innovation and expand its impact for patients. We are also establishing early leadership in Agentic AI with the scale of our deployments working across the ecosystem. We have built an extensive library of over 3,000 reusable agents reflecting our deep industry and functional expertise. These agents have been used in real client environments, giving us a unique foundation of proven solutions to help clients move faster and with more confidence. Over to you, Angie.

Thanks, Julie. Now let me turn to our business outlook. For the second quarter of fiscal '26, we expect revenues to be in the range of $17.35 billion to $18 billion. This assumes the impact of FX will be approximately positive 3.5% compared to the second quarter of fiscal '25. Our Q2 guidance reflects an estimated 1% to 5% growth in local currency, including about a 1% impact from our federal business. For the full fiscal '26, based upon how the rates have been trending over the last few weeks, we continue to assume that the impact of FX on our results in U.S. dollars will be approximately positive 2% compared to fiscal '25. For the full fiscal '26, we continue to expect revenue to be in the range of 2% to 5% growth in local currency over fiscal '25, including an estimated 1% impact from our Federal business.

Excluding the impact of Federal, our revenue is expected to be an estimated 3% to 6%. This year, we continue to expect an inorganic contribution of about 1.5% and we continue to expect to invest about $3 billion in acquisitions this fiscal year with the potential to do more. For adjusted operating margin, we continue to expect fiscal year '26 to be 15.7% to 15.9%, a 10 to 30 basis point expansion over adjusted fiscal '25 results. We continue to expect our annual adjusted effective tax rate to be in the range of 23.5% to 25.5%. This compares to an adjusted effective tax rate of 23.6% in fiscal '25. We continue to expect our full year adjusted diluted earnings per share for fiscal '26 to be in the range of $13.52 to $13.90, or 5% to 8% growth over adjusted fiscal '25 results. Due to slightly higher business optimization costs in the quarter, which were $58 million above our original Q1 estimates, we now expect GAAP EPS of $13.12 to $13.50.

For the full fiscal '26, we continue to expect operating cash flow to be in the range of $10.8 billion to $11.5 billion, property and equipment additions to be approximately $1 billion, and free cash flow to be in the range of $9.8 billion to $10.5 billion. Our free cash flow guidance reflects a very strong free cash flow to net income ratio of 1.2. We continue to expect to return at least $9.3 billion through dividends and share repurchases, an increase of $1 billion or 12% from fiscal '25. Our Board of Directors declared a quarterly cash dividend of $1.63 per share to be paid on February 13, a 10% increase over last year. We remain committed to returning a substantial portion of our cash generated to our shareholders. With that, let's open it up so that we can take your questions. Alexia?

Thanks, Angie. I would ask that each speak to one question and one follow-up to allow for as many participants as possible to ask a question. Operator, would you provide instructions for those on the call, please?

[Operator Instructions] And today's first question comes from Tien-Tsin Huang with JPMorgan.

Julie, I appreciate your comments on scaling AI and how it's not a quick fix. So I did want to ask or maybe comment that we've noticed a bit of a shift in how people view the consulting industry's role in AI. Do you agree with this, Julie? And if so, why now? What's driving the change? And are you seeing any impact on business activity as a result of that?

Thanks, Tien-Tsin. Yes, we're absolutely seeing the shift. And it's really because of what we've been saying for a while, right? Enterprise AI is fundamentally different than consumer AI. Consumer AI adoption is instant, right? In the enterprise, you can't adopt it unless you have the right security. You've done the right work around processes and most companies have fragmented and siloed processes. You have to have the right data, and most companies have mountains of data with a lot of issues in the data, and we call it, they have process debt, they have data debt. And of course, they need a modern digital core. And that's why so many companies are still early in the journey. Clients -- our clients are convinced AI is going to be a very important part of their future, and it's going to allow them to unlock brand-new value. And that's why they're coming to us because now they want to actually get there. And that's the foundational work that's driving our business.

And then when you look at our bigger deals over the last quarter, for example, you see that advanced AI is a bigger part of those deals, but you also see that it's both growth and cost because clients are not only fixated on the productivity side, you cannot cut your way to growth. And in this market, they need to find more growth. And this is where our strength really comes in, and that's why we're delivering strong quarters, right? We have great momentum this quarter because we can help them on the growth agenda and the cost agenda. And we're so critical with our ecosystem partners. We understand where the technology is and where it's going. And you saw that again this quarter with our growth, with our top 10 ecosystem partners outpacing overall and then, of course, some exciting new partnerships.

All right. Perfect. And sort of a related question, you mentioned the partners. I'll ask around these AI partnerships. You announced a bunch, Anthropic, OpenAI, Snowflake, et cetera. Just how might these partnerships on the AI front be different than other tech ecosystem partnerships in terms of we think about time to productivity or investments to certify personnel to scale it up, et cetera. Just your thoughts on that and how quickly might these AI partners get to the top 10 on the partner front?

Well, first of all, the partnerships really demonstrate our talent advantage. So we've got decades of experience in being able to learn new technologies, skill people, who else could have hundreds of thousands of people, helping our biggest partnerships continue to grow incredibly well and be able to dedicate and commit 30,000 people here, et cetera. So I think that's a really important part of what you're seeing with us in our growth and our ability to grow into new areas is that we've got this foundation of great talent and the ability to upskill. And look, we're expanding in these partnerships because of what we see in client demand, but our clients have an ecosystem of partners. And the role that we play is to be -- we really try to be #1 with all of the partners so that we can help our clients integrate and use these new technologies with their existing ecosystem, which is absolutely critical to them. So lots of excitement. The market is expanding, and we're going to grow with that market.

And our next question today comes from -- sorry about that. And our next question today comes from Jason Kupferberg with Wells Fargo.

I actually wanted to pick up where Tien-Tsin just left off. I was curious as a follow-on regarding these big partnerships in the AI world, when do they start moving the needle on revenue in your view? It's obviously critical to lay the foundation right now, but is this a next 12-month dynamic where we can start to see it showing up in the P&L perhaps? Or is it longer dated? And then I have a follow-up.

Jason, what I would say is these partnerships are part of an ecosystem for our clients. And so it's I think more about the market itself, right? So our enterprise adoption is dependent on clients -- these partnerships are all about enterprise adoption. So I would focus more on how the market and enterprise adoption is going as opposed to specifically -- because we can scale as fast as needed. And that is -- our expectations there are reflected in our guidance.

Okay. Understood. And then I wanted to pick up on your comments about the increased adoption of the fixed price work because I think it ties in with a trend we've been seeing in your numbers where revenue growth is outpacing headcount growth pretty materially and consistently. So do you feel like that trend is sustainable? And what's really driving that? Because we've all been talking about nonlinear revenue growth for a very long time, and it seems like Accenture is now actually starting to see it.

Jason, it's Angie. Let me take that. We're really pleased with our revenue per person this quarter, which did grow 7%, which is really primarily driven by our talent rotation. We're now hiring -- as we shared with you last quarter, we're hiring for the new skill. And so we expect that revenue per person growth to moderate over the course of the year. And that'll go up and down really based upon when we bring people in.

And Jason, just to add on though, you're right though, the sort of revenue and headcount, that sort of breakage has been going on though for a long time. It really goes back all the way to the introduction of RPA. So we'd expect that to continue, but it's not tied exactly, as Angie said, to like what's happening quarter-to-quarter.

And our next question today comes from James Faucette with Morgan Stanley.

Want to follow-up on a couple of things that have been said by Tien-Tsin and Jason. First, back on AI and those bookings. I appreciate that those are -- AI is becoming integral to all of your engagements and so maybe define specific bookings, et cetera, on a go-forward basis makes sense. But how should we think about like the mix between what we could think of as proof-of-concept type engagements versus going into full production? And any color you can give on the types of projects that are moving to production, whether that be by industry or type, et cetera?

Sure. Great question. So first of all, one of the things that I think is important to understand is that people have moved ahead away from just thinking about models, right? It's about models embedded in solutions, and most of the solutions involve different kinds of AI, right? So you have -- if you have classical AI or RPA that is 100% accurate, right, depending on what you're using, you really have to understand all of that. And that's what the clients are looking to us for, is to bring them more solutions, which is why our partnerships are really important, and our understanding of the industry and the function is so important. So if you're doing something in banking, which is one of the industries where there's a lot going on at advanced AI, know your customer and compliance.

The solution they're looking for, you have to understand the actual compliance, how it works across the bank. And that's really the importance -- now of you're seeing that people get that these are about solutions, it's about really understanding operations, and that's what we're bringing. If you look at sort of where things are scaling into production, customer -- so a lot of customer service, and I gave one of the examples today on the call with the Virgin Media O2, you're also seeing areas like finance and procurement. And so these are areas where you've got good technology readiness, we have a lot of depth of understanding here, and you can move relatively quickly because it's using a lot of usually pretty good data.

We see a lot of value coming and being embedded in the core value chain, the grid and utilities, right, pharma and R&D. But those are the harder areas to crack. And so it's still pretty early in terms of scaling, but that kind of gives you a flavor for it. And so any of the industries that have a lot of customer service are there. Banking has been one of the lighthouses, insurance to some extent already. But one of the things I tell my clients is that in every industry, unlike prior waves of technology, there are leaders in every industry who already had strong digital cores who are leapfrogging. It's very different than cloud where you had some industries like, say, energy lagging behind for quite some time. It's quite different this time. And that really plays to our strengths because we have that diversity of industry expertise, and our clients look to us to really bring it across the board.

That's great. And then as a follow-up, you mentioned and talked a little bit about the revenue per head and the evolution there. One of the questions that we get from investors a lot is around pricing, particularly on a like-for-like basis, and how that may be evolving and how we should think about the puts and takes there, especially as it relates to your margins and margin growth objectives.

Yes. I think -- so as it relates to pricing, so just overall, I think you have to look at it in totality. And so as you think about our pricing, one of the things, and we've been seeing, and it's early, but we're seeing pricing improve in several parts of our business. And as you look, one of the things that we're super pleased about is that our contract profitability, we're starting to see some of that improved pricing show up in the P&L and we saw that this quarter. So we were really pleased with that, and it's really about balancing those components.

And our next question today comes from Bryan Keane at Citi.

Wanted to ask about discretionary spend. We're all waiting around for a while here for that to come back. I'd just be curious to know how you're thinking about that heading into next year, conversations with clients and should we be hopeful that discretionary spend comes back in the turn of the calendar year?

All right, Bryan, I'm not waiting around for it to come back, okay? So just to be clear, we have not been waiting around for it to come back. So I know you guys are, but we're delivering our results despite it because we really -- at this point, we haven't seen a change in the market. And when you look around, like we're all reading the same thing. We saw what came out in the U.S. this week on jobs. If -- there isn't some catalyst out there where we're saying that's the catalyst that's going to change confidence or change industries. And look, we work across industries. Every industry right now has got a different set of challenges with a lot of these big macro trends.

And so we're not having conversations today that would suggest that there's a big -- going to be a change in discretionary spending. But what the conversations we are having, though, is CEOs who are just like Accenture, they're very resolute that they have to deliver results despite the market. And that's why we're focused on pivoting the way they're spending. We're focused on doing the large transformational deals and then being at the center so that when we hopefully do get tailwinds, we're there to really benefit from them. But we're not seeing a catalyst. We'd love to hear it if you guys are in the external environment and our conversations haven't changed, but people are -- they're going to deliver despite that.

Got it. Got it. And then just as a follow-up, that 60% of work being fixed price is above industry norms and you guys have been working on that forever, and that's pushed up 10 points in the last 3 years. How do we think about the -- as AI becomes a bigger piece of that, does that -- can that number get up to 70% or 80%? And just a little bit of how that pricing works with productivity, how do you pass on some savings and keep it yourselves?

Yes. Well, one of the things that I think as you think about our positioning in the market is that these fixed price deals really are about our clients having confidence that we can deliver outcomes. And to do that, you've got to have our scale, our experience, our strong financials. And so we see that it is a real competitive advantage in this market where clients cannot simply experiment, they can't take a flyer. They have to know that when they're investing with a partner, they're going to deliver results. And that's why we feel the market now continue -- we're taking market share because of that. So I can't predict exactly, I mean, I think commercial models are going to continue.

We believe they're going to continue to evolve, and we've got a really strong foundation for that. And we are starting to see more focus on trying to get to outcome-based. So I think more to come in the models, but it really strength -- it speaks to the underlying strength of our business. And then with respect to pricing and passing it along to our clients, remember, this has been a business model for the industry really all the way going back to the introduction of technology and RPA, right, where we're signing contracts that depend on our use of more technology over time to provide productivity. And so that's still the commercial model of the industry right now.

And our next question today comes from Bryan Bergin at TD Cowen.

Wanted to ask on the growth side, so just really the moving parts as you consider the fiscal '26 growth outlook from here. So you -- from the 2% to 5% after a solid 1Q at the top end, 2Q looks largely as expected, yet the Federal headwind is actually a bit less than the lower -- than the prior range. So it sounds like demand is broadly consistent. So the question is, what may have precluded a low-end raise just considering that low end raise had deterioration assumed before? Just curious, is there anything incremental there or just ongoing uncertainty and just prudent approach just given this early in the fiscal year?

Bryan, so look, as we think about -- we just had a really strong print in Q1. We had strong bookings 2 quarters in a row. We can see the backlog from our large deals. We've got a solid pipe. And so our 2% to 5% really reflects what we see going for the remainder of the fiscal year. We got 3 quarters left, and it's our best view. And we were really pleased that Federal came in a bit better than what we had anticipated which is the strength of the work that they do.

Okay. Understood. And my follow-up is on Song. So I heard the mid-single-digit growth that sounds consistent here, which is good. I wanted to dig in, and I appreciate the detail you gave in the slides. Can you just talk about the implications for Song growth? Just looking ahead, just you consider recent advances in models like SOAR and there's a perception that enterprises can do more of this themselves or just the cost of such services will see material deflation. So how do you navigate that type of a backdrop going forward?

SOAR is a great example of something we embrace and are helping our clients embrace because what SOAR does is help them accelerate production. And so it's like a really good tool, but it's just a tool, right? So what Accenture does is say, how do you use these tools to actually get productivity differently to, more importantly, create the right new products and the new experience. And that's why one of the things I was mentioning earlier was that if you look kind of at our largest deals this quarter, a big proportion of them have customer and customer service in it because clients need growth.

And the tools are just a productivity piece. They're not what you can do to -- how do you actually respond to social media sentiment in an hour, right, which is what we can do in our operations around marketing, for example. So we see Song as critical because again, can't cut your way to growth. The market's not getting better overall for our clients, and they're really turning to us to find the new ways and to help them use these tools in the meantime to get more productivity.

And our next question today comes from Darrin Peller at Wolfe Research.

Julie, what does the revenue opportunity look like at a client that has done all of the digital core work required to be effectively leveraging AI as much as you'd like to see? And then maybe just a quick follow-on would be just around managed service opportunity and what that looks like, really trying to get a sense if you have any customers that are really at that stage that you can give us examples of whether it's a net increase to revenue and the opportunities you have and different services that could help a client out that has the digital core ready.

Darrin, it's a great question and what we're seeing is it's really expanding our work. So think about you've built a digital core. Now of course, just keep in mind, there's still a lot to do in the digital core because there's so much new opportunities and new ways of thinking about data, for example, than if you built your data foundation a few years ago. So there's still a fair amount of work to do even if you've got a pretty modernized digital core. But the real work, and this is why I think it's so important to understand how you adopt AI, is that you have to then change the processes, you have to upskill your talent, right?

One of the things I talk to CEOs a lot about is that if someone comes to you and says, here's how we do something today, now we're going to use AI and there isn't a big change, then they're not going to get value. And most of the work today has been around sort of isolated areas. It hasn't been across the enterprise. And so what you're seeing is we talked a little bit also about this last quarter, this inflection point where you've got now clients saying to us, okay, we have to do this across the enterprise. How do we think differently? Like how do we put marketing and sales and service together and they used to be in different functions. What does that mean then for the use of AI?

So the actual rewiring is a huge amount of work. And remember, the technology today, like it's great in some parts of the enterprise. But like if you think about manufacturing or engineering where we've had been investing for decades, it's still early in the digitization journey even for those who have a core. So we have lots of great examples, and that's why I talk about to our clients. Look, where people have already invested a lot, they're investing with us now to try to leapfrog. And that's where we are so different because we're tech, but we have the industry and the functional and the process and the change management, which is what unlocks the enterprise AI. So we really see a big opportunity over the next decade.

That's great. That's really helpful. Just very quickly, following the business optimization, what is your headcount? What should we expect from headcount or your headcount strategy for the remainder of the year now? Happy Holidays.

Thanks, Darrin.

Thanks, Darrin. I'll take that. So as it relates to our headcount, look, we expect to -- we're doing our talent rotation, and we expect to increase our headcount throughout the year in the U.S. and in Europe. So you should see that come through for the remainder of the year.

And our next question today comes from Kevin McVeigh at UBS.

Congratulations on the results. I think you had mentioned that advanced AI is 1,300 of your 9,000 clients and that implies about 14% versus the 6% revenue and 9% bookings. Should we use that in terms of a leading indicator or goalpost of what the revenue and bookings should scale? And if that's the case, any sense of that 14%, how that scales over time, just as we're thinking about the adoption rate?

Yes. The way I would really just think about it, so it's not meant to be some new metric in that way. What it's really showing you is just how rapidly it's moving, 100 clients initiating a quarter and that it's at the same time, really, really early, right, when you think of our whole client base. But I wouldn't start now creating like kind of a new metric and that, it's just too early to start drawing those correlations. But I'm trying to give some insight into like kind of where is the market right now.

It's super helpful. And then just real quick, the 17.3% margin, I mean, I went back, I think that's the highest Q1 you've ever had. Is that a function of the efficiencies and again, the fixed pricing? And how should we think about the margin trajectory of the business maybe a little bit longer term to the extent you can comment on that?

Yes. Kevin, we're really pleased with the 30 basis points of op margin expansion that we posted this quarter, which was in line with our expectations. And certainly, our operating margin is affected by the level of investments that we do throughout the year. So we're really pleased that we're reconfirming the 10 to 30 basis points for the full year. As it relates to EPS, I do want to make one point, really pleased with the 10% growth that we posted this quarter, strong operational results, op margin expansion as well as gains on investments.

But there's one thing that I do -- there's 2 things that I want to call out as it relates to Q2 specifically for your awareness and how we're thinking about it. First is that we expect our tax rate in Q2 to be above our full year guided range due to the tax impact of equity compensation. And then second is that we had higher gains on investments in Q2 of last year, and we don't expect the same for this year. But importantly, there's no change to our overall adjusted -- our full year guidance for adjusted op margin, tax or EPS. This is really timing.

And our final question comes from Dave Koning at Baird.

The health and public services growth was sequentially, I think, up 7%, the strongest, or I think the second strongest in 12 years, so very, very good. Is a lot of that just the federal spending headwind dissipating from Q4 into Q1? Was that a lot of it? Or is it just underlying health and federal spending just getting better than normal?

David, so as it relates to our health and public service, really, we saw strength in -- we told you about federal, and we know that and that came in better than we expected. And then the second component is the strength that we're seeing in EMEA and Asia Pacific, really strong, well positioned in terms of that business for us.

And keep in mind, that's where we've been investing over the last few years, including in acquisitions, and you're seeing that -- those investments pay off.

So thank you, everyone. In closing, I want to thank our shareholders for your continued trust and support. I want to thank all of the people who do this work every day, all of our reinventors around the world. I hope everyone has a very safe and happy holiday season. Thank you for joining today.

Thank you. That concludes today's conference call. We thank you all for attending today's presentation. You may now disconnect your lines and have a wonderful day.