Appian Aktie

All right. Well, thanks, everybody, for joining us in the last session of the day for day 2 of the Citi Global TMT Conference. I'm Steve Enders, part of the software research team here. And with us today or for the final session today, we have Serge from Appian. Serge, thank you so much for joining us.

Thank you for having me. Great to be here.

Great. Maybe just to start off, maybe we can talk a bit about just the main use cases that Appian involves and, kind of, run through the high-level story for Appian today.

Yes. So Appian has been in business for 27 years, and what we do is we automate complex business processes. So that's a lot of words. So I'll give you some examples. We automate fraud prevention for financial institutions, mortgage or health insurance applications for health care and financial services companies, a variety of public sector use cases. One of my favorites is we run inventory of ammunition for one of the branches of the U.S. military, so that's as mission-critical as it gets. And what these use cases have in common is a few things.

Number one is they tend to be cross-functional. They tend to be mission-critical and accuracy is very important. They frequently involve the customer in addition to the company itself and accuracy is exceptionally important. And the other question that usually comes with that is like, okay, well, what does Appian replace? And the answer is there's always something in place. Sometimes it's just a paper process that becomes automated for the first time. Other times, it's a homegrown application that is now having trouble scaling or simply cannot keep the functionality what the business needs or it could be a rudimentary automation tool, third-party automation tool that, again, cannot meet regulatory requirements or security requirements or accuracy requirements. So then Appian steps in and replaces it. And the answer, whatever it is, is that customers use us in order to have greater flexibility, greater agility, lower cost and in many cases, direct revenue outcomes for using us, and that's been our story for the quarter century now.

Okay. No, that's great to hear. Maybe we can talk a little bit about just the demand environment and the business environment that you're seeing right now. I think the business has been accelerating for almost a year now in every quarter. Cloud has been accelerating in the past few quarters. Just what is driving that? And how do you think about those factors and sustainability of that moving forward?

Yes. So we are seeing a very strong demand environment, and it's really driven by our AI capabilities. So Appian has been out there for multiple years now talking about AI as this powerful technology. It has tremendous benefits, but it needs a framework to actually operate at scale. We talk about this as AI in Process. And now you've heard a number of terms that have emerged for this idea, whether you talk about a harness, whether you talk about orchestration layer, it's this idea that AI needs something in order to be effective and accurate at scale. And so because we've been out there, consistently talking about AI in this context and because our product actually delivers AI capabilities and functionalities and strong returns while adhering to accuracy and performance requirement that our customers need, which are highly regulated industries in the public sector, we're seeing the demand grow.

We're seeing strong pipeline that is converting, and I liken that to a wave. We have a wave of demand coming our way, and then it's our job to surf that wave or rather convert that demand into actual revenue. And we're doing a great job. Happy with execution, continuous improvements in sales productivity and you combine existence of demand with ability to close it, and that's been the story of Appian for the last couple of quarters and the story behind our acceleration that you mentioned on our cloud revenue in particular over the last couple of quarters.

Okay. I think we hear a lot about people adopting AI, people adopting agents. Can you just maybe help like crystallize a little bit more fully, like, what are, kind of, the use cases that your customers are using Appian AI for? And how do you think about the repeatability of some of those use cases to potentially spread more fully across the base?

Yes. So front and center for us right now is a use case or a functionality that we call DocCenter. So DocCenter is AI-enabled document processing at high accuracy and already a part of your process. So you're not separately processing your mortgage applications in one silo and then feeding it into some process. It's all integrated in a single process and with high accuracy and sort of ability of AI to reason and send for your next action, whatever it needs to be. The great thing about document processing is that it's a very horizontal use case. Every enterprise is drowning in documents of some sort. So it applies in every one of our verticals. It applies in every one of our geographies. And more importantly, everybody has more than one use case.

So an example that comes to mind for me is a health insurance company here in the U.S. that implemented its first DocCenter use case in June. And they already need to buy incremental AI usage just to satisfy their use case, and they're talking to us about the next couple of use cases that will only further drive the need for their spending on our platform. And so I think about DocCenter as, kind of, the tip of our AI spear, and that's probably a multiyear cycle because, again, doc processing is such a pervasive use case across enterprises and certainly in our geographies -- sorry, in our industries.

The next one behind it, I would say, is agents. So we GA-ed our Agentic capabilities last year, and we're seeing strong interest. It's obviously earlier on, but we're seeing, kind of, a very broad use cases from relatively straightforward simple agentic deployments to very complicated ones and very sophisticated ones and sort of across the board. So we're working this year to continue hardening the product to continue improving our ability to implement. And that's something that we're going to put more arrows and more focus on as we look into 2027.

So I think DocCenter number one, agent is, kind of, like, the next and the overlapping, kind of, driver of demand. And then the third one is various products that make it easier to build applications with Appian. So Composer is the one that we talked about. We also have a functionality called DevMCP, which allows you to use your favorite vibe coding tool and use that to connect to the Appian platform and build apps using natural language on the Appian platform. So what those products will do is they will just reduce the cost of building on Appian or rebuilding on Appian, and that will be an incremental driver of growth. Obviously, that one, in my opinion, is sort of the longest in terms of the duration and very, very large, but the one that's earliest on.

Okay. Maybe on the agent piece of it, I think that one is a little bit maybe earlier in terms of the adoption curve for you all. I think when we talk to CIOs and people, it seems like agents in their view is a little bit slower for adoption. But just maybe where are we in terms of that adoption curve? And are there certain agentic use cases that are starting to pop up today for you all?

Yes. So the challenge with agentic is that really -- that true agentic really requires the right use case. And what I mean by that is that it's a case in which you need adaptive reasoning, you have a sufficient amount of ambiguity that you both want and need to deploy AI more broadly. And you still do it because it is more cost effective than applying a human, but you need to make sure that it's accurate and reliable. And again, like the agents, you want them behaving under the set of rules that you are operating. So that's sort of the sweet spot.

But we see a lot of customers coming to us thinking that they have an agentic workflow, and we tell them that they're actually better off applying a more of a deterministic process with more narrow AI capabilities, which will be more accurate and less expensive. And that ends up being the better outcome for the customer. So it's not a traditional agentic use case, but it's AI-powered and it's very valuable to the customers. So again, like it comes down to ambiguity where a lot of context needs to be driven from multiple different places as opposed to 1 or 2, where incremental pieces of information are needed to, kind of, involve in step-by-step reasoning. That's where agentic is meant to be better used. And if you apply it more broadly, which some have done, not with us, but in general, you end up probably in a place where you're either spending too much money or you're not getting the accuracy that you wanted to get and then you end up going back to the drawing board.

Okay. And when people come back to -- go back to the drawing board, does that end up creating an opportunity for you to then step in and take on that use case?

Yes, absolutely. What I've generally seen is that a lot of the vendors that we run into, I feel like they've overpromised on AI early on. And this idea that AI can self-govern and just turn it over to AI and agents it will be fine. And so they're backtracking a little bit on that message. It helps our credibility that we've been pretty consistent and that we can then back up that consistency of message with the quality of our products, and that's why we're seeing the demand that we're seeing.

Okay. No, that's great to hear. Maybe, kind of, staying on this on the line of thinking, but maybe turning more towards the monetization opportunity with AI. Just how do you think about how that, kind of, develops for you moving forward? Is it more about tiering? Is it about consumption? Is it about incremental SKUs? Just how do you, kind of, think about that adoption curve and how the levers, kind of, evolve from here?

So there's multiple levers. The first one, I would say, is actually the tiering. So just to take a step back, we introduced tier pricing at the beginning of 2024, so we're 2.5 years into it. And basically, we created a standard tier, but to get access to our latest functionality and most importantly, our AI functionality, you have to upgrade to what we call the advanced tier and that runs you roughly 25% to 35% more. So just to have access to our in-production capabilities, you got to pay us out of the gate. And we talked about nearly 40% of our customers having some portion of their ARR on the advanced tier. And in Q2, we also mentioned that 85% of our new logos in the quarter actually off the street came in and bought the advanced tier, which I think speaks again to the quality of our -- credibility of our AI message.

And so our advanced tier ARR has been building consistently and will continue to do so. So that's step one. Still plenty of room to go. Step 2 is usage. So our advanced tier and other ways you can buy AI come with what I would describe as a moderate amount of usage included enough for a single use case. So for example, the health care company that I talked about, their first DocCenter implementation already depleted sort of what's included. So now they need to come back and buy more AI bundles from us, more usage. And as they implement the third and the fourth use case, then obviously, all that is accretive.

It's early days. Relatively few customers are at a point in which they need to buy AI usage. I think that sometimes in conferences like this, it's easy to forget that AI is still very, very early in terms of adoption really in enterprise when it comes to mission-critical applications, particularly enterprises that we serve. So it's early days, but it's building, and we think that's another medium-term lever of growth. And then the third bucket is we're going to play this game again. So above advanced tier, there's something called premium tier. Very, very few customers are in that level now. But over time, we'll start to put more advanced AI features there, and we'll ask for another 25% to 35% uplift. So we think that there's plenty of -- if you think about it both from the perspective of use cases and ability to monetize use cases, we're in early innings no matter how you think about it.

Okay. And on the premium tier, I guess, what are the capabilities that you get incrementally from -- versus the advanced tier? And I know it's still early, but like longer-term potential, where could that potentially go in terms of the penetration within the base?

So right now, it's a handful of features. Right now, it's more included AI usage. And so we are very much still in the seeding the market stage, which is what we're doing with the advanced tier. Once we feel like that game is largely behind us, which I'm not suggesting it's this year or next year. But at some point, we will start putting the incremental feature there. And then it will be sort of the next chapter of that story. But either chapters 1 and 2 have a long way to go.

Okay. That makes sense. I want to ask a maybe high-level question on AI and the opportunity set that you see here. I think we still get the conversation or the questions around like build versus buy and what that means within your -- the customer base that you serve and how they think about when it makes sense to use Appian, when does it make sense to use custom code? And how does that, kind of, view evolve over the next kind of few years? And it would be great to kind of get your perspective on what that looks like.

So thank you for saying custom code because I sometimes need to remind people that custom code has always existed. So it's not a new concept. It's not something that began with vibe coding, right? So you can always go hire yourself a bunch of developers at $250,000 a pop and have them build something custom for you. That's always been competition for platforms like ours. So nothing is really changing from the perspective of like this competitive force always existing. Okay. So now comes vibe coding or ability to use natural language coding to accelerate the process of custom coding, and it's very powerful, obviously.

We've heard the customers' position change, I would say, sort of in April, May of last year. And that what we were hearing before that is, these are interesting tools. We're learning what they're good for. We would never ever ever use them for something that is very mission-critical and core to our process, but there's probably other things around that this will be an interesting alternative for. To now the conversation changing a little bit of as exciting as those tools are at the beginning, you start discovering costs associated with them down the road. And the word maintenance suddenly starts coming up. One of our partners talked to me about -- and this is a large systems integrator who built like a meaningful app using natural language processing in the hopes of demonstrating internally and externally that it can be done and then it becomes a business for them. They soon found that this app requires a couple of dozen people to be maintained.

So -- and by the way, these are people who have far more technical expertise than an average enterprise. So for them to be in that situation, it shows you that this might not be tenable anytime soon for like a traditional enterprise, which is always struggling to get technical talent on board. So we see interest from customers. We see apprehension. If anything, the balance between apprehension and interest has switched, I would say, over the last 4 or 5 months more in the direction of apprehension. I'm sure they will have like a role to play, but we don't see them impacting how our customers make decisions about processes that they would consider deploying on Appian.

Okay. And at this point, it isn't having any impact in terms of like deal cycles or their decision-making to move forward with an application on Appian versus them choosing to try to build something themselves?

Not at all. Demand is strong and like deals are continuing as they have been.

And pipeline looks good.

Pipeline is very good.

All right. That's great to hear. Maybe shifting gears a little bit with this being a big Fed quarter, and I think that's about 1/3 of the business, something like that for you all.

Federal government, 25%, total government, low 30s, yes.

Okay. Just maybe we can talk a little bit about the demand trends that you're seeing within that vertical. I know that there's been a lot of automation initiatives with DOGE over the past couple of years. There's a massive $500 million ELA there as well. Just how do you kind of view that opportunity moving forward? And any kind of views for what that means for this quarter and kind of like what you're assuming in the guide at this time?

Yes. So there's been a positive structural change in the federal sector for Appian last year. And what I mean by that is the government is focused on efficiency. The government is focused on impact of its technology initiatives. Ongoing never-ending projects are out of favor and dealing directly with vendors who are going to sell you software and implement that software so you can deliver flexibility, agility, cost savings, whatever it may be, to your use case is what the government wants. And that's simply put us in a better competitive position than we've been before.

We are more frequently directly competing as opposed to resellers. There's greater focus on sort of the value of the software as opposed to just its price. And that speaks to the strength -- that speaks to our strengths. We've had a very strong year across the board in federal last year, including a strong close in Q3. And frankly, to me, the question was, is this a onetime thing, meaning the first year of the new administration? Or is this like an ongoing change? And sitting here 6 to 9 months later, I can tell you that it feels like an ongoing change. So that's why it's like not a onetime thing, but a secular demand driver. We expect to have a very strong quarter in Q3 in federal. That's part of our guidance for the year. And we expect to have a strong year in federal next year.

Okay. And I guess the Army ELA that you have, how do you kind of view, what that means and I guess, the repeatability of that with other agencies within the federal government at this time?

So it was an important milestone for us because it sort of demonstrates -- it's sort of like a physical manifestation of what I just talked about, like increased sort of visibility that Appian has inside a major part of the U.S. government. So it's a framework agreement to spend $500 million over the next decade, I guess, 9 years at this point with the Army. It's not a commitment, but it's sort of think of it as a purchasing vehicle that dramatically accelerates or simplifies ability to get new processes on top of Appian.

And it does 2 things. One, it has the potential and has demonstrated so far that the benefits of specifically pursuing new business with the Army. But more importantly, it also serves as sort of like a badge of distinction, if you will, with the rest of the -- not just the military parts of the government, but also the civilian as well. So if a portion of the government views us as such an important partner to be willing to create this framework, it just opens a lot of doors for us. And again, it speaks to the strength of the federal business over time.

Okay. No, that's great to hear. Maybe we can shift gears a little bit and talk a little bit about go-to-market at this time. I think there's been a lot of focus on incremental hiring and headcount and investing in the go-to-market and trying to drive more ramp coverage. Where are we kind of in that investment cadence and turning that spigot back on? And where do you kind of view the biggest areas for kind of incremental opportunity to go invest behind?

Yes. So let me take a step back and just talk about the chapter before the current one.

Sure.

So in 2024 and 2025, Appian was very focused on sharpening our focus at the high end of the market. So enterprises, large strategic deals, improving productivity of the sales org. And frankly, our sales org didn't grow in '24 and '25 because we were focused on improving our productivity and returns. And we've succeeded in that. And we showed some data on this -- in this regard at our Investor Day. But we've improved our productivity and returns and paybacks to the point where I was saying internally and externally, we've earned the right to grow.

And that's great because our sales force is tiny compared to the opportunity that we face, but you want to grow it once the returns warrant it. And then as you grow it, you want to make sure the returns continue improving or at least remain stable. So halfway through this year, we feel very good about that. We feel good about the performance that we showed in the first half. We feel good about the pipeline and the forecast for the back half. So we said to ourselves, okay, here's an opportunity to start hiring early for 2027. As we think about the back half of the year, you're putting together your structure, your territories, your comp plans.

So wouldn't it be great if we can get those people a few months earlier than otherwise in order to put them in a position to be ramping and to hit the ground running into 2027. And again, with the revenue outperformance, there was room in the P&L to do that while still expanding margins, and that's what we decided to do. As you think about where all these heads are going, the same places. There's no need to put them anywhere else. We have plenty of coverage opportunities in our core verticals. And so there's no need to -- anywhere near the need to try something new or dramatically different in order to grow the sales org. We can just keep doing what we're doing, serving the markets that we're serving for years to come and just keep growing our coverage in order to grow our revenue.

Okay. And I guess with these headcount additions, what should we -- I guess, is there a way to kind of like dimensionalize like how much we're growing this? And I guess, secondarily, there's been a big focus on rep productivity for you all. And like that chart that you have in the investor deck showing that trend and show that increasing. Like should we start to expect that to level off and maybe decline as those reps get up to -- get ramped up? Or just how do you kind of think about what that means moving forward here?

So I think the key thing to think about is balance, right? So you want to grow the rep headcount, but you want to make sure that you are doing it in a way that they are as productive as the base and not dilutive. And so you don't want to do too much at once because if you do that, then you will have lower productivity, you will have attrition and you'll have sort of the need to kind of reset and you don't want to put yourself in that position. Instead, what we're hoping for is consistency of growth.

So adding rep next year, adding rep this year, the year after and so on and so forth, while maintaining or improving average productivity over time. Because, again, like if you -- what we're trying to build is a consistent compounding return story that begins with revenue, goes down the margin, further down to net income, every metric per share, but it begins with consistency of revenue. So I think companies frequently make a mistake where they extend themselves for that incremental percentage point of revenue growth and take operational or execution risk. And instead, just given the size of the opportunity, we're going to grow at a healthy rate while at the same time, maintaining our productivity and expanding margins.

Okay. No, that makes sense. As you think about the partner opportunity, where do they kind of fit in into this investment cycle? And kind of where are we in terms of them pulling you into incremental opportunities and I guess, leaning into them a little bit more as a growth lever?

So there's a lot of opportunity to do better with the partners. First of all, there's a lot of implementation works that our partners already do, and we're happy for that to be the case because it provides them incentives to be sort of engaged. We are -- we have rebuilt our partner org and increased focus on key partnerships because we want to invest in people who invest in us. So it's better to have a smaller number of very vibrant partnerships than kind of like a peanut butter approach to a larger number.

Secondly, what we're seeing from our partner community is that they are interested in new ways of doing business with Appian because their traditional implementation business, as you think about it in the world of AI, there's a lot of questions around what that looks like. However, if there's ways that we can go to market together, produce solutions together, figure out new ways to share revenues, and it's all incremental to us because, again, they carry the distribution. So there's a lot of early discussions and excitement, particularly when you marry that with our AI products, which are resonating very strongly with them. And so that's another sort of opportunity for us over time.

Okay. I want to ask on maybe the financials a little bit, shift gears. It does seem like there's a little bit more balance today between investing and driving growth versus letting things flow down to the bottom line. So just how are you kind of thinking through that framework at this time? And what should we kind of expect moving forward here?

I would expect the balance to continue, and it's a keyword for us. So we're in a fortunate position to be able to grow revenue while continuing to expand margins, and we want to do both over time. We don't want to invest aggressively and then put the risk in terms of our productivity profile or our returns profile. And also, we don't want to start the investment -- starve the business from investment for like a continued improvement in margins, a rapid improvement in margins, but that reduces our ability to kind of drive long-term growth.

So this year, for context, we're going to grow revenue 17% at the midpoint of our latest guidance. We're going to expand margins by 200 basis points. That's up from 100 basis points original guidance while investing in the business while starting some of that hiring earlier that we talked about. And we're happy that we can do that. And by the way, both of those things, meaning revenue growth and margin expansion, have a multiyear runway for Appian, which I think is an appealing part of our kind of returns algorithm for our investors.

Okay. And I guess as a piece of that, is there a way to think through like free cash flow versus EBITDA and driving that conversion to free cash flow?

Right. So if you think about what's between EBITDA and free cash flow, we have interest expense. We've done some work this year in terms of refinancing our debt in order to get better returns. So -- but there's an ability to kind of dimensionalize that based on our financials, very limited CapEx here and there, we'll open an office, so not a big deal there. We mostly get cash upfront when we collect from customers. So that's offset some of those elements of the contraction. So like roughly speaking, they're going to be growing in tandem with each other.

And our most important sort of capital allocation commitment is to, over time, effectively return all of our free cash flow to shareholders in the form of share buybacks, and we are doing $100 million buyback this year, and we'll continue doing strong buybacks as we go forward. And that will more than offset the dilution that we have from stock-based comp.

Okay. And then I know you're leveraging AI more into the product set, but what does that mean, I guess, for internal usage of AI? Where are kind of the core areas where you're deploying that across the various teams and what that means for further expenses or where you can find more cost savings moving forward?

So the place where we're leading is within our R&D org, credit to our engineering team, they're seeing great products -- great improvements in productivity and more to come. They're, in fact, reimagining entire software development life cycle around AI, and that will sort of continue improving our engineering productivity. And what that allows us to do is for the same investment, put more innovation out in the market, which is great from the perspective of helping drive revenue growth while at the same time, driving operating leverage because if the expenses grow slower than revenues, and you get operating leverage.

We're having early successes, mostly actually using Appian platform itself across our G&A line items, whether that is in finance, whether that is in people or the hiring process or quoting processes, all have Appian AI deployed. I'm starting to see benefit. And what that allows us to do is, as the business grows, we don't actually have to grow headcount dramatically to support the growth in the business and some of those support functions. So those are some of the areas. There's plenty more that we can be doing. But again, it's all in the context of generating leverage, showing margin expansion while at the same time, revenue growth. And we have all the normal scaling tools, if you will, at our disposal, plus all the AI tools to kind of further cement that runway for us.

Okay. I want to ask a little bit on the competitive environment and what you're seeing there. I think there's a lot of organizations and companies in the space talking about agent orchestration or talking about this kind of like next phase of leveraging agents internally at enterprises. Just maybe what do you kind of see there? And how do you kind of -- or what do you kind of view as like the differentiators to what Appian does versus others in the marketplace?

So first thing I would say is we don't see any significant changes in the competitive environment. Occasionally, in conferences like this one, we get asked about the new players, about the model providers or do we see any of them as competition? The answer is no. And so it's the same competitive set that we've kind of faced over the last few years with one distinction, which is our win rates are significantly higher when AI is a specific factor. So when we get the RFP, when we get into the competitive situation and customer really cares about AI, which isn't always. But when it is, our win rates are significantly higher than they are on average, which again speaks to the credibility of our AI offering and frankly, the quality of the product.

When it comes to -- so we're still very much living in that stage, the stage of the first or the second use case, the stage of like getting agentic to work for the first time. So as you now try to imagine a world, which to us does not feel like it's happening in the very near term at all, some world of like agents in one corner for one company from a different corner in a different company and then needing for all those agents to communicate with one another, we have a lot of wood to chop before we get to that world.

When we get to that world, the benefit that we will bring to the table is the same benefit as when you deploy your first agent on our platform, which is guardrails and data access. So ability to operate in a framework and ability to access data across the enterprise without having to move it [indiscernible] anywhere, which is our data fabric offering. So we have an MCP offering, which will allow other people's agents to access data and processes that exist on the Appian platform. We expect to monetize that over time. It's very, very early. Customers are trying it. But as I said, it will take a while. But in a world in which like ultimately, what AI, ours or anybody else's needs is framework and data, we provide both and we expect to benefit.

When AI comes into the RFP, is the competitive set that you're going up against, is it any different?

No. So it's the large traditional competitors your ServiceNow, Salesforce, Microsofts of the world and it's automation players, most notably Pega.

Okay. Okay. That makes sense. I do want to ask about some of the use cases that you drive, I know we've talked in the past about like ServiceNow had this great use case in ITSM. They started as a platform, but then found this and started stamping that across the customer base. When you think about what Appian could do to find a repeatable use case, are there areas where you feel like that -- you feel like are better suited for Appian to do that and drive a similar level of success? Or just how do you kind of view that ability moving forward?

So for better or worse, we are exceptionally versatile. And so that makes it very, very broad in terms of what we can implement, but it does make the sales cycle longer and you need to sort of find a use case that is complex enough and sophisticated enough that it warrants an Appian platform. And so the way that, that plays out for us is that we have exceptional growth of our customers for many years after they get on our platform because we keep finding new use cases that we can deploy once we land in an enterprise. But again, every one of them needs to be -- needs to require flexibility and versatility as opposed to like a point solution that you can apply off the shelf.

So that does mean that our sales cycles are long, but it also means that our gross retention rate is exceptionally high because customers love our product and frankly, it becomes very sticky, and it builds a stable base over which we can grow over time. So the acceleration for us has not been that we figured out a way to run the flywheel faster. It's that we just focused on larger and larger deals. So the payback at the end of the sales cycle becomes bigger. And we think we can continue doing that, and that's going to continue being a driver of the growth as opposed to like productizing or focusing on specific features that kind of opens like a different angle that may come over time, but we think there's great benefits in selling the platform even if it does mean that sometimes you have a more complicated sales to begin with.

Okay. Last question here. As you think about model adoption moving forward and you think about what are the right use cases for embedding a frontier model within Appian or utilizing open source models, how do you think about what that mix looks like? And how do you optimize for some of the cost versus benefit dynamics there?

So first, we provide a tremendous amount of flexibility to our customers, where you can choose your model and not just for the entirety of the use case, but for the individual components of it. Historically, so far, because it's early days, we haven't found customers really availing themselves of that capability very much. They kind of go with whatever standard setting we give them for the most part. However, and you know this, the topic of AI expenses has definitely moved into the forefront for my colleagues sort of across the board.

CFO cares what you spend your money on. And after a while, it's no longer just a side pocket of money, it becomes significant. So as we see that happen and as we see customers having incentives to be more thoughtful, where do I really need the frontier model? Where can I go with an open source model or an earlier generation model? There will be another way for them to tweak value that they get from their use case, and we benefit either way. So we're happy to be sort of the agnostic layer that lets them pick the underlying LLM, and we benefit from the competition and innovation that way.

Okay. No, that's great to hear. I know we're out of time. So Serge, I want to thank you so much for joining us today, and great to hear from Appian.

Excellent. Thank you for having me again.

Thank you.

Thank you for standing by. Welcome to the Appian Second Quarter 2025 Earnings Conference Call. At this time, all participants are in the listen-only mode. After the speaker's presentation, there will be a question and answer session. Ask a question during this session. You'll need to press star 11 on your telephone. You will then hear an automated message advising your hand is raised. To withdraw your question, please press star 11 again.

Please be advised that today's conference is being recorded. I'd now like to hand the conference over to your first speaker today, VP of Investor Relations, Jack Andrews. Please go ahead.

Good morning and thank you for joining us. Today we'll review Appian's second quarter 2025 financial results. With me are Matt Calkins, Chairman and Chief Executive Officer, and Serge Tonga, Chief Financial Officer. After prepared remarks, we'll open the call for questions. During this call, we may make statements related to our business that are considered looking. These include comments related to our financial results, trends and guidance for the third quarter and full year 2025, the benefits of our platform, industry, and market trends, our go-to-market and growth strategy, our market opportunity and ability to expand our leadership position, our ability to maintain and upsell existing customers, and our ability to improve and our ability to acquire new customers. These statements reflect our views only as of today and don't represent our views as of any subsequent date.

We won't update these statements as a result of new information unless required by law. Actual results may differ materially from expectations due to the risks and uncertainties described in our SEC filings. Additionally, non-GAAP financial measures will be discussed on this conference call. Reconciliations of GAAP to non-GAAP financial measures are provided in our earnings release. With that, I'd like to turn the call over to our CEO, Matt Calkins. Matt?.

Thanks, Jack, and thank you, everyone, for joining us today. In the second quarter of 2025, Appian's cloud subscriptions revenue grew 21% to $106.9 million. Subscriptions revenue grew 17% to $132.7 million. Total revenue grew 17% to $170.6 million. Adjusted EBITDA was... was 8.1 million. Last quarter I shared two metrics that measure Appian's progress towards efficient growth. The first measure is the productivity of our sales and marketing expenditure.

In Q2, Appian's go-to-market productivity ratio was 3.3. You can see on slide 4, that's our eighth sequential quarterly increase. And I believe more upside ahead. Our weighted Rule of 40, which expresses our strategic priorities by weighting cloud subscriptions revenue growth twice as much as adjusted EBITDA margin. was also up slightly to 31. We're pleased with our second quarter results. I'll briefly mention two reasons why they are good. First the internal factor, our upmarket strategy is working. by strong sales organization and execution, we are reaching the high-value transactions where Appian belongs.

Second, the external factor, artificial intelligence. Our platform gives AI the things it needs, like data access, structure, guardrails, and tracking. so AI can solve complex business problems. AI is having a tangible effect on our financial results. getting higher prices because of AI we add a 25% up charge we're in new deals because of AI and even new industries. I'll talk about that in a moment. But one more point about it. Whatever AI has done for our revenues, it's done more for our pipeline. And whatever it's done for our pipeline, it's done even more for our value proposition.

So I see this being a strong growth factor in the future. Speaking of growth, most of our seven-figure software deals signed this quarter were with our AI-inclusive license tiers. I'll share two examples of AI impact in big applications for big customers. First, an international grocery retailer and seven-figure ARR customer manages supply chain logistics and insurance claims with Appian. In Q2, it deployed Appian AI into an existing field dispatching application built on our platform. Before that, it was a business-wide business. For AI, drivers filed paperwork when they encountered a shipment issue and back office workers manually recorded discrepancies before correcting the information and re-issuing a new dispatch order in their Appian application. managing these expectations was slow, and there were sometimes human errors.

Now, drivers upload their paperwork into Appian, and our AI automatically reconciles the information. It's faster and more accurate. Second, a top global asset management firm and longtime Appian customer has deployed our platform across its enterprise. It runs dozens of Appian applications. This quarter, it upgraded, purchased a seven-figure software deal to upgrade its licenses to deploy features like Appian AI into areas like its client investment operations. Appian AI Agents will accelerate the processing of customer requests. Agents will classify forms and extract data related to opening, closing, and changing accounts. Turning to the U.S. public sector, our performance in the first half of this year has been strong. our federal business outgrew the global business in cloud revenue, in new bookings, and in software pipeline.

We have a reputation for driving efficiency in a sector which now prioritizes efficiency higher than ever before. We're seeing some good opportunities. A U.S. agency supporting national healthcare is unifying its enterprise, and in Q2 it chose Appian as the backbone to all virtual care operations and signed a seven-figure software deal. Millions of patients will use our platform to engage with clinicians, coordinating virtual appointments and sharing health data in real time. The agency expects to save $38 million per year using Appian. We've been using the phrase cautiously optimistic in quotes all year. to describe our expectations for the federal business in the face of doge and other volatility. And I'll stick with that wording. But looking back, our cautious optimism has been validated by results. we see a large opportunity emerging in the modernization of legacy applications.

We've been modernizing applications for a decade already, but the industry is about to transform as AI lowers the cost of extracting old applications and translating them into a new format. Businesses modernize applications to reduce cost, eliminate technical debt, improve functionality, and unify silos. Let's start with an example. Aviva is a multinational insurer that consolidated 22 legacy call center systems into a single application. achieved 40% cost savings and the ability to service customers nine times faster. Now that AI makes it easy to achieve modernization, the industry is set to grow. Modernization was the hottest topic on my latest customer tour, and generally, customers brought it up themselves. This industry is going to be big because each major organization, every major organization around the world, supports hundreds or even thousands of applications at great expense. and they would rather have fewer. And they wish they were better integrated. and they regret the data incongruity, and they worry about the long security perimeter, and they want to access them all in modern ways.

And nobody likes silos. Silos are just the way applications are laid down, as IT departments solve one problem at a time. But it's not a good way to structure an enterprise. Appian brings three powerful advantages to the revitalized field of app modernization. First, our platform is a great destination for translated applications. It's full of powerful pre-written functionality. It's secure, reliable, enterprise-grade. Second, recreating an application in Appian is a dialogue, not a delegation. We manage a multi-step dialogue between the designer and the AI.

The AI presents the designer with proposals, like for the interface or the data structure, and the designer can modify them. the designer is satisfied, the AI builds the new app. And even then, the app remains highly modifiable in Appian's process modeling interface. Third, Appian consolidates many applications into one. The modernization process is a unique opportunity to consolidate old applications into fewer new ones that offer the same functionality in a more coherent way. Last quarter, a customer asked me if we could translate 3,000 old applications. He didn't want us to give him 3,000 new ones. Appian is built to unify functionality and data into a combined application experience.

I love this modernization market for its scale and universality, and also because Appian's advantages won't be easy for rivals to duplicate. Another example, a leading Spanish bank is running a large-scale modernization campaign to decommission inflexible technology. In Q2, it purchased thousands of Appian software licenses and became a new customer. migrate all back office workflows from legacy tools and consolidate them on our platform we expect the bank will run core processes 30% faster and save millions of dollars annually with Appian last customer example a prominent US health insurer is undergoing a company-wide initiative to consolidate its tech stack and save $1 billion. It selected Appian two years ago to modernize its core applications and deployed a single application to unify its previously dispersed approval process for prescription fulfillments. In Q2, it signed a seven-figure software expansion deal to deploy Appian across its business starting with Medicare and Medicaid enrollment. Finally, I have one personnel announcement. Last month, David Crozier joined Appian as our new Chief Marketing Officer.

David holds a deep understanding of enterprise software and AI and brings decades of experience leading marketing teams and scaling operations globally. I'm excited for him to join our team.

With that, I'll turn the floor over to Serge. Welcome, Serge. Thanks, Matt, and thank you everyone for joining us today. Since this is my first earnings call as Appian CFO, I want to take a moment to share my reasons for joining Appian and the opportunity I see ahead. First, our product is great, which is reflected in our strong retention rates. I have consistently heard from our customers that they are happy with Appian and want to find ways to do more with our platform. That satisfaction is a great foundational asset on which to build the company. Second, Appian's AI value proposition resonates in the market.

Enterprises are wary of AI hype, and want to deploy this technology in ways that are safe, compliant, and most importantly, generate tangible value. Appian's focus on deploying AI agents within a process achieves just that. Third, Appian is focused on efficiency, as evidenced by an impressive improvement in profitability over the past 18 months. Since joining, I've seen the work done behind the scenes to improve our processes, systems, and execution. We're building a strong foundation that will help us drive efficient growth going forward. Finally, and most importantly, Appian's culture deeply resonates with me. Appian's values are intensity and excellence, and those are also my personal values.

This team is ambitious and wants to win, and I'm excited to be a part of it. Now let's turn to our Q2 results. Appian exceeded the guidance ranges we provided on our key metrics of cloud revenue, total revenue, and adjusted EBITDA. We had a strong quarter of new business signings due to continued momentum at the high end of the market and the AI demand, as Matt mentioned in his remarks. Cloud subscription revenue was $106.9 million, an increase of 21% year-over-year. Total subscription revenue was $132.7 million, an increase of 17% year-over-year. On a constant currency basis, total subscription revenue grew 14% year-over-year.

Professional services revenue was $38 million, up 13% compared to the second quarter of 2024. As a reminder, services revenue can be variable quarter-to-quarter. Subscription revenue represented 78% of total revenue, compared to 77% in the year-ago period and 81% in the prior quarter. Total revenue was $170.6 million, an increase of 17% year-over-year. On a constant currency basis, total revenue grew 14% year-over-year. Our cloud subscription revenue retention rate was 111% as of June 30, 2025, compared to 118% a year ago and 112% in the prior quarter. Our international operations contributed 38% of total revenue unchanged from the year-ago period.

Moving down the income statement, I will discuss our results on a non-GAAP basis unless otherwise noted. Gross margin was 75%, unchanged from the year-ago period, and down from 78% in the prior quarter. Our subscription gross margin was 87%, compared to 89% in both the year-ago period and prior quarter. Professional services gross margin was 33% compared to 30% in both the year-ago period and prior quarter. Total operating expenses were $122.7 million, flat with $123.2 million in the year-ago period. Adjusted EBITDA was positive 8.1 million versus our guidance of negative 5 to negative 2 million, and compared to an adjusted EBITDA loss of 10.5 million in the year ago, This outperformance relative to our guide was largely driven by greater than expected revenue as well as timing of certain expenses which we now expect to incur in the second half of this year. Net income was $0.3 million, or a break-even per diluted share, compared to a net loss of $18.2 million, or $0.25 per share for the second quarter of 2024.

This is based on 74.6 million diluted shares outstanding for the second quarter of 2025 and 72.3 million diluted shares outstanding for the second quarter of 2024. Turning to our balance sheet, as at the end of Q2, cash equivalents and investments were $184.8 million compared to $159.9 million at the end of last year. For the second quarter, cash used by operations was $1.9 million, compared to $17.6 million cash used by operations for the same period last year. Turning to guidance, for the third quarter of 2025, cloud subscription revenue is expected to be between 109 and 111 million, representing year-over-year growth between 16 and 18%. Total revenue is expected to be between 172 and 176 million, representing year-over-year growth between 12 and 14%. Adjusted EBITDA is expected to be between positive 9 and positive 12 million. Non-GAAP earnings per share is expected to be between 3 and 7 cents.

This assumes 74.7 million fully diluted weighted average shares outstanding. For the full year 2025, we're increasing our guidance for cloud subscription revenue, total revenue, and adjusted EBITDA. Cloud subscription revenue is expected to be between 429 and 433 million, representing year-over-year growth between between 17 and 18%. Total revenue is expected to be between 695 and 703 million, representing year-over-year growth between 13 and 14%. Adjusted EBITDA is now expected to range between 49 and 55 million. Non-GAAP earnings per share is expected to be between 28 and 36 cents. This assumes 74.7 million fully diluted weighted average shares outstanding.

Our guidance assumes the following. First, we expect professional services to grow modestly on a year-over-year basis for both Q3 and the full year. Second, we anticipate term license revenue to be flat on a year-over-year basis in Q3 and grow modestly for the full year 2025. Third, total other income and interest expense will be approximately $3.5 million in Q3 and for the full year 2025. Finally, our guidance assumes FX rate as of August 1, 2025. In closing, we're pleased with our Q2 results, and in particular with our ability to win new business. We're confident in the opportunity ahead and will continue to invest responsibly to maximize our long-term value.

and I will turn the call over for questions. Operator? Thank you. At this time, we will conduct the question and answer session. to ask a question you'll need to press star 1 1 on your telephone and wait for your name to be announced to withdraw your questions please press star 1 1 again please wait while we compile the Q&A roster Our first question comes from the line of Ramo Lenschao of Barclays. Your line is now open.

Perfect. Thank you. I've got two quick questions. One from Matt, one for Serge. Matt, if you think that dream or the idea of app modernization, as you said, has been around for quite a while, and AI should really help here, where do you think that dream is? Where are we on that journey though to kind of really get this to happen and how much will come from just one vendor rather than like tools from different ones? And then for search, can you talk a little bit about the NRR, the cloud NRR that kind of 111 kind of stepped down a little bit again? Are we finding the level here?.

Thank you. Yes, app modernization is going to be a much more complex market than it appears to be from this distance. Early in its conception, it seems like it may just be unitary, but it won't be. There's an extraction motion, there's an instantiation motion. AI can help with both of them. The first is more services intensive, the second likely more software intensive. We're obviously doing this market. We've been in this market for years, and we have a track record, and we're already a legitimate leader in modernization. But the game will change so much over the next year or two as AI is brought to bear on both of the two primary motions that comprise this market.

We're confident that we have something to say and can lead in both sides of that equation, and we're driving forward.

Yes, and hey, Raimo, thanks for the question. Let me jump in on the NRR rate. So let me say a few things. First, as we've discussed in the past, NRR is a helpful metric, but it has certain limitations. In my mind, most importantly, it's backward-looking, sort of averages growth across quarters, and obviously only reflects subsidence. of the business. With that said, the downtick to 1.11, I would contribute it to some of the same reasons we talked about in the prior quarter, which is kind of the ongoing effect of a couple of downsells that we've experienced in the past as they work their way through the system in this backward-looking metrics. I will also say that as we look at the composition of our new business, business in the first half of the year, a higher percentage than in the past has actually come from new customers. which we actually see as evidence of strength, our ability to land in these new logos with large and strategic mission-critical deals at the outset.

That's a strong sort of contribution, or strong testament to our value proposition. When you talked about sort of the metric bottoming out, you may have noticed that I did not mention in the script the range of 110 to 120% that we used to reference in the past. And that's not because we actually anything has changed in the business. We remain very confident in our ability to grow with our existing customers. But we're not referencing that range because we don't actually run the company to achieve an NRR level. We run the company to achieve total new business, whether it's on-prem or in the cloud, whether it's new or existing customers, but it's total new business that we forecast, that we discuss, and that we compensate people for. our metric is an output and we'll obviously continue reporting it, but it doesn't make sense to talk about the expected range because it's not actually how we run the business.

Okay, perfect. Thank you. Good luck. One moment for our next question. Our next question comes from the line of Keith Weiss of Morgan Stanley. Your line is now open.

Thank you guys for taking the question, sitting in for us and just saying this morning. I think this is similar to to Rana's question, but maybe a little bit more specific. So on the call, you talked about Appian's advantages that won't be easy for others to replicate in this market opportunity. But I think that's exactly what a lot of investors are worried about overall for software, but particularly for app development platforms and companies such as yourself, is that this view that generative AI, agentic computing, and these AI labs are going to be able to do more and more on a go-forward basis, automate more processes, and obviate a lot of legacy or existing vendors or even the SaaS layer altogether. Can you take in a little bit on sort of what those advantages are that Appian holds that you think are going to prove true moats, right, that aren't going to be able to be replicated by just agentic AI or kind of what the AI labs are doing to help us get and help investors get a little bit more comfortable about durability, if you will. Yes.

Yes, absolutely Keith, and thank you for the question. So I know a lot of people are worried about this, about how AI will be able to write applications, and they're concerned. They don't know how that's going to affect our market, but let me tell you, there are things that AI will absolutely not be doing. Appian comes with a built-out frame of functionality. and whether that's scalability or security quals or the ability to run on a mobile phone or all the features that come built in when you create an application in the center of our platform on the modeling environment. All of that comes with whatever app you put into our platform. And AI is not going to do that. AI is not going to write a hot, hot failover, for example, so that if the app goes down in one location, it automatically starts up in another location, a high availability kind of functionality.

That's a perfect example of something you would never get out of AI. Also, AI wouldn't be merging 100 applications into one like we're talking about. But look, the competitive advantage isn't just against AI, it's against our competitors. And we find that the direct large company competitors have a platform that's inferior to ours. Porting an old app into JavaScript or Apex code is not as good as porting it into a platform like Appian's that's easy to introspect, modify, and comes built out with all these features. And the startups aren't going to have the credibility to be used in major circumstances. And from what I've seen, most of the modernization opportunities are major circumstances with hundreds or thousands of applications for worldwide famous organizations.

They wouldn't be going with a startup. So there's either a platform problem with our directs or credibility. problem with startups, I think we've got a unique situation where we're large enough to be a credible player in this market, but also we've really invested in having a great process environment so that when you create an application on our platform, that's a fully featured, scalable, secure, reliable application in a way that our competitors and AI would be unable to construct.

Can I just also chime in because I'm new and I have some of the same questions and sort of an analogy that Matt uses was helpful to me. It's helpful to think of AI in the context of an application as an engine, but engine in and of itself doesn't accomplish enough or much. It needs a car to go places, and we are the provider of that in the context of security, safety, durability, accuracy, actually. And so that gives us confidence. that that gives us confidence that it won't change and that we have a durable advantage here. Yes.

Like take our data, it's an example, right? AI's not going to write a new data fabric that integrates all the data sources across the enterprise and automatically tunes your queries, tunes it, so that the queries that are asked most frequently get better performance. That's the kind of thing that you need a platform like Appian in order to do well. So, I know there's a lot of imagination about what AI is going to be able to create. And AI will create a great engine, but as Serge says, and as we like to say, it's good to have the car with that engine.

Excellent. That's a great analogy. And I think you're right, Matt. We're at a part of the innovation cycle and the hype cycle where there's a lot of broad aspirations of what AAL will have to do. So you guys bringing out analogs like the car versus the engine, I think, is really important to help investors in the marketplace understand. what's the right place that AI will go into. Serge, it's great to hear from you again in the new role. So congratulations on the new seat. I had a question more specifically for you. We're seeing 14% constant currency growth overall in Appian and flat OpEx growth.

And Matt was talking about some of the efficiencies you guys are already seeing in the business, particularly in sales and marketing, productivity from utilizing AI. Where are we in the Appian journey? Like how much more is there to go in terms of you guys garnering efficiencies out of of your own use of these technologies and getting those margins heading in the right.

Yes, so I would generally constitute it as we've made progress, but there's plenty more to go, and maybe I'll take a little bit of a step back. I commend Matt and the management team on the efforts that were put into place over the last couple of years. And really what the team has done here internally is focus on the areas of lowest productivity where the ROI wasn't there. and you've seen the improvement in margin. And that requires discipline and resolve. And once again, I'm happy to be in an environment that can do that. As we roll forward, I sort of see three key drivers of continued profitability and efficiency. The first one is continued improvements in sales productivity and the payback on our sales and marketing investment.

And it's very encouraging what we've been able to do here in the first half of the year, but obviously the game's still afoot. But we're optimistic about where we can go from here, and we'll achieve further improvements by improvements in our go-to-market process, as well as targeted incremental investments that will have a disproportionate impact on that sales and marketing payback. So that's bucket number one. Bucket number two is we have an ambitious product roadmap, but we can deliver it cost efficiently by growing our R&D base across the world and in particular in India. So we've made those foundational investments, I would say, over the last couple of years, but we're going to continue pushing in that direction. And then finally, to your point, we can use our own AI. We can eat our own cooking sort of across the company, and we're seeing some good early results in the context of some go-to-market functions, but we can do more as far as customer facing. We can do more as far as how we write our own code, and of course, in the back office as well. a lot of work done already, but plenty for us to continue doing here and to sort of balance that, find that balance between growth as well as improvement in margin.

excellent that's super helpful and uh congratulations guys on a solid quarter thank you.

One moment for our next question. Our next question comes from the line of Steve Enders of Citi. Your line is now open.

Okay, great. Thanks for taking the questions this morning, and sort of looking forward to working with you more moving forward here. I guess maybe just to start, just in terms of the contribution that Appian AI is maybe having on pipeline or maybe how is it changing the customer conversations in terms of how they're viewing, I guess, Appian as a key partner moving forward? Just what have you seen from those dynamics and is it having a, I guess, impact to, or how would you kind of frame the impact it's having to.

some of the demand out there for for Appian right now. Yes, I'd say it's a great driver for pipeline. We are seen differently by customers. We can show them that we can create far more value with AI than we could before. AI is a brilliant digital worker and we've been selling digital workers for a decade or more within our process model, but now we've got the best digital worker ever, and we can demonstrate how much productivity that can add. Also, our case studies are accumulating, and we've got a lot of great things to say to each specific vertical industry. We could talk case studies. We could talk our performance record.

We can talk expectations for each primary model that we frequently deploy of how much AI efficiency should be gained, how much time should be saved. We're showing that we understand better than others what can be done with AI in a practical sense, and it absolutely does change the conversation. So that's pipeline, that's bookings, that's revenue, and most importantly of all, and the precursor of all of that is its value proposition, which has changed meaningfully.

Okay, that's great to hear. Maybe just on the guide, it's a pretty healthy raise here, I can understand, you know, I How much of that is maybe a bit of a change in the guidance philosophy or the guide framework? Yes. here or is there some impact here from the change in FX rates? Can you just kind of help maybe walk through what's different today with the guide versus 90 days ago from the prior annual outlook? Yes, I'll jump there. So no change in guide.

guidance philosophy or how we think internally, frankly, about our pipeline and our ability to close. Matt has been talking for the last couple of quarters about the changes that we've seen this year in the macro environment and obviously some of the uncertainty that we have related to Doge. Although we're happy with our performance, no need to change the or the philosophy behind the guide at this point. It still feels a little premature. On FX specifically, what I would tell you, We, as we've done this time around, as a general practice, we use current FX rates when we provide guidance. And we also don't forecast where FX goes from wherever they are at that current moment. And if you look 90 days ago in early May, much of the dollar declined, which is beneficial to our revenue had actually already played out.

So FX was marginally helpful for Q2 and and it's a part of the sort of the increase in the guide but much of the increase in the guide really is about the fundamental strength that we're seeing in the business and the cautious optimism that Matt talked about.

Okay, perfect. That's helpful context. Thanks for taking the questions here.

One moment for our next question. Our next question comes from the line of Derek Wood of TD Cowen. Your line is now open.

Great, thanks guys. This is Cole on for Derek. Matt, I just wanted to double click on DOGE. And, you know, it seems like some of the initiatives have tapered back a little bit since 90 days ago. you know, can you just dive in on the federal pipeline and what you guys are seeing? And then maybe as well, how, you know, AI ties into this and how, you know, you're going and selling to them versus commercial. Thanks.

Yes, that's right. Well, DOGE may have died down a little bit, but there's fundamental undercurrents that have been started by DOGE that look to survive and shape the federal marketplace for years or decades in the future. Perhaps the most important of those is the disinterest that the government now seems to have regarding hire or spending through intermediaries. There's a far greater interest in doing business directly with Appian or with the software provider as the case may be and that allows us a greater degree of control, customer satisfaction and revenue involvement. And so that's a very good trend for us. Another one is the government's increased prioritization of efficiency, something for which we have long had a reputation in Washington. These developments are very positive for us. I think it just structurally changes the market in a way that ought to help, and our pipeline is healthy.

Great, thanks. And then just one more on pricing of the AI process. You guys had said that you're going to take a look at shifting pricing and then last quarter you said that You might migrate some customers on renewal to a new pricing structure. I mean, what's the update there? And, you know, do you have anything to add? Thanks.

Yes, that's right. Well, there's a long-term concern in this industry, just to flesh out your question, that since AI is going to reduce the number of users on any given application, that it might have a negative effect on the pricing scale that a lot of software vendors use. We sometimes price by users, but not always. We have a number of different pricing models. or flat app or all you can eat or consumption model. We do a lot of different styles of pricing, and it differs by region as well. And so we see the same problem. We are relying more on our other pricing methods, and we're doing a careful conversion, kind of a migration internally away from the seat-based pricing and toward a kind of consumption model. But that's a very deliberate, cautious, and gradual migration.

And we don't need to make any sudden moves because we have a set of pricing models that we can rely on. And maybe just I'll also chime in to say that a pricing model is ultimately just a way to get value.

and we're very confident in our value and frankly our customer seat as well. And leaving pricing models aside, we've been increasing prices successfully, just apples to apples, not new functionality, for multiple years now. And that ultimately tells us that our value proposition is strong and that we'll be able to get our fair share of the value we create going forward as well.

Helpful. Thanks, guys. One moment for our next question. Our next question comes from the line of Devin O. of KeyBank Capital Markets. Your line is now open.

Hi, yes, thanks for taking my questions here. I wanted to ask about some of the new sales leaders you have hired in the media in the quarter, in addition to the new chief marketing officer. Could you maybe elaborate on the appointments there? You know, what are you hoping these new leaders would bring to Appian? And are you expecting any, you know, notable changes in the go to market motion in that?.

region? Yes, the general trend across all of our go-to-market operations is one in favor of alignment, discipline, best practices, and all the hires we've made in EMEA and anywhere else for that matter are in line with that transformation. It's been going for a long time. a while. There is not a sudden change. We're just continuing to drive the strategy through aligned leadership across the organization. That's it.

Got it. That's helpful. And then maybe just one quick one for Serge. I know you mentioned some of the up performance in the quarter for EBITDA was kind of driven by some expenses shifting out to the second half. Could you maybe just elaborate more on what these are? Is it mostly headcount related? Any color there would be helpful. Thank you.

Yes, no headcount. It's marketing and some consulting expenses that we just sort of tactically moved from the second quarter into the back half, but it's a relatively minor contributor. We just want to kind of be transparent and give you guys the confidence so that you can understand how the guide moves versus the prior one.

Great, thank you. One moment for our next question. Our next question comes from the line of Jake Roberge of William Blair. Your line is now open.

Yes, thanks for taking the questions and Serge, looking forward to working with you moving forward. Good to see the continued productivity on the go-to-market side. Serge, you talked about this being a key area to drive more efficiency in the business. As you've looked at things, can you talk about what's worked for the company thus far and where you think think some of the low hanging fruit is moving forward. And then as you, are you starting to see your new AI solutions help drive faster decisions from customers, just given that the ROI for them might be a little bit clear as you're going to market with those? Yes, so a few things.

And by the way, thank you, looking forward to working together as well. So, again, if you look at the rearview mirror, we've removed some of the least productive areas of investment and channels, and that sort of helps productivity sort of in a mathematical way in that like it raises the average of the rest. And that's helpful because it saves money, but it's not sort of what's going to drive the business going forward. What we've seen, however, over the last couple of quarters, and in particular in Q2, is improvements in productivity driven by better execution and our move-up market. We're seeing bigger deals, we are seeing more strategic deals and that comes from our ability to take our great product, our strong relationships and marry them with improved execution and just get better outcomes. And honestly, it's my first quarter here and I was impressed with some of the deals we've been able to get in from the perspective of size, duration, names, structure, and And I think, again, that speaks to the Latin opportunity that we have here to monetize with our customers over time. So you should expect us to see more than that in the context of improving productivity, improving process.

Some of the leadership changes are going to keep helping with that front as well. And the way that that fits into the overall model is just the better the productivity, the more you can grow revenue while expanding margins at the same time. And so, again, early days.

you know, no victory to declare here, but some pretty positive signals. Okay, that's helpful. And then can you just double-click on what you're seeing with your public sector business? Things seem to be progressing really well thus far this year. But as we head into the third quarter, could you just talk about how conversations with customers are going, just given the larger Q3 buying cycle there? Sure.

Should I take that? Yes. All right. Well, I'll say that we are in a very, very good position to be able to do this. in healthy conversations and that we're pleased with the way the behavior of the federal government has changed in its priorities and its buying patterns but But beyond that, I think that's all I can add. Very helpful. Thank you.

I am showing no further questions at this time. Thank you for your participation in today's conference. This concludes the program. You may now disconnect.

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[Call has ended.]

Good afternoon. Well, thank you, everybody, for coming, and thank you for those watching online. We're very excited about what we have to show you here today. And obviously, we started with our favorite slide. So I'll let you speed-read this for 30 seconds.

Okay. The safe harbor out of the way, let me just run you a little bit through the agenda here. So welcome to Appian Investor Day 2026. We're very excited to have you here with us in New York.

You're first going to hear from Matt, our CEO. Then Sanat and Jake are going to go do a deep dive on our product platform and strategy. Then we're going to switch it up a little bit and introduce some outside voices. We're going to have a customer panel and also a conversation with PwC.

Then Mark, our CRO, is going to come on stage and talk to us about how he's driving an evolution in our go-to-market. Then I'll jump in with a little bit more insight into the finances of our business, and then Matt and I will wrap it up with Q&A.

So we have people in the room and online who are new to Appian. So let me provide a little bit of a snapshot. We were founded in 1999, which means that we've been automating processes for over 25 years, not too shabby. We crossed the $700 million revenue mark last year, and we have 140 customers who pay us more than $1 million per year in software, and you're going to learn more about them as the day goes on.

We're going to do approximately $100 million in EBITDA this year. It's a big milestone for us, and we have over 2,000 people globally who are Appiannits.

Okay. So we're going to throw a lot of slides at you, and you're going to have a number of people, Appiannits and external, talking at you. But what we're hoping that you get out of today at the end of the day is these 4 things.

First, Appian is mission-critical. We want you to come away with a deep understanding of how our customers use us, which is for complex, cross-functional, mission-critical use cases in regulated industries. And again, you'll hear that from us, but you'll also hear that from customers themselves.

Second, and you've heard us say this before, we are an essential AI enabler. We are that deterministic layer that AI needs in order to make an impact at enterprise processes at scale.

Third, we're continuing to drive sales efficiency. And Mark, in particular, is going to talk to you about this, but we're excited about the progress that we've made, and we believe we can do better.

And then finally, we're going to talk to you about our growth algorithm to drive productivity, sorry, profitability per share and the multiple ways that we can do that for the next several years.

And with that, I'll introduce Matt.

All right. Good afternoon. My name is Matt, Founder and CEO. I'm going to tell you what Appian does. Here it is. We're a worldwide leader in complex process automation. And because of that, we're able to bring reliability to AI in enterprise situations.

We've bolded a couple of words there, process and AI. I'm well aware of which one of those is trending relatively. But I'm going to start with process. I want to start with process for a couple of reasons because I think it's important that you know where we're coming from.

And also because I think until you understand what it is we've accomplished in process, the capabilities we've built in process, I don't think you can fully appreciate what we bring and the unique position we inhabit in the modern AI ecosystem.

So with your permission, I'm going to start with the basics. I'm going to start with process.

This is a process. You look at a flow chart. It looks like a flow chart. Some people call it workflow. That is an apt name because it is about the way work flows through an organization. It governs the flow of work. It starts and ends in circles. It goes through the diamonds and the work gets done in the rectangles.

Process is a concept that was invented for 2 reasons. The first is so that workers could specialize and do a certain job really well, so you could allocate labor more effectively. The other reason it was invented, and probably more pertinent to our discussion today, is reliability.

Process allows you to ensure reliability, even though the inputs may be flawed, someone might make a mistake somewhere in that process. But by the end, the answer is correct. The way we do that is we validate, we check, we escalate, we remediate, we check with the rule. There's so many things a process does in order to be sure that a mistake gets found before it reaches the output, right?

So you can think of process as kind of an error remediation system, a tool that preempts mistakes and makes sure that they don't go forward. That's your first clue, by the way, what the connection is going to be between process and AI because process was built to give humans a reliability upgrade. And these days, we have another technology that needs a reliability upgrade.

Right here, process, this is like a kindergarten version. Let me show you something that looks a little bit more indicative of an actual process that we would see in real life.

This is like the intake of – it was just going to animate, I thought. Okay, we were going to give you an animation. This is a process that's probably got 500-and-some nodes, and it does claims management, all right?

Now these nodes are not the whole story. Some of them may be subprocesses, which means that when you get into them, they reveal another layer of nodes. And that new layer could be equally as big as this one, could easily be so, right?

So a process like this is not the whole story, but this still has a lot of nodes. Here's another example. Now we have the flyby. Okay, this is going to show you what a layer in a process looks like. This one is actually the center of a logistics enterprise.

This is like a ticking clock that coordinates all of the handoffs, all the integrations between a worldwide logistics network, a lot of nodes in there, and yet that's not the totality.

In both cases, you saw 500-and-some nodes. That is not typical of an Appian process. A typical Appian process would be more like 6,000 nodes, all right? Now we're going to give you the flyby. A typical Appian process would be 6,000, 6,000, 7,000 nodes, and that's the average of our average customer.

But if you go to our high-end customers, the 7-figure customers, then you're talking an average of maybe 26,000 nodes versus the 400 or 500 that you just saw a moment ago. So even now, I'm not showing you the full thing, but I wanted you to get a sense of scale, just how complicated these things can get.

So working from the basic example all the way to this is the reality that we actually work with day-to-day.

So why would somebody use an Appian process to handle a behavior in their organization? Number one, if that behavior is complex, that's why they would use us. And secondly, and in parallel, if they needed it to be perfect, if they want absolute reliability even on a complex thing, that's our job.

Now in order to do that, we have a terrific data layer that informs the process and aggregates information and allows you access to it across the ecosystem and gives you read and write and it's secure and optimized. It's amazing. I'm not going to talk about it very much. It will come up later, but it exists, and that's another reason why people use Appian process.

And if you put together those things, the complexity, the total reliability and the unusually strong industry-leading access to data, you get the formula for handling mission-critical systems. Well, that's it. That's what we're for. That's what we flourish at, and it's no surprise.

I mean if you know that much about us, you wouldn't be surprised to find out that 80% of our customers are in highly regulated industries. Our top sector is government. After that, it's financial services, pharmaceuticals, insurance. This is where we thrive, and it makes all the sense in the world, considering the values that we bring to bear.

I'd like to walk you through 3 examples that show how this works, right?

This first one is going to be at a major financial services conglomerate. You know this company; I can't say their name. This is fraud management. We do their fraud management, which is to say we review millions of transactions, and we send high-volume, very detailed reports to the federal government on which the reputation of this leading financial firm depends. That's our role.

They used to do this through 6 legacy systems, a lot of personal time coordinating them all. There were major risks. There was risk of fines. There was risk of crimes happening. This was a very risky thing in the past.

We came in, we're handling it. At this point, the gathering of all the necessary data is taking on the order of seconds instead of just seconds. They've reduced by 98% the time it takes to process, and they've reduced their financial crime risk by 76%. I don't know how you measure a reduction in financial crime risk. This is their number, not ours, but it's a big accomplishment. We're helping them achieve their mission-critical needs.

Next example is a global pharmaceutical leader. We're doing their quality control for their medicines, which include debatably the world's leading pharmaceutical product. We are ensuring the safety of those medicines.

This process used to be extremely paper and human intensive. They had to get it right. There's no doubt about that. But they used separate applications. They merged it with paper, they merged it with human time, and it was so laborious because they had to get it right.

It was so laborious that it was a common occurrence for medicines to expire while still in the warehouse. They had to get it right. You can imagine why they did not go from that system to just turning some agents loose and seeing what happens.

This is the kind of thing you've got to get right. So they turned to Appian. We now automate this process and the work it takes to do a minor check has reduced by 95%, and we have reduced the time it takes to clear a batch by 65%. The accuracy is still there, and now they've got speed. This is a big accomplishment.

Third, out of 3, and I'm going to do 3 examples right now. This is the last one for the time being. This is a major branch of the U.S. military. We're talking about a provisioning system that we're running, which is to say this is the way that we deliver objects, weapons, data to soldiers in the field. This is the system by which they get it.

It used to be there were multiple systems. They were legacy. They were kind of disjointed. They would make errors, there would be redundancy. So you'd wait a long time to get what you needed in the field. And by the time you got it, you got 3 of them, right? That's how the system used to work.

They turned this over to us. We've now coordinated it so it's a lot faster, not just a little. A process that used to take 6 to 8 months is now taking under 2 days, and it's also accurate. So the data is better, the quality is better, the provisioning, the accuracy. It is just a major transformation in the way an exceptionally important process runs for this U.S. military branch.

Now our customer base, this is our customer base. This is who we do our work for. It's not just mission-critical work. It's mission-critical entities that we're providing it to.

We have 7 out of the world's top 10 pharmaceutical companies, 7 out of the world's top 10 insurers, 8 out of the world's top 10 non-Chinese banks, every one of the 15 U.S. governmental agencies, every one of the U.S.'s military agencies, the governments of 20 countries.

This is our customer base. And these are not trivial relationships either. These are deep, valuable annual relationships. That's the work that we do.

All right. Now that previous slide, that's the endorsement that means the most to me. But this is a nice endorsement as well. These are the world's largest analysts saying that we're a leader in whatever they call our industry.

They've got different names for it. I think Gartner calls it business orchestration and automation technology, which sells both. And the others have their own names, but they may not agree on what they call it, but they do agree that we're good at it. And we do have the unanimous agreement of the major analysts.

Now with that, I have come full circle and explained the process side of what we do. But please keep that in mind as we go forward into the AI side because it all depends upon what we've accomplished in process.

When we started, all the workers in this process were humans, which is to say that every one of those rectangles where a job gets done, the job was being done by a person. Now 10 to 15 years ago, maybe 20 years ago, that changed and it changed gradually over a number of years, but it changed a lot.

And it got to the point where digital workers were doing the jobs instead of people. And you had a mix. You had a team. There were lots of different digital workers. You had robotic process automation and API calls and business rules and of course, artificial intelligence, though this was before the heyday of large language models, it was still AI.

So we had all these different workers doing a job in tandem, and we found out right away that it really matters what job you give to what worker. Workers are good at different things.

At this point, it's almost a cliché to talk about probabilistic versus deterministic, right? I'm sure you've heard that like way too much. I'm going to have to explain it briefly, apologies, because it's important to what I'm trying to say.

Probabilistic is any technology where if you ask it the same question twice, you get different answers. That's totally important because it means it's not perfectly predictable. And if it's not perfectly predictable, it's not perfectly reliable.

So you've got a technology that, though it may be powerful, like AI, it can be powerful, it is not perfectly reliable. And in the cases that we've been talking about in those 3 case studies, in those customers that I put up on the slide, they need total reliability, just total reliability.

So this is an essential distinction. It's a profound difference actually between the 2, but I think it's largely understood. So I'm not going to spend much more time on it.

In fact, some people have gone beyond that conclusion and they say, not only is it obvious that there's a divergence between probabilistic and deterministic, but probabilistic technology like AI will require a deterministic layer in order to work reliably.

Now I agree with that. And I'll talk more about it later, but that's where the conversation has gone.

For the time being, I'm going to focus on the different kinds of workers and the way we discovered their capabilities when we put them into a process in the early days. We found that they sort themselves into 2 categories.

One of those, they are reliable. They do exactly what you ask them to do. However, they're not capable of reasoning their way through a hard problem. They do exactly the right thing, but they can only handle simple jobs.

The other workers are much more capable. They do reason, but they are not entirely predictable and also they're more expensive. Like AI and people can do great work, but they're expensive and a little bit unpredictable.

What the world is looking for, of course, with the AI revolution is, could we just take AI and kind of shift it into the middle. Could we make AI not just powerful but reliable? This is the million-dollar question, the trillion-dollar question. Can we make AI totally reliable?

And the answer is yes. Yes, we can. In fact, Appian is doing it. We're doing it all the time. And the key is the process technology that I walked you through at the beginning of this talk.

Let me show you how we're doing it.

This is a close-up of one node in a process model. As you recall, the work gets done in the rectangles. In this case, we gave the work to AI. What that means is we expect AI to be the worker that does the work.

When we put AI into a process node and delegate the work to it, here's how we do it. First of all, we make sure it's got a single job that AI is specialized at, the thing that it does.

We have a narrow group of inputs that come in and they're sorted according to the AI's likely ability to do that job. We give the AI a very narrow range of possible actions, not any improvisation, but a set of pre-collated, curated, audited behaviors that it's allowed to launch, which while narrow, are still extremely powerful.

We're auditing extremely closely, not just what the AI does and whether we think it's appropriate, but the net outcome of the entire case. If it touches AI, we look at the total outcome to be sure that AI is not correlated with inferior net case outcomes.

Whatever AI does, we're reconciling it with some other entity. It could be another AI run in parallel. It could be a person. It could be a rule, but everything is reconciled. Humans are in the loop, other things are in the loop. We're careful all the time about what AI is doing.

If we find a systematic problem, any kind of a deficiency, we're going to change the definition of the AI or we're going to route work away from it to take away the work that it's not doing as well on in order that the AI can exhibit top performance on the thing to which we delegate it.

I mean look at this, right? It's almost like we don't trust AI as a joke. We don't trust AI, but we know that if you put it in with all these restrictions, it's going to give you a great output. But you need to be careful. You can't just let it loose. This is the structure that can make AI reliable.

Now if you follow the literature, if you've read the conclusions of surveys, then you are aware that the industry has struggled to make value with AI. Study after study has shown that many organizations are getting not just a little but zero value from AI.

It's astounding actually. This is the greatest technology of a generation. And our economy seems to be having trouble finding value at all out of AI, particularly in high-value use cases, particularly at times that you're making strategic decisions or facing a customer or doing something where you can't afford a mistake, then it's enormously difficult to attach AI.

And it poses the question, and I believe it's the number one question of 2026 in business anyway. And that question is, how are we going to make value with AI in strategic applications?

And let me show you because I think we had the answer to that critical question.

This is our AI usage over the past 9 quarters. And I love this growth, and I think it tells a story. Q4 was bigger than all the quarters that came before it. Q1 is bigger than all of 2025 put together, right? This is an exceptional growth story here.

And as you look at it, please keep in mind who these customers are and what they're doing. Remember, this is 7 out of the 10 largest pharmaceutical companies, 7 out of the 10 largest insurers, 8 of the 10 biggest non-Chinese banks, every branch of the government, every branch of the military.

And by the way, you know what they're doing because you saw the case studies. They're doing the most mission-critical things, and they are the largest, most error-intolerant organizations. This is the cohort that is hardest to move to AI, and this is what they're doing.

40% of them are paying Appian for AI, 40% of our entire customer base, and their usage is going up exponentially. And that's how we're answering the question.

This is our ARR on the AI tier in our product. Again, terrific, terrific growth.

I have mentioned reliability as the core reason why you would use AI in a process and specifically in an Appian process. And it is the number one reason. But before I proceed, I want to explain that there are a couple of other reasons why you would want to use AI in an Appian process.

One is our unparalleled access to data. The Appian data fabric is really something special. I'm not going to talk about it now, but access to data. And for that matter, access to shared assets across the enterprise. Appian is really good at inventing and sharing shared assets.

AI, by its very nature, must be bottom-up. You give AI a job like build me this application or do this job. It thinks bottom-up. We think enterprise top-down. There's a fundamental difference and sometimes it's really important.

And then finally, there's data access, and then there's some applications that you just need human attention on. It could be because they're going to be reviewed by the government over the course of years to be sure they're absolutely perfect like FedRAMP and IL-6 and stuff like that. There's some code that you've just got to get right.

It's important to know actually, and I'm not sure there's been enough talk about this publicly, but there's an entire side to the software industry, the application creation industry, where the main cost of making an application is not the cost of the lines of the code, it's the cost of the mistakes if you get it wrong.

We tend to work on that side of the business. It's not about the lines, it's about the mistakes. Code could be cheap, but mistakes are expensive. That's the side of the business we work on, and it's an important side. I feel like it's been overlooked in the conversation of the last few quarters.

So that's important to us. When people say AI is going to write everything, I think, well, have you ever heard of a SIFMU, right, a systemically important financial management utility, right?

Not only can they not write their code with AI, they can't even write a spec without the permission of the government and a lengthy public review period, right? No way are they just going to publish an AI code.

There's a whole side of the software industry, especially the government, the regulated industries where what you publish is of utmost importance for its reliability. And that's the true cost.

And why do I know about SIFMUs and the lengthy regulatory process? Well, because we automate it, of course.

All right. So it's one thing to make this argument. It's one thing to say why AI belongs in a process. It's deterministic and Appian does all these things. It's one thing to say it; it's another to show it.

We felt it was essential that we demonstrate our thesis by choosing a solution that had universal applicability and demonstrating it to the world in action. And that's what we did.

We chose this one. It's called DocCenter. And basically, it's processing incoming documents. Every organization has this problem. Everybody's got thousands of incoming documents that they've got to read.

They could be registrations or submissions or regulatory checkups or complaints or policy changes or address updates or receipts or photographs of the crash to your insurer or whatever, right? Everybody's got a torrent of incoming documents.

So we thought this would be a great place to demonstrate how AI and process can make music together. And so we've built this product, DocCenter, and that's basically just exactly what it does. It takes all your incoming documents, no matter what shape or format, digital, physical, anything.

It takes all those, it parses them through AI, and there's a complex way we're doing it. We've got multiple large language models and humans working in the team. It's very kind of predictable. And then you get 3 things out of that.

You get uploads to your databases, you kick off any response processes that you need in order to react to the incoming stimulus. And then third, you make your response. And that's it. That's what DocCenter does.

But the statistics have been fantastic. And I mentioned a few of them across the bottom here, but this is just hardly scraping the surface. Hundreds of organizations are using this now, enormous enthusiasm around it, driving a boom in AI usage. It's been a terrific thing for us.

And I believe, most importantly, it's made our point about how AI and process together can do things that AI alone cannot because that's the core reason that we're doing it.

Take, for example, this life insurance company based in North America, which is using us for incoming document processing. They do 11 million documents per year. And it used to be that they were doing manual reviews and it was slow and error prone and they couldn't scale.

But now they've adopted DocCenter and they're processing 600,000 pages per month with a 75% reduction in review time and a 98% extraction accuracy.

So this is the magic of DocCenter. And there are so many examples, and many of them were on the stage at our show 2 weeks ago, and it was great to see the success stories that they proclaimed. Everybody on stage was talking about AI and about what we can do in a combination of AI and process, and it was great to see all that success.

Now we use agents. We have a special take on how to do agents. First of all, our agents are smarter, simpler and safer, and that's the core of our differentiated value proposition.

They're smarter because we have access to our Appian data fabric, and agents are as good as the data you give them. We have a terrific way of serving data to our agents.

They're simple because the intuitive interface by which you create and later define an agent is incredibly straightforward. You could do it in a couple of minutes, like 10 minutes max, and it's just as easy to revise it later on if you want to. And that, by the way, is with all the guardrails. That's not like fire and forget. That's with all the modifications and the safety and the tracking and everything. You could do that in 10 minutes.

And then third, it's safer because we monitor everything. In our process environment, we know exactly what every entity is doing, and that's just great for AI. You want that total numerical record in order that you can optimize it, improve it, track it, change it, reroute it, what have you. That's the environment AI thrives best in.

All right? So here is an agent example for you. It's the North America telecom provider, and they're doing wire installation processes for large housing communities. This is an integration-heavy, complex process, and it used to be done very inefficiently.

Now they've got Appian doing it with an agent, and they have 90% accuracy before involving a human. So the agent is doing an incredible swath of the job by itself, slashing costs, slashing time. They've got such savings and it's highly accurate. So that's our agents in action.

Now this is a really important point. This is something I wish that everyone understood. In fact, if there was one slide that I wish I could just show in Times Square and get everybody to totally understand, this might be the slide.

My point here is about application development, but as you'll see as I go on, it could apply to a lot of things that agents do. In this case, I've drawn a triangle. And that triangle represents all of the applications that your business or any business does. And it's sorted according to how much reliability you need.

Some of it requires only a little bit of reliability and some needs a lot. My scale for reliability is 9s, which is the customary scale for reliability, like 9s, like 99, 99.9, 99.99, right? That's how many 9s you need.

And some processes don't need many 9s and some of them need actually an incredible amount if personal safety or financials or something is on the line, if you're deciding who gets a job or sending astronauts into space, then you need a lot of 9s.

So vibe coding is good for some applications, but it's not good for every application, and you can't do it if you need a lot of 9s. This is a really important thing to realize. And it's just as true for work that AI does as it is true for applications that AI writes because writing an application is really like just doing the work in advance.

You're making the decisions, you're writing the script that makes the decisions instead of making the decisions in real time. But basically, it's the same thing. You're entrusting the decisions and you need reliability in some cases.

So what we've got here is a situation where vibe coding and AI generally covers part of the market, but stand-alone, it cannot cover the other part of the market.

And so that's where we come in. We are doing spec-driven development, which is to say that we can provide AI the reliability in writing code like we do provide it the reliability in doing work. It's not that different.

In both cases, AI alone can fill the bottom of the pyramid, but it takes AI plus Appian to fill the top of the pyramid in both cases.

And let me just wrap up by stating the obvious here, which is that the more reliability the job necessitates, the more reliability the job needs, the more likely it is that it is valuable. In fact, the correlation between reliability and value is so tight that you could basically consider it the same axis, which is why I've just labeled it value.

So we're not talking about some esoteric corner of the business here. This is actually the most valuable part of the business, where AI can't go, where statistics repeatedly show that AI has not gone, right? People are not using AI for this. They don't dare because they can't afford the mistakes.

This is where we can take AI. With our technology, we can take AI to places that it cannot go alone.

Let me show you more.

The mainstream way of developing in Appian is now something we call Composer, which is a natural language development methodology. I could say it's like Code-as-You-Go, but I would be more precise to say it just is Code-as-You-Go. Like we use Code-as-You-Go.

There are 2 differences between the way we build an application and the way you would work with Code-as-You-Go.

Number one is that the endpoint of the process, in our case, is an Appian application, not a code application. That's difference number one. And there's a number of advantages. There's a number of reasons why you would prefer an Appian application because it's a lot of prebuilt power. It's very strong. It's got data integrations with our Appian data fabric, we could go on, but there are a lot of great things about using the Appian platform. It's modern, it's updated. It works on all these devices.

Okay. So that's one reason. One difference is you get an Appian application instead of a code stack.

The other thing that is different between Composer and Code-as-You-Go is that before we write the application, we pause. We pause.

We say, and we show it to the person. We give them a complete and detailed dashboard and say, this is every last detail of the application that we are about to write. But we're not writing it yet until you check this and you agree.

Here's every role. Here's every user. Here's every screen. Here's every data table, here's every index, here's every rule. You audit it, you look at it, you share it around. When you are ready to go, then we build it.

But we have this moment of collaboration, this moment of togetherness and auditing to be sure it's right. And then after we build it, you can go back to that stage any time you want.

You can go back to that and say, okay, tell me the way it is right now, and I'm going to make a few changes. I'm going to tweak this rule. I'm going to change that interface. I'm going to modify this or that. You could just make the changes and you iterate.

You just cycle again and again through this exceptionally collaborative and ever-changing evolution of your application. So those are the key differences between us and Code-as-You-Go.

Now this is an amazingly powerful capability here, and it serves 3 purposes. The first is if you've got a new application, the first thing you do is write a spec. That spec becomes the incredibly detailed dashboard. That dashboard becomes your application faster than ever before, incredible time savings.

The second thing is you take a legacy app, we extract that into a spec and then we proceed as before, we make an Appian application to replace your legacy application. I'm going to talk more about that in a second because I think it's an amazing new possibility.

And then third, you continuously improve existing applications that were already in Appian. This is marvelous. It's going to keep our customer base up to date. I'm really excited about that functionality as well.

All 3 of these are game changers. But I'm going to drill into particularly the legacy apps concept for just a moment because it really represents a whole new horizon for us. And it could be an extraordinarily valuable application of AI technology.

Every organization I speak to has the same problem. They have thousands of legacy applications. They are out of date and redundant and insecure and trapping data and poorly integrated and hard to use and requiring training, and CIOs absolutely hate them, but they survive because it's expensive to replace them and they're worried about risk, right?

The old application may be bad, but at least it works. And if you change it, it might not work.

So that's it. The cost of replacement and the risk of replacement are the reasons those applications stay where they are.

Now AI has changed 2 main things about this. Number one, it's made it a lot easier, a lot more cost-efficient to make that change. And then secondly, it's made it more urgent to make that change because of applications like Anthropic's Mythos that can crack into existing applications.

It's one thing to have a modern application that's going to get a patch in the next month to be sure that it covers whatever deficiencies it may have. These old applications, they're not getting a patch. They're flawed. They've been exploitable for decades.

Like who has said, 70% of Fortune 500 applications are 20 years old or more. It's incredible how out of date these things are. They've been flawed for all that time, but nobody ever found how to get into them. Now it's going to take Anthropic's Mythos a couple of minutes, and they're cracked.

So the security perimeter is becoming a major problem. There is an urgency around this that there didn't used to be and organizations have to move.

And when they do it, they want to replatform. They're going to move a code stack onto a modern platform in order that it is going to be safe and maintained and modern in the future.

They want to improve the application, not just translate it, but make it better. And furthermore, they would prefer to consolidate.

And we can offer all 3 of those things, and we have. We've been in this market for a long time. We have a track record of being a leader here, though it was in the past a relatively small market.

But we've got a fantastic track record. We consolidated 500 applications at Hitachi down to 1. We saved the Air Force $80 million in the first year by consolidating. We've done some incredible things in legacy modernization.

And now we're ready to lead in the new version and the much bigger version of the legacy modernization market.

We have specific advantages here. One of them is that the platform we port the application to is a great platform. I'm talking about the Appian platform. All the capabilities that come in that platform make it a terrific landing point for migrating your legacy apps.

The second is that moment, that pause, that pause where you can collaborate with Code-as-You-Go and decide exactly the nature of the application. That's the moment where you can take a legacy app and make it better. You don't have to just reproduce your legacy technology on a new platform.

You can bring it up to date. You can make it modern and you could do so safely. And that's really an incredible gift.

And then third, we're capable of consolidating applications, like I just mentioned at Hitachi where 500 went down to 1. For all these reasons, we feel that we're a strong player in this market as it grows, and our technology is right up there with the best.

Here we're talking about a Fortune 500 insurer that does end-of-life insurance underwriting and application intake. It used to do this with a legacy portal. It replaced that portal with a secure, governed application.

It saved a great deal of time by doing the new application writing with Appian AI with Composer. And that's the reason I mention this use case because we were able to provide them tremendous savings and translate an essential application to a working format, a superior working format, in an efficient manner.

All right. So what I've now done is complete the entire circle. We talk about process. We talk about AI. We established what Appian's edge was in process, and we understood why that gives us a unique place in the expanding AI ecosystem.

AI is not stand-alone technology. It's probabilistic. It's not reliable enough. There's going to have to be an AI stack. The AI stack is going to have to include a deterministic framework that makes AI reliable.

We're not going to be the only player in that stack. In fact, when I look around, I feel like every tech company on the globe has a lightweight workflow layer, like literally everybody seems to have it.

But we're at the high end. We're not the only player at the high end either. But we've got some powerful technology. This is a meaningful, evolving market, and we're exceptionally well-placed for it.

We've always claimed a big TAM. We've always been in a big market, but that market gets bigger when you talk about the combination of process and AI. I like to say it doubles. Some people say it more than doubles.

I think our ability to add value to a customer has definitely doubled and probably more. And that's before you mention legacy modernization. I think legacy modernization is just off the charts in terms of the potential.

We have always fought over the very top layer in an enterprise, the last thing that they're building, like the newest thing, the latest initiative. And so we have clashes over the very most modern system.

But you talk about legacy modernization. Now you talk about every system they've ever done. We're not fighting for a single most new application, but now the mass remediation of all of their out-of-date and probably insecure applications.

This is a big prize. It is dawning right now. It is beginning right now.

And the thing that will set the trigger for this, that will make a small industry into a gigantic industry is technology. It's going to be who can deliver this safely. Not who's the first person to put an AI on the start line and hit a big green go button.

It's going to be who can deliver this with true reliability because the cost of migrating an application isn't the lines of code, it's the cost of making a mistake.

So we are as close to this, I think, as anybody right now, and it's a truly exciting prospect.

With that, I would like to hand the stage to Sanat to talk about our product. Sanat runs product for Appian. Please welcome.

Good afternoon. So great to see you all, and I'm excited to talk to you about our product and the platform that we have built that sets us up for being able to deliver these mission-critical use cases for the most complex customers on the planet.

But I'll start with a little bit about myself. So as Matt said, I'm responsible for the product here at Appian. And my career has been in complex B2B enterprise software.

Before coming to Appian, I spent many years at Oracle and then at Amazon Web Services, helping build very large businesses, working with some of the largest customers on the globe on very complex problems like supply chain systems and logistics systems, manufacturing, financial transformations, CRM transformations.

And so when I first spoke with Matt, what really got me excited about Appian was Appian has been maniacally focused on solving these really, really hard-to-tackle mission-critical problems for these customers, and we are really great at it.

And the reason why we are great at it is because we take this process perspective. And so I'm going to – Matt showed you this slide a couple of times, and I'm going to drill down into this a little bit. And I'm also going to give you lots of examples of why processes and solving processes is so important.

So if you think about technology investments that the large companies have made, they've spent hundreds of millions of dollars, sometimes billions of dollars, in technology transformation programs.

But if you go talk to a CEO or the Chief Operating Officer at many of these companies or the CFO and ask them, how did you do on the return on investment? Did you achieve your objective of transformation?

By and large, I think the answer is going to be, we got there part of the way, but we didn't really achieve our objective.

And so our diagnosis is that happened because they were automating transactional silos. But the real world doesn't work in silos. Real-world processes run across departments, run across systems. They don't really respect those boundaries.

And so you must take a process perspective to say, let me think about the end-to-end process, let me design it, automate it, optimize it from that process perspective. And so that's the approach we have brought to the table.

And Appian, for 25 years, has been really focused on bringing that process improvement mentality to all our customer engagements. And then again, as I said, we have gone after the most daunting problems these companies have had.

And then to be able to do that, you need a platform, and that platform needs certain capabilities. And so I'm going to walk you through these capabilities and why they are firstly so critical and then also why they are so difficult to replicate.

And so the first thing you need is you need the capability to design, automate and optimize processes. And then when you are doing that process automation, you really need a portfolio of capabilities. It's not just one thing that can solve it; you require a portfolio. So we'll talk about that.

And then for processes, whether it's people making decisions, whether it's systems making decisions or now AI making decisions, we all have heard about without good data, you cannot make good decisions. And that's particularly true of AI.

And so we'll talk about the investments we have had to make in building out probably the industry's best data layer. We call it Appian data fabric.

And then the next thing is once you develop these processes, what happens, they decay over time, so you need a process intelligence layer. And then last but not least, we are talking about some of the most important processes these companies have. And so you need an industrial-grade platform, the foundation on which to deploy those applications.

And so all of these things aren't built overnight. They are easy to think about and ask for, but they're really, really hard to build. And so let's go into each one of these.

So the comprehensive automation portfolio. A given business process, and this is an example of, say, an order-to-cash process, you're going to see so many different systems involved, in some cases, 50, 60, 100 systems that are involved.

And some of those systems talk to each other through APIs. Some of those systems talk to each other – there is no API. So you've got to figure out a way, such as robotic process automation.

And so we have a portfolio of techniques, technologies. Think about those as digital workers that need to come together. And so we call this ensemble, and this approach, process orchestration.

So what the process layer must do is it must decide how these systems talk to each other. So in many cases, you have got APIs. So you've got to have a robust API integration. And that has to be secure. That has to be scalable. That has to be very, very reliable. It has to be heterogeneous because these systems, legacy systems, have been built up over the years.

And so now you need to support sometimes hundreds of different standards. And that, again, is not that easy to build. It takes years and years of doing this to really get there.

So you've got those integrations. In other cases, you have lots of business rules. So if you think about an insurance claims process, what is my policy for approving a claim or rejecting a claim? What is the threshold?

And so these are deterministic business rules, and you need a robust, extensible way of defining those business rules.

And then you have robotic process automation for, say, mainframe systems, which don't expose their APIs. So you have to emulate human beings punching keyboards.

Then you now have AI and AI is playing an increasing role, whether it's machine-learning-based automation or now generative AI automation. And so that's becoming an important digital worker in business processes.

And then last but not least, people play a super important role because the types of customers we support in regulated industries, there is zero tolerance for error. So people now are – right, these are called human-centric workflows where it's human in the loop, and they are supervising what's happening.

And the process layer is what's making all of this work flawlessly right every time, every single time.

So to bring this to life, let me give you an example of a global European-based industrial conglomerate. And so this company manufactures very complex diagnostic technology, medical diagnostic technology. These things can cost tens of millions of dollars.

And the process they had before, their order management process where these orders were coming in through e-mail or their reps were sending those in. And it took really a long time for human beings to extract all the information, look at the bill of materials in the complex orders, check those against their standard product definitions to make sure that it was something they could fulfill, that it was compliant with regulations and then get those orders manually into systems.

So a lot of swivel-chairing going on. And as you might imagine, when you are talking about these complex medical equipment orders, you really don't want to delay those.

Not only is there a customer implication, there is also a revenue recognition implication. And this is what this customer was struggling with.

And so Appian went in and we put in an automated process with a variety of these digital workers to incorporate intelligent document processing to extract information from these very complex orders, a bunch of business rules as well as then people overseeing what was happening.

And then finally, the orchestration of APIs so that the data entry got automated. So these orders were getting created and then the whole supply chain started working very smoothly. And they were able to achieve 95% accuracy. We call it straight-through processing. And this was only possible because we had this portfolio of tools available on our platform.

So let's talk about our Appian data fabric. This is another capability that we have invested in for a long time, and this is a word that the industry has started using quite a bit.

And the reason why this is becoming so important is the processes, as we saw, don't really respect silos. They have to work across multiple systems. And the effect of that is the data is siloed.

In this case, we talked about the order-to-cash process. You have a CRM system that could be Siebel or it could be Salesforce or it could be SAP. You have an order management system that could be a custom-built system or it could be, say, Oracle Business Suite or SAP.

You have a manufacturing system that most likely is homegrown. You've got financials. Your compliance system might be something else. And so you have this data silo or a series of data silos.

And your process really has no way of pulling all that information together for automation. And so the typical way the industry solves the problem is, okay, let's go build ourselves a data lake, a data warehouse, and we will spend months and months building that.

The data will be together. That gets you part of the way there, right? Because now you have good reports, you have analytics, you have insights, but it still doesn't solve the process's requirement, which is I need to complete the transaction end-to-end. I need to go read from the source system, the system of record, which say for CRM, it could be Salesforce, and then I need to write back to it to keep that data consistency and integrity.

And so that is the problem that we solve. And the way we solve it is instead of saying, give me all your data, I'm going to go put it in a data lake or a data warehouse, which is what everybody wants to do. We took a very different approach.

We say we are going to create this virtual data fabric on top. We are going to layer on top of all these different source systems. And then we are going to do the heavy lifting of caching that data into this virtual database. And we will do it seamlessly. We will do it at very high reliability and very high scale.

And now you have all this information in one place for the process to make decisions. And guess what? Now AI can use that information to make great decisions as well.

So our Appian data fabric is very quickly becoming the essential context layer for AI. In the modern AI stack, the context layer is becoming an essential component. And our Appian data fabric kind of sets us up to do that, and our customers are beginning to use that at scale for that purpose.

As I mentioned before, this concept is becoming very popular. So all of our competitors have begun saying data fabric. We happen to have a true competitive edge here, hard to replicate, many patents on this technology.

And I would say there are 3 unique differentiators here. The first one is we create a semantic layer. You may have heard a competitor talk about ontology. And so the semantic layer tells you what is the data, what are the entities, what are the relationships, their description.

So for example, what is the standard definition of customer in my enterprise. So it really is the information model for the enterprise. And AI then uses that to navigate that data and then to be able to find the precise data and make the right decision.

The next I talked about is the read-write access. What read-write access provides is instead of having to consolidate all that information, we leave the information where it is, and then we read and write at the right time from the right data source.

So that also is very powerful. And then as you can imagine, it also gets you to the fastest way to AI value because now you are not spending months and months trying to harmonize data into a data lake or a data warehouse. Essentially, you leave everything as is and you layer our Appian data fabric on top.

And then last but not least, security and access control is paramount. And so just like you don't want any employee to go into your systems and have access to everything in your company, you don't want AI agents to have access to all the information. You want to provision just the data that they should be allowed to see.

And so our Appian data fabric has very strong data access control. So you can do row-level access control based on rules and then you can do that provisioning. So again, this is technology that works at scale with millions of records, and it's proving to be invaluable in our AI journey.

A way of bringing this to life is to talk about a large Japanese conglomerate. They're, again, a global company. And so their process problem was that they acquired a lot of companies, and they wanted to cross-sell and upsell to those.

The issue with that was a given account team could not get access to the right information at the right time. And so in that case, what they ended up doing was they layered our Appian data fabric across 500-plus source systems, and they were able to then create that single view of the customer.

And with that, they were able to kind of accelerate their process, save a lot of manual work and they say that their account and their sales teams got 50% more productive. So a huge outcome.

The next topic is process intelligence. And so when you create new processes, you want to continuously monitor those and visibility is a huge problem. And so with our Process HQ product, you can get visibility, real-time visibility to process performance, key performance indicators, where the bottlenecks are in processes, where that process is going to benefit from automation.

And then you can go precisely with the help of AI, remove those bottlenecks.

What's happening now with AI agents, and Matt talked about this, all the customers we talk to are really struggling to figure out what is the value that AI is creating? What is the return on that investment?

In our case, when you deploy those agents inside a process, then you are able to see, are they really adding value? Is it speeding up the process? Is it replacing costly human work? Is it replacing or getting rid of those inefficient loops in the process?

And so again, Process HQ is a technology that is super valuable. It was valuable before, and it's even more valuable in the age of AI and AI agents.

One example that I'll quickly touch on is this Latin American financial institution. They had a process problem where their compliance systems and customer onboarding processes were very slow, and they could not figure out why.

They were missing service-level agreements all the time. And they were using some other automation technology. So we went in, we layered in our Process HQ, and we were able to quickly diagnose for them where the service-level agreements were being breached.

And then with that precise information, we were then able to say, here is the automation that you need to incorporate. Using our automation technologies, the portfolio approach, we were able to address those bottlenecks. And then they were able to save 10 full-time equivalent resources, 2,000-plus days every year in just that one process.

Last, I'm going to talk about, and this is an important area, our enterprise-grade platform. And so the types of examples Matt talked about or I walked through, these are all customers where these processes are truly mission-critical.

If that process does not work or does not work fast enough or is insecure, it's a threat to those companies’ well-being, right? That's the definition of a mission-critical process.

And so it's easy to prototype with AI. It's really challenging to now deploy it in production, and here's why. For mission-critical processes to be reliable and dependable, you need, firstly, the scalability.

And so in our case, our platform supports scale. So for example, a funds processing company processes their 401(k) reconciliation process. And so that has to happen every month in a very, very tight window of time. And so you can't afford the system not to be available.

And so that scalability, our customers run billions of processes every month on Appian. It has to be incredibly secure. And so I'll talk more about this, but security, again, we have 30-plus compliances that are very industry-specific, including some of the – working with some of the world's most security-conscious customers in financial services, but also in the intel community.

And then the reliability. Our customers require five-9s availability. And so that system can only be down for minutes a year. And so achieving that type of reliability is super difficult to achieve from a technology perspective, and that's taken us years and years of investment to get there. And that's why these customers, the type that we talked about, come to Appian.

I talked a little bit about scalability. And so you had some numbers. Our AutoScale technology is able to automatically scale 10x to 100x from the baseline. And so as an example, there is a health care insurer who runs their Medicare enrollment process on Appian.

And so for that, again, in a short window of time, there is a lot of seasonality. So you need to be able to support that burst of workload. And so Appian does that.

Appian data fabric supports unlimited number of rows of data to be brought in and cached. And so as you deploy, for example, AI agents at scale, that capability becomes super important.

And then last but not least, sometimes these processes have tens of thousands of customers for any given app, and you need to be able to support that in a very performant manner.

From a security perspective, this is another one where it requires a lot of investment, a lot of experience to really get right. And so most software companies achieve compliance in the first column, which is SOC 1, SOC 2. It's pretty much you can't do business in the B2B world without having that. And so there are thousands of customers who have that.

But as you start going up that spectrum, the field starts thinning down, right? So if you want to do business, for example, with the Department of Defense in the U.S., you have to conform to something called FedRAMP.

And so there is a very specific set of controls that you've got to respect and you have to prove. And so that's FedRAMP Moderate. Again, several hundred providers have that.

But then FedRAMP High and then Impact Level 5, these are a very few select set of vendors who offer this. And what you get to do when you get to those higher levels is you get to connect your systems to the network of the U.S. government for the most secure workloads that they can imagine, right?

So these are intelligence agencies. This is the U.S. military, so on and so forth. And so that's what we have achieved.

And in fact, very recently, we launched the Appian Defense Cloud that is at Impact Level 5 of the Department of Defense. It's FedRAMP High. And we were awarded a $500 million contract to be able to now do business with the U.S. Army.

An example here is a branch of the U.S. military – they run their supply chain system for their arms and ammunition on Appian. And in this case, as you might imagine, they had a variety of systems. In fact, they have some of the most complex and diverse technology landscapes, and they wanted to consolidate that because these processes needed to work fast. This was mission-critical, right? Any delays were putting the mission at risk. It was putting our war fighters at risk.

And so they were able to use the Appian Cloud to be able to deliver this capability. And a big reason why they selected Appian was because of our security posture and our security capabilities.

So hopefully, I was able to convey that these capabilities are not that easy to build and replicate. And that's why these customers work with Appian on solving the most complex problems and then they stay with Appian for a long time.

And by the way, it also sets us up really to be a fantastic foundation for AI and to provide reliable, mission-critical AI.

And so to talk about that, I'm going to invite my colleague, Jake Rank, to the stage. Thank you.

All right. Thanks, Sanat. So again, my name is Jake Rank. I'm on the product team. I lead our portfolio for all of our AI and automation capabilities.

You can see I've actually had a very long career at Appian working with many of our most demanding customers in the field as part of our customer success team. So I've been out there working across the industry, seeing these processes in practice using our platform with our customers on military facilities, in banks all across different industries.

As part of my role in the product department at Appian, I've also worked across multiple parts of our product, building out some of those integrations, RPA, Process HQ and now especially focusing on the AI area. So I've seen many different aspects of the product, many different aspects of our customer base and processes that they run.

Now we talked earlier about how the way to get value out of AI is to put it into a process, focus it on specific activities, specific tasks where it can do its job with context and governance around the most important and critical parts of the process.

Of course, AI is only a part of our full automation suite. The right tool for the right job is a really important concept in Appian because you don't want to use AI where you don't have – you don't want more risk, more delays, more cost.

We have a complete suite of automation capabilities, so you can always pick the right tool for the right task. Now of course, AI has extended what we can automate, right? It's bringing more value because you can do new things that used to be done by a human and now we can take those tasks, whether that's a simple single-step task or whether it's an agent task.

I'm actually going to walk you through 3 specific ways that we use AI as part of our processes for our customers.

Now the first one I want to talk about is simply looking at individual applications of AI. Now we've been doing this type of AI work for years. We have taken different technologies, whether that's computer vision or machine learning or GenAI.

The technology doesn't really matter because what we've done is we package that technology into an easy-to-use capability that customers just drag into their processes, very easy to configure, don't have to worry about technical details. It gets a job done. It solves that problem. It automates that task.

We've designed a full suite of individual capabilities that allow people to easily automate those parts of their processes. So our knowledge about the process leads to our knowledge about the solutions, making it really easy to use.

Now one of our customers, a global truck manufacturer, uses Appian to automate their supply chain and production planning process. That means as they are looking at their supply chain, every day they're getting warnings about which parts might not be available or running low, all the different things about the supply chain logistics.

I guess you remember a few years ago with the pandemic, how much of an impact supply-chain logistics can have and how much of a negative impact it can have on manufacturing ability to deliver. So this is really important to get right.

Now they were using a manual process: coming in the morning every day, looking at all those warnings, trying to figure out what should we do about this? How should we handle this today? And they had to do it before the production team at the floor ran into certain bottlenecks.

So now with Appian, they've streamlined that process. They are now able to come in in the morning and instead of slogging through a bunch of manual processes and paperwork, AI has already done the hard work because we put that into the process to automatically process those warnings, to automatically process all the paperwork.

Their planners come in in the morning and can simply review and approve everything that they need to do to get the production line running. And that has allowed them to streamline their delivery of trucks by up to a day per vehicle and saving EUR 29 million annually.

Now Matt mentioned this, another place that we've seen the ability to deliver real value with AI is with document processing. Every process has documents. Documents are a hard way to get value out of. There's a lot of dark data inside a document. You need to extract information. You need to classify those documents. You need to understand what you're getting, whether it's a document, an e-mail, any unstructured text.

There are so many opportunities for where you can use document processing to improve the efficiencies and the outcomes from business processes.

Here, I'm showing, for example, an insurance underwriting use case, but it's every process. Every process has multiple places where documents are used and you need to be able to tap into the value that they hold.

So we created the DocCenter solution to package everything that you need in order to be successful with document processing into one easy-to-use package. With DocCenter, we actually use multiple technologies, GenAI, machine learning, computer vision layered so that you can get extremely high accuracy with a very low-cost effort.

Some other technologies might require you to draw boxes on thousands of individual documents or to maintain those templates over time, which really leads to a very high total cost of ownership.

But with Appian, we're very flexible. We're very agentic, dynamic. We get you to a high accuracy fast, and we keep you there even if you bring on a new vendor, even if you bring in a new business partner that might have a different format for that same type of document. We can adapt to those different document formats.

And then, of course, we make it easy to incorporate your document processing right into your process flow because it's all the same technology. It's all the same platform.

So just like we can drag and drop in a specific AI capability, we can now incorporate your document extraction process into your overall business process. And when you need to route to a human, maybe because it's low confidence or you can detect that there's an error, humans are always part of the process in Appian.

So you're never far away from having a human take oversight on a highly critical business process. And that's really important to a lot of our customers, along with those security compliance. Everything stays within the boundary of Appian.

So you can build as many of these as you want and never have to worry about, am I going to be compliant? Am I going to be secure? Am I going to have my data going out to some third-party provider and do I have to worry about what they're going to train their models on and maybe sell that model to my competitor?

You don't have to worry about that in Appian. It's all private. It's all inside the box.

Now you also noticed we got recognized by Gartner. They looked at our IDP solution, our DocCenter, and they ranked it the number one use case for automated processing.

So that's a good validation from Gartner, but I actually really like the validation that we've gotten from the many, many customers that are using DocCenter at very high volume, as you saw when Matt showed the massive increase in AI volume. That is the success that we've seen with these customers deploying, in many cases, DocCenter.

Here, we have a U.S. mortgage company who's using DocCenter to accelerate their post-closing audit process. So every mortgage that closes has to go through an audit process that involves 23 different document types and an Excel checklist that their users have to go through and check all these different rules, make sure that everything is in order, make sure all the dotted lines are signed, et cetera.

And if you guys have done mortgages, I mean, you know it's like a huge stack of paperwork, right? Different title companies doing different things. It's not all the same formats, they're scans, they're messy.

So DocCenter handles that with extremely high accuracy, and that's allowed them to increase their processing by 3x. They had a 45-day backlog when we started that project, and they've completely eliminated it. So they were headed in the wrong direction with the backlog, and now we solved that by streamlining their process.

And think about the fact that post-closing audits is only one small part of the mortgage process overall. Every part of that process has documents. Every part of that process is an opportunity for them to use Appian to streamline their business processes further.

Now we recently announced several big enhancements to DocCenter at Appian World 2 weeks ago. One, we use AI as a second reviewer. So when you extract information from a document in Appian, now we also use AI as kind of a second check, a second pair of eyes to look at what was extracted and see, is that a high-confidence extraction? Is that a potential error?

Now if it is, you can route that to a human because, again, humans are always part of the process in Appian. And if it isn't, you can have confidence in straight-through processing that document, which increases the value that you're getting out of the automation in your process.

Now we also take that feedback from AI as well as the feedback from human reviewers, and we combine those and use AI to generate automated recommendations that improve your extraction over time.

So you use DocCenter and it gets better as you use it. It helps you stay at a high accuracy even when business conditions change, again, even when you bring in new partners or new document formats.

So let's talk about Appian's agents.

Agents in Appian, of course, can think. They can look at data. They can look at the content that they're given. They can take action. They can then look at the outcomes of those actions, reason, learn from what they've done and then iterate so that they can rapidly adapt to different conditions. That's the real power of AI agents.

Now Appian uniquely brings our foundations so that our agents can be even better by using things like Appian data fabric that Sanat went into details on. So Appian data fabric, not only does it bring together data from across your enterprise, so you can tap into the broadest set of information to make the best decisions possible, but it also secures that data so that both humans and agents only get access to the information that they need.

So you don't have to worry about letting an agent loose in your ecosystem and wondering what data it's going to process, what data it might leak. You can secure data to an agent just the way that you secure data to humans.

Agents also leverage our process engine, right? Just like everything else, an agent can be placed into a process to do a specific task. Maybe it's replacing a human or augmenting a human that's doing that task.

Agents can also take advantage of calling processes. So an agent might decide at this point in this flow, I need to call a deterministic sequence of steps. Instead of letting the agent run 1,000 tokens to do that, you can simply run a process model.

You have tight control. Agents can decide when they want to be flexible and when they want to have tight controls. Maybe there's a specific step that requires a particular regulatory approach. The agent can take advantage of a prescriptive process to make sure that happens the right way every time.

And of course, that all happens within the context of the controls that Appian provides. So the guardrails, the cost controls, the visibility, the reporting and of course, the human escalations, the fact that humans can always review what an agent is doing and adapt the outcome as they see fit.

So here's an example of what an agent in Appian could look like.

Let's imagine that you're getting an e-mail and the e-mail contains a dispute for a credit card charge. Now you can imagine there's many ways that the customer might choose to identify themselves in that e-mail. Maybe it's by the e-mail address, maybe they included their account number. Maybe they attached a document to that e-mail, which is the statement.

Agents can adapt to all of those scenarios by looking at what they've got, extracting the information, maybe using DocCenter to extract the information from that attachment at high accuracy, then reasoning about the data that's available.

So let's look at Appian data fabric. What information do I have? How does that match what I could potentially query in Appian data fabric? We try to identify that customer. And I can repeatedly do that until I'm confident that I know which customer this is.

Then we can reach out and use business rules so that we can apply deterministic logic to help maybe route the flow further. We can even call other agents so that you can have a specialist agent maybe in a fraud review and one agent can call that agent to make sure that they're specialized in that job and we specialize in the overall processing.

And then you can call out to other systems to maybe do other fraud policy detections. You can call those process models to take deterministic steps, maybe writing data to other systems or to make a final decision.

So we overall have taken this nebulous incoming e-mail through an adaptive process. Our agent has turned that into a confident recommended resolution. And that's just one part of the process.

Now that can be routed downstream so you can actually take action on that resolution. So it all pieces together into the end-to-end business process.

Now our agents are accurate and reliable because we bring the right tools and context to bear. Agents are deployed in a process. That means that they're targeted at a specific task.

You guys probably all use some form of AI in your daily lives or in your work, right, like ChatGPT or Gemini or whatever. If you think about the context across your entire business, hundreds, thousands of people all putting information into that little chat window, and you don't have any idea what they're putting in. Bosses don't know what their employees are doing. They don't know what they're doing with the data that comes out of that chat.

That is a scary concept to a lot of people, especially in the industries that Appian works in. By putting the AI instead into a specific task, it's bounded, it's controlled. It does the task that you ask it to do, and it doesn't do all the other stuff that you don't want it to do. That's the power of AI in a process.

Now of course, our AI also takes advantage of our unified context layer. So everything that you build in Appian, every record in our Appian data fabric has metadata that describes what is that data, what's that field? How should that field be used? What are the valid values for this field?

That's the information that makes agents so reliable, so able to use the queries and the lookups in Appian data fabric. And remember, Appian data fabric is accessing information not just in Appian, but also across your enterprise.

You're plugging all of your major enterprise systems into our Appian data fabric, which means agents are able to use all of that information. And we do that not just for data but also for process and business rules and documents and integrations and other AI tools.

So everything that you have in the Appian platform is described in a way that allows agents to be effective using it. And it all benefits from the common security, the shared security model, the shared deployment model, the shared compliance and the shared data privacy so that you know that the information is your data and it's not going anywhere else.

So we have a customer who's a major Australian insurance provider, and they're doing IT case management for complex financial products. They have to adapt to constantly changing business needs. They're working with a lot of complexity and high-end customers. So they have to be very responsive.

Their IT case management system wasn't keeping up. They had to constantly define new workflows. And defining those new workflows meant that they had to get people together for hours at a time to decide how to process maybe even an individual request.

So they've now used AI agents from Appian as part of their process to take that time of planning and implementing new IT workflows from hours down to minutes.

So think about the way that makes their business now more adaptable. They're able to serve their high-end customers in a more reactive and quick way, which is critical to the way they operate their business.

Now one of the things that we recently announced was broad support for our Model Context Protocol. You guys probably heard about MCP.

Appian agents can now take advantage directly of any MCP tool that's put out by other customers, by other products in the ecosystem. Many other major products are putting out support for the Model Context Protocol.

Appian agents can now directly plug into those. That means our agents have even more access to data, even more access to take action within the enterprise ecosystem.

And everything that we have in Appian, all the data, all the process, all the records, all the business rules is also accessible via MCP, via the Model Context Protocol to other agents and other AI systems.

So when you build a process model in Appian, that process model immediately becomes a secure, deterministic tool that any agent that anyone is building can take advantage of.

So we are now a fantastic toolset if you're building an agent outside of Appian and our agents are even more powerful because we can tap into those same tools across the ecosystem.

Now we made a bunch of other improvements as well that we announced recently, including the ability to take these agents and actually embed them into an Appian UI and into an Appian form.

So now you can be working on a form and have an agent assisting you, an agent that can look up information for you, that can take action for you, that can even fill out the form for you.

So this is an incredible capability, and you can imagine what customers are facing right now with brain drain. Institutional knowledge is walking out the door every day.

Now you can bake that institutional knowledge into the agent in the form of documents, of policies and other data that you have in Appian data fabric. When you have a new worker coming out of training and they need to know how to do that task, they can ask the agent and the agent walks them through the steps.

It helps them get their job done faster, but it also helps them get the job done better. So remember, we're talking about critical systems, mission-critical processes. It's really important that people be able to ramp up quickly so there's no risk to the overall organization, and agents helping humans does that.

Agents also take advantage of the unified context layer like I described. And because we can provide feedback, just like with DocCenter, as you use agents, you give them feedback. Developers can give them feedback. LLMs and AI can give them feedback. Even end users can give them feedback.

All of that feedback is synthesized using AI and generates improvement suggestions for our agents. You don't have to be a prompt engineer. You just look at the improvements and iteratively improve your process, your agents.

We actually have seen, say, an agent that starts out 70% accurate go to 95% accuracy in 30 minutes of feedback. So that's a rapid increase that you don't have to know exactly what prompt to type in. You just rate it as a subject-matter expert and it gets better.

And of course, we've taken our AI guardrails and broadened them to the entire ecosystem so that now AI does the stuff that you want and none of the stuff you don't want.

Okay. So I told you I was going to tell you a number of different ways that Appian allows you to use AI in the context of a business process to automate the business operations of our customers.

We talked about being able to drag AI into a process, being able to do document processing with DocCenter and being able to use Appian's AI agents.

Now I want to pivot to the challenges that customers see even applying those AI capabilities because a lot of customers are not AI ready. Their systems aren't AI ready. They're being held back, as we were talking about, with all these legacy systems, right?

90% of their budgets going to the legacy rather than to the new things that are going to move them forward, the things that are going to differentiate them from their competitors. So there's an imperative to get out of this situation to unlock the secret that allows them to modernize these legacy systems.

And it's not just mainframes. It's not just COBOL systems. It's things that were coded 40 years ago. It's systems that some CIO picked 30 years ago and that have just lingered and been the core of the backbone of a business process in that company, even though nobody knows how it works.

That is such a major risk that we see. It's not just that these systems are old. It's not just that they cost a lot and they're not adaptable. They don't – they can't adopt modern technology.

It's that nobody even knows how they work anymore. And that is a huge risk. So it's all about risk. And our approach to managing that risk is through collaboration and spec-driven development. Matt talked about it a little bit before.

It's not just using AI in any form, it's using AI in a structured way, using AI first to go extract requirements from those legacy systems, so you know what the old system actually did. It's using collaboration on a plan that allows business and IT to work together to make sure that the plan that you're building for the new application isn't just replicating the old patterns, but it is actually optimized for what's available in the new modern system, that it's actually captured all the requirements correctly, that it's the screens that we want.

And then we use AI to go build the application on Appian, where it can execute, where it can run, right? Unlike building in cloud-only, we also run the application. So it's an immediate platform for you to execute the things that you build.

So I talked about the extraction, again, so important. You have these old systems. Nobody knows how they work. You've got to go in and find out how they work. You've got to talk to the experts, but you also have to take screenshots.

You have to understand maybe diagrams from 30 years ago about how this application was originally built. You can take even those spreadsheets, right? Everybody knows we have spreadsheets that run our business, right?

You can take that spreadsheet, you can upload it into Appian. It will understand how that spreadsheet is used, the data in it, the columns. It will reverse engineer the process, and then it will present the plan.

With the plan, you can sit down between the business stakeholders and the IT delivery and you can say, is this the right plan? Let's add some things. Let's remove some things. Let's change some things. Let's look at the screen previews that you're going to build. Let's look at the data model. How about the processes?

Everything about what we're about to build is now on screen and is there for you to be able to collaborate. You can change things, you can remove things. Again, it's a very important step to know what you want to build. That's actually true of all projects in IT, right?

They always say requirements is the most important part. That's why projects fail. This design solves that problem.

And then AI goes and builds it. It builds a fully functional, fully production-grade Appian application that has a UI, a data model, process models. It's got integrations. It's got decisions. It's got AI. It's got agents. It has everything that the platform does built in at the appropriate place with the appropriate requirements.

Now we've used Appian Composer at a global insurance broker. They have a contract life cycle management process that's on legacy software. They write contracts for $200 billion of annual premiums, and nobody knows how that system works.

So like, holy crap, right? Whatever – you know the adage, if it ain't broke, don't fix it. You can understand why nobody wants to touch that system, but the fact is it is broke because you can't modernize it, you can't take advantage of new technologies and you're at risk every single day.

So you need a confident way to modernize a high-risk, high-profile system. And that's what Appian delivers with Appian Composer.

So we looked at their .NET application. We looked at all the requirements. We talked to all the people. We bring all those things into Appian, build that plan. They collaborate on it. And now they have been able to build that forward with AI to a modern Appian application.

So high-risk stakes, large dollar amounts, minimal information about how it works today, but still able to be successful and highly confident.

Now we enhanced a lot about the way that we use AI in our platform when we announced at Appian World a couple of weeks ago. One of them is we released developer agents. Now not only can you build end-to-end, but you can also use individual developer agents to delegate step by step, which gives you even more fine-grained control over the implementation of your application.

And it's not just for building new applications. You can go to existing applications and improve them. You can do everyday developer tasks with the help of an assistant developer agent, which means every part of the Appian life cycle is now accelerated, and we deliver more value for our customers.

We've enhanced a lot of the AI planning capabilities with more document formats, more reasoning, gap analysis. You can ask what did I not think of? And it will perform a gap analysis against your requirements and tell you – it will actually ask you questions that help fill in those gaps.

So again, you're rounding out your plan before you move to development.

And then we talked about our process intelligence layer. We inject automatically all of the reporting, all the telemetry that's necessary. When you build an Appian application through AI, it gets all the things that are needed to report into Process HQ.

So you can do process mining. You can do bottleneck detection. You can do KPI tracking according to the things that matter to your business. It's all baked in. You don't have to take extra effort as a developer to put that in. It's automatic.

Now we think that Composer and dev agents are an incredible way to build in Appian. It's the future of Appian. It's how we're going to accelerate the value for customers across the board. But we also know that developers sometimes want to use their own tools.

So we've taken all the greatness, all the goodness of being able to use AI within the platform and also made it available using the Model Context Protocol to tools like Code-as-You-Go, to IDEs, to other environments.

So now development shops that want to use those tools can also build and deploy and run on the Appian platform with production-grade quality.

So I've walked through a number of the different things, both on the process automation side, how process and AI together are delivering more value and better outcomes for our customers, as well as showing you some of the details of how we're capturing that legacy modernization opportunity.

I want to thank you guys all for your time, and I think we're going to move to a panel with Marc Wilson in a moment. Thank you.

I'm one of the founders of Appian. These days, my job is to serve as Appian's Chief Executive Ambassador, which to me is a fancy way of saying you'll find me in an airport if you're looking for me.

I travel the world and get an opportunity to meet with our prospects and our customers and get to hear what their issues are, the things that they're trying to solve. And the best way I would describe what we're trying to do for them is to help them achieve strategic value in the face of a world where they're largely confronted by a lot of tactical value opportunities, particularly those that are trying to, in the words of their boards, "do AI."

So they're looking for more.

This afternoon, I have the pleasure of leading a panel with 3 of our customers. Guys, if you want to come on up. And we want all of you to hear directly from them about the challenges that they face and what they've taken on with Appian.

So thank you, gentlemen. I'd like to start with some basic introductions. So why don't you tell us who you are, a little bit about your organization and what your technology priorities are?

Okay. I'm Scott Morris. I'm the Chief Technology Officer of the National Association of Insurance Commissioners, kind of a mouthful there, the NAIC.

We are not a regulator, but we support regulators throughout the United States, state insurance regulators. If you don't know, insurance is regulated by each state, territory and the District of Columbia. And our organization helps them collaborate on policy, but then my main goal is to help them with technology and technology that helps them lower the friction to do business with them.

So helping insurance companies, we're kind of that hub in the middle. So insurance companies interact with us, and we provide data and information to the insurance regulators.

Our priorities for the year, strangely enough, modernization is a big key. I felt in good company when Matt mentioned 70%. So we definitely are seeing that 20-, 25-year-old applications that we've been working on, and we'll continue to do so.

Data and data platforms is key for us as well as improving our overall customer experience, going from a siloed experience to more of a uniform experience. And then the last thing I would mention is our ability to take – we've been doing a lot of experimentation with AI.

We are certainly seeing efficiencies, but the question is, are we really providing value to our members and to our customers? And that's – I don't think we're seeing that yet, and that's one of our focus points.

Great.

Yes, I'm Bob LaBaran. I'm a Senior Vice President at Neuberger Berman and lead technologist for our alternative technology business. So we support all of our private markets business across various investment verticals and strategies.

Keith?

Keith Koharski. I lead our global development organization from a digital and technology perspective at Regeneron, a pharmaceutical company, looking at how to bring medicines to patients.

So we all know the amount of time, the amount of capital it takes to prove drugs are effective and safe. So really, what we're looking for over the past couple of years is really how automation, how digitalization can be used across global development.

Why don't we start out by talking a little bit about some of the use cases you have for Appian? What's an example of something that you're taking on with that technology?

Perfect. I'm happy to start.

So one use case that we recently went live with and was actually one of the Appian Innovation Award winners a couple of weeks ago was our study co-developer.

So when you create a protocol for a study, you work through what your patient population, what your inclusion, what your exclusion criteria will be, looking at all historical data, looking at the markets, what countries you're going to go into, the number of sites you're going to go into.

That requires a lot of different data sets and a lot of different data sources, requires some internal data we have as well as some third-party benchmarking data.

That also works across various different groups inside Regeneron. So it's not just one function that's working through that. Rather, a function might take a portion of that protocol. They might give it to another function to fill out their portion and then have that debate back and forth.

The Appian application that we went live with helps combine all those data sets together, helps in what we call a single pane of glass, but really a single UI. So you don't have to Alt-Tab between 5 and 6 different systems.

You don't need to go out to a third-party data set, but you make the foundation of having that scientific debate so much easier. So instead of going back and forth on what data set you need, rather we are looking at the same set of data in the same UI.

And then you also have the orchestration layer and that workflow component. So I can do my portion, right? They can do their portion, and I can give it to Scott. He can do his portion. And in real time, we can collaborate together on that same document, being a much more efficient process.

How would you describe that process before you took on the orchestration-based approach?

It took a lot of manual effort, right? And at Regeneron, kind of our really focus on AI is – it helps with some of the automation. We don't feel it can take out the scientific judgment, right?

So I want to make sure we're using AI and using automation appropriately to help provide information, but ultimately, we're making the decision.

And that's a pretty regulated process in the industry. Tell us a little bit about your regulators and how they look at things like this.

Yes. So I think from a pharma perspective, about one-third to half of what I do on my daily basis is GxP, GCP, GLP validated. So different regulatory bodies will give specific guidance of how something needs to be followed.

So when we go through and we build applications like Appian, you need the basics and the essentials. So you need audit trails. You need role-based access controls.

You need to be able to trace down how a decision was made across what data set, across what program, to have repeatability if you need to make that decision again, if you need to understand what went into that decision.

So these are highly regulated, highly well-documented systems, and that's where you need to have an application, a platform as robust as something like Appian, so you have those constraints well thought out.

Yes. So we use Appian for a couple of key areas at Neuberger. So we use it for our deal closing workflow and also for our fund investor onboarding platform.

So we have 2 main applications, but they're really more a platform of micro applications, I guess you would say. So for our deal closing, it handles a lot of the pre-trade compliance checks that we have to do.

It also handles a lot of operational setup for our deals. It also handles other checks that we have such as ESG, SFDR. We have a lot of different checks that we have to do to make sure that we're in compliance.

For some of our investment strategies, we also have built what used to be an Excel-based allocation file for allocating a given deal to all of the different Neuberger funds that would like to participate in the deal. That's all now done in Appian.

And so that's been really nice to be able to see real-time the allocation status rather than having a bunch of different versions of the same spreadsheet floating around and having to deal with the version control and see all of the rules that we have in terms of who can participate.

Is this a good fit for them? Do the LPs or the fund of one have veto rights, things like that. So it handles a lot of those complexities.

And then on the fund and investor onboarding side, we handle all of our investor checks to make sure that – it's kind of a combination of managing the subscription docs, but then also the initial onboarding steps of are these docs in good order, have the AML checks been performed and so forth.

It also includes all of our fund onboarding as well as all the entity onboarding. And in private equity, this group may or may not be familiar, but when you are setting up a fund or a product, you're going to go out, you're going to go to market and you're going to say, I'm going to create this fund and gather investors.

Your investor base is going to be comprised of – could be U.S. investors, it could be non-U.S. investors. And based on the jurisdiction that they have, you're trying to minimize the tax liability so that people aren't double taxed, triple taxed and so forth. So that people are paying the taxes that they should owe.

And so as a result of that, you have a whole bunch of different entities that you are setting up. So a single fund structure could have anywhere from 1 to dozens of entities that are set up. So it's a pretty complex thing that we're working with.

And as a result of that complexity, both on the fund and entity side and as well as the deal side, when we were first evaluating our solutions, we were looking for, okay, do we have any internal solutions that we can leverage?

We utilize ServiceNow for a lot of our IT processing and SDLC, but didn't feel like that was going to be a good fit for what we needed to do, and there's nothing off the shelf that you can just go buy because it's such a bespoke process.

And we have so many different business lines as well. And some of those business lines have been developed internally, and they've kind of evolved organically as our businesses change, but some of them are stand-alone businesses that we said that's a good business model. Let's buy that, and that will become one of our investment verticals.

And for a lot of what we had in the past, the tech stack was Excel and binders. And so it's been much improved as we've started gathering all of the data and systematically moving it along the chain as we execute our deals.

Scott?

Yes. So we have a 20-, 25-year-old platform that we replaced with Appian. And it is – when you're an insurance company, about 4,000 insurance companies in the U.S. that are regulated, you have to go through your regulator or regulators to get a product approved or a rate increase approved.

And the state rules are different by each jurisdiction. And so largely, what we had in place originally was 20, 25 years ago, we automated scanning of documents and then workflowing those documents.

That was all fine and good. Obviously, we wanted a more automated process. And so that's when we took on to do this with Appian. And the 2 key things we're really trying to achieve here are regulatory consistency.

So somebody mentioned earlier that there are folks that are moving out of the workforce that's in a state Department of Insurance. That's certainly the case. Regulatory consistency was achieved through seniority and longevity of the staff there.

So we want to make sure that folks are making good decisions, making similar decisions on these product filings. And then the second piece is we want to speed this process up, and we're just beginning to see some benefit from this, but it takes about 40 days on average in the United States to get an insurance product filing approved. And that varies greatly by state, but we want to automate the process so that becomes faster.

So those were our 2 key goals. We used Appian to build out those workflows. A lot of it is consistent through all the jurisdictions, but each of those has kind of their own unique needs.

So that's one of the platforms that we've built out. And these are product filings. So there's 4,000 insurance companies. They make about 600,000 filings a year. A filing can have up to 100, 150 files. And so there's a lot of information flowing. We're just trying to contain that and automate that with Appian.

So on that topic then, Scott, what have you seen of Appian's AI features that can help with those processes? What are you doing today to look at getting that to go faster with AI?

Yes, that's a great question. The first thing we've done is automate the intake process. So we had actual people looking at, okay, they uploaded all these documents and then they filled out this form with all this metadata.

It used to be a regulator – that was their full-time job is to make sure that metadata matched what was in those documents. So now we're using Appian's AI capabilities, intelligent document processing, to pull that information out and automate that process.

The other piece that we've seen using – just recently moving to DocCenter is classification. I mentioned there's around 150 documents in these filings. It turns out humans aren't really good at classifying these documents.

With DocCenter, we're seeing about a 98% effective rate there. And that's sped up the process. So that's what we're doing today.

We are experimenting with more of a capability to review those filings. So every state has what they call a checklist, and that checklist is just natural language that says this insurance contract must have this particular exclusion or something like that.

And so what we're doing is using AI in a pilot mode right now to go through and parse that out and then show them where it meets their rules or where it doesn't meet their rules.

And once again, it's not just one set of rules, it's 56 sets of rules. So that's how we're using AI.

That's great. And Bob, I know Neuberger has been leading the charge on this for a while. I remember the presentation at Appian World last year. Tell us a little bit about what you're doing with Appian AI.

Yes. So we're kind of similar to what you mentioned. We use the intelligent document processing in DocCenter.

Specifically with our subscription documents, those documents are crazy long. They could be 200 pages, and they're bespoke. The documents could vary based on the jurisdiction, if it's Cayman, Luxembourg, U.S., Japan.

So those could all vary in terms of their complexity and length and sometimes even depending on a fund, could have a very specific page that's added into there. And so we're pulling in and extracting all of the data from our subdocs.

And what that's unlocking for us, too, is the ability to be able to extract the data more accurately so that we essentially have zero human data entry from there, but also the ability to be able to do checks that were perhaps not done consistently or could be done on a haphazard basis.

There's sometimes questions too that have not been worth extracting certain data points because we might only get a question once a year, but now it's so much easier for us to just be able to add a single data point and extract all that data upfront so that we can solve all of those and answer all of those ad hoc questions as we're getting them down the road.

So we've put in extraction models. Investors sometimes will upload documents where they're supposed to. Sometimes they'll say, here's my one consolidated PDF and you like go and find it. And so it becomes a challenge for our team to be able to scale.

We have – we've always had a very linear headcount between our AUM and our employees that we have to hire. And so we're hoping to break that correlation so that we can now start to have a higher carrying capacity for each employee as we utilize AI.

And so we're using that really heavily there. So it's been able to extract the data from non-standard docs. It's also been able to apply and do the first wave of reasonableness checks as we're extracting all of that data.

And one of the other things that we really like about it is as you're extracting that data, when you're in DocCenter, there's a couple of things that we really like about it.

First of all, it's just adding a new data extraction model is basically just a configuration within the tool. And so now I don't need to have my developers playing middleman with the business analyst who's actually the most familiar person with the document.

So now a business analyst can go in. They can actually configure the extraction model and they can test it themselves and make sure that they're getting the results that they want before they hand it off to the development team so that the development team can take something that's essentially ready for production and they can then just drop it into the workflow.

One of the other things that's really nice about it is the ability to be able to geotag the fields. And so this was a really big selling point for us because it's one thing to be able to extract the data. But as we all know, AI can hallucinate.

And if you're just getting a blob of the text, how do you know that it's actually – if the country says that they're from Germany, how do you know that they're actually from Germany without actually going into the document and seeing it?

So we have the ability to be able to click – it looks like a little map icon, and it's essentially geotagged within the document. So you can click on the tag and it pulls it right up next to you, so you can see from an accuracy standpoint if it extracted appropriately.

So those are some real key selling features for us as we use it.

Wonderful. And let me ask one last question before we close up here for Keith. It's the opposite side of what we've talked about here.

There's been a lot of discussions about how AI is just going to solve every problem in the world. It's going to write all of our software. It's going to make software companies go away, for example.

What's your reaction to that? And how does Regeneron think about that on a daily basis?

Yes. So we're really taking a hybrid approach. So we don't feel – my opinion, software companies aren't going away tomorrow. You see new players every day. I think a lot of the companies that existed 2 years ago have a different model today. A lot of the new companies today may not be around in 2 years.

So kind of taking a hybrid approach and understanding kind of what core software do we need, how are the core software going to interact with AI, whether it's part of that core software package or whether it's taking that data and embedding it with other data inside of others?

For us, the key is scalability. So we don't necessarily want to make a lot of different bets and vendor-lock-in.

We want platforms like Appian where you can use it as a single pane of glass and reach out to AI and other things. If you do need to make a change, you can easily plug and play a different component, a different model in easier.

But we are trying to take a hybrid approach, understanding what's coming but also making sure that we are – if there are changes in the ecosystem, best prepared to be able to make any changes.

All right. Well, gentlemen, thank you so much for your time.

Thank you.

Thank you.

Thanks, everybody. I am Scott Van Balkenberg. I lead global alliances and channels for Appian.

We have one of our top alliances here on stage, PwC. Dan Scott is a principal. We'll do some quick introductions, but we want to talk a little bit more about how our partnership and driving growth in the market is going to impact over the next couple of years with AI and the types of things great firms like PwC are doing in the market.

So Dan, real quick as we sit down, why don't we do an intro. We've had an alliance for 8-plus years.

It's been a while.

It's been a while. We had some amazing announcements at Appian World, doubling down on our alliance and efforts that we're going there. Just a little bit about you, your background, et cetera.

Sure. So Dan Scott, I'm a principal in our cloud and engineering practice.

Let's see. I think I'm required to say that the opinions that I express here are my own.

Highly regulated.

All right. So with that being said, let's dive in.

One of the things I was really excited about was our announcement at Appian World on legacy modernization, several things Matt talked about, the opportunity to really transform the clients you serve, the clients we serve.

Share with us some of the use cases and thoughts and how the firm and you as a practice are viewing Appian.

Sure. So let me start with us. So we're in the process of transforming the way we do business. And we're trying to use AI in everything we do to bring down the cost of delivery to a client.

That has brought up the number of opportunities for us pretty dramatically. And so that's been really exciting. And we think Appian is an important part of that, and that's part of the announcements that we made at Appian World around how do we get what we call end-user computing, which is Access, Excel applications.

They seem boring, but they are what enterprises run on. How do we get them off the desk and actually into something that we can then identify and make more useful?

So when you think about that across these use cases, the firm put a ton of investment, people, resources in building the Appian practice. Are there some examples you're seeing in certain industries where this is more or less common?

I would love to tell you that it's one industry, but it's right now across the industry. We are hiring more people all the time because this is a hotspot. It's AI-adjacent.

And so I think there's a lot of folks who are looking at this and saying, they have a workflow tool. In a lot of cases, that's Appian for the customers that I work with. And they're like, okay, I didn't like 2 things about my workflow tool, no offense.

One, it took me a while to actually configure that process. And two, when I actually configured that process, occasionally, I had to have one of my employees actually jump in and do something because the tool wasn't able to do it.

And so I think at least our clients are saying, "Hey, this is a great mix of deterministic that I can test, that I've been running in production for years and nondeterministic in actually getting that faster."

Now that's a little bit of a challenge and putting more miles on our team because we are doing more engagements for slightly less money, although I shouldn't share that with some of my clients. But that has created a lot of new opportunities for us in the market.

So you're starting to see the shift. Every day, there's a new approach on AI, how are people thinking, adopting it, et cetera. There's been a lot of the experimentation mode and this bridge in this gap from production mode.

Your views of what we've been doing together in the past and some of these new announcements and changing that, what's getting that excitement in the firm?

Well, so we have a lot of customers who are deploying either code tools, and I'd love to tell you they're using it a lot better than just code completion on the UI. They're not always doing that.

Or they've launched something with an end user – and there's a big gap in between that. And so that has been a lot more challenging for companies to actually build and deploy at scale.

There's a lot of discussions that folks have, should I go with a general agent. So I can't name names on general agents, but I installed and have one of them at home. I do not give it any of my credit cards and/or passwords – that is a really bad idea.

But while the general agent concept is wonderful – that, look, I just put this agent in, the agent replaces an employee. If I do the math in a company really quick, I can count up my savings really quick, and this is going to be great.

Okay, except there's some problems with that. Your controls in a highly regulated industry were not really designed for an agent. Have you fired an agent?

Yes, I haven't.

What do we call agent collaboration? Is that collusion? Do I have a four-eyes control able to work?

So there's a lot of controls that are in these industries today that you have to rethink when you go to AI. And so a lot of times, people are like, "Hey, I'm going to get myself a subscription to a coding tool." I have many such subscriptions. I will tell you that that is not the panacea that it sounds like it is. We have an entire generated code practice that does a very good business, helping companies with generated code, but it costs a lot more than I think people understand.

Yes. And the subsidization of token pricing as well.

So as you think about the announcement we made together at Appian World and the investment behind the firm, the concept of the vibe coding, these mentions, why did the firm put its weight behind Composer and legacy modernization with Appian?

So look, this is a growing level of interest for our customers. We've been doing, over the last 2 to 3 years, a lot more legacy modernization. I would say AI has put gas on that particular fire.

It was a real no-brainer. In a lot of cases, we're talking about moving from legacy code to new code, okay? But new code has a lot of the problems that old code has. And when I say that, it rots over time. God only knows what was in it.

If you're moving to a fully generated code model, again, that requires some structure that not every one of our clients is willing to sign up for.

And so the idea of taking the business requirements out of that, modernizing that a little bit through the – because I'm assuming you do not want the same green screens in Appian. There were some announcements about UI, but I'm pretty sure there's not a green screen mode. There's no green screen.

Doing a little bit of modernization in that process and then getting that application in a modern environment that stays modern that you can buy a subscription for, that's pretty attractive.

And the interesting thing as we've collaborated, it's not industry-bound. I mean this topic is touching every industry the firm is serving.

It's across industries.

Yes. We're super excited.

So when you take that in and the discussions you have with clients today, I think Mark had mentioned the SaaS-pocalypse, what does this mean?

What are you hearing from clients in a broad sense? How does that relate in the way that you've personally seen our collaboration together, et cetera?

So I – again, I hope not to disappoint you and/or insult you on the stage. But we view Appian as an app platform. And so that's what we use it for, whether we're using it with clients, whether we're building products to sell to clients, whether we're using it for ourselves.

That's kind of what we look at Appian as.

So I'll leave it to others to figure out what that means in terms of the SaaS-pocalypse, but we don't view you in the same way that we view other SaaS products.

Yes, I definitely think there's a different view of the Platform-as-a-Service piece.

Do you think clients are thinking this way? Or is it just everything is, "throw AI at it"? I know we've had a lot of discussions on ROI.

Well, so clients is a very broad term, and we've got clients that are at every part of this equation. We have some clients who were with us with some of our AI partners when they were doing initial announcements. We have other clients who are not quite as large there.

So we try and meet the clients where they are. We're in the process of going through this same thing ourselves. And so we try and bring that humility, and we also try and bring the learnings of how do we actually get the team up to speed on this so that you can start to do it.

But I think I heard it mentioned earlier, we actually call it – strange that it's the same thing. So you need an AI stack. If you're thinking that you're just talking about a coding tool, there are a lot of tools you're going to want around that.

You might want a wiki for knowledge management. The Internet is a cesspool. You might want a ticketing system. You might want a workflow system, the same.

So there are a lot of tools that we use around AI and a lot of companies have not yet sort of figured out what is their AI stack going to look like. And we have just touched the surface of that.

We look at it all the way from how do I wrap that API that I get all the way to how do I make sure I don't have injection, how do I make sure that the right data is going to the right folks.

You can either bring us in for a lot of those things or, in a lot of cases, Appian brings that out-of-the-box to the platform. So for a lot of clients, it's really exciting.

Awesome. So there's been a lot of discussion about our firms and their business models changing, et cetera. How do you see this in the demand and the demand for the things that we do together? What's the general feeling in the market?

Yes. So I would say there are sort of 2 buckets of things that we're seeing. We're seeing the same things that we used to do, and we're doing more of those because we can actually bring the cost down. So the ROI got better on that.

And then there are actually some exciting new things that either weren't feasible from a cost perspective, weren't feasible from just "not able to do that" perspective.

Right now, I would say we're very focused on bucket number one, but we're starting to see people dream into bucket number two. And that for me is a lot more exciting because that means revenue instead of just cost takeout.

And as much as I love cost takeout and I'm willing to help clients all day long with cost takeout, revenue is a lot more fun.

Great.

A couple of last questions to wrap up. One is the firm and Appian have leaned, from the beginning of this year, incredibly heavily into our collaboration, our alliance in ways that I don't think either side had seen before.

When you think about this motion and the prioritization of Appian within the firm, what are the growth ideas and areas that you're leaning more into? I know we talked about legacy modernization and others that help support clients.

So look, we have some of our technical practices like legacy modernization, which is important here. But I think most people know us for our business knowledge.

And so we have a lot of folks who have some great business ideas either for a service, that they're going to take their advice business and turn into a service, or that they're going to work with clients to make their services better.

We want to be able to support them as an Appian practice to get their ideas into a production-level application as quickly as possible.

While some of this AI means there are fewer people in our Appian practice per project, I think overall, our program is still growing. And even if that is the case, then we will have enabled our ginormous business consulting business, which gets a win.

I'm a partner in the firm, not just in the cloud engineering practice.

I think it's great. If you aren't aware, PwC has developed some amazing solutions on Appian. They're taking it to market, especially in the pharma life sciences space, including iHub.

And we just made the announcement on pharma co-labeling, these complex processes similar to what was spoken of in financial services – different geos, regulatory environments, et cetera, in ways to help clients.

I guess last to close out – what are you most excited about in our collaboration? Are there any areas that you're particularly excited about?

Just one spot? I'm actually excited about the opportunity in this space. But I think if there's one thing that we have seen over the last couple of months that makes me most excited is as the BPM leader, we are very interested, now that we have made business process management easy with the ability to use AI to generate a process reliably, repeatably, and then the existing capability that exists within Appian to use Agentic AI, there's a lot of customers that need that, and we're excited to be here to help them.

Thanks, Dan. More to come with our relationship. I appreciate you spending the time with us.

Next, we'll introduce Mark Dorsey, our Chief Revenue Officer.

Thanks, Scott. Scott, you get the clicker.

Okay. Well, I can do it without the clicker.

Well, first of all, I want to kind of start off by saying thank you to the customers who spoke today. Thank you to the partners you heard from, and thank you all for taking the time to be here.

You all have lots of things you can do with your day. I appreciate you spending it with us.

I want to tell you a little bit about me, right? So my background.

So I spent 15 years at IBM. It was a great experience. Earlier in my career, I was the software sales representative of the year, the top rep out of 7,000. This is early in my career.

So from there, it catapulted my career within IBM, where I actually went and I had many, many, many jobs, but I was asked to be part of the senior executive training program. And to give you an example, when I left IBM, I was called – I was a Vice President, and it's a B and C executive.

Now to tell you that, what I mean by that was when we acquired Sterling Commerce, and I was part of that team that did that, the CEO came with the same level I was at. Just to give you an idea of all the experiences, not to be braggadocious, but just to let you know in context because titles mean different things at different companies.

IBM was a great training ground for me. I really learned how to – I learned how to run a business and I learned how to sell with value, right? And value is something you're going to hear from me a few times today because Appian's technology, our platform, delivers tremendous value.

So from there, I did a short stint at Bank of America Merchant Services as Executive Vice President. Then I went into Oracle. Oracle was a great experience for me.

I spent a lot of time at Oracle in a few different roles. I was recruited there by Rich Sarraf, who worked for Mark Hurd, and I was fortunate enough to be mentored by Mark Hurd for a number of years before he passed. It was a great experience. I learned an incredible amount from Mark.

I guess talking about that, one of the key accomplishments at Oracle for me was that I was asked to run their cloud business in the beginning. And I took that cloud business from $10 million to just shy of $1 billion in a very short period of time, competing against AWS, Microsoft, Google and other hyperscalers.

What a great opportunity that was to really compete, and we had to compete with a great technology and to sell value, kind of what we're doing here today.

And I saw a snippet at Alteryx. We got acquired. And then fortunately, I competed – I had 2 offers at the very end: to go run – to be Chief Revenue Officer of just shy of a $2 billion business, which was more of a run-and-maintain; or Matt gave me the opportunity to come here.

And I looked at that opportunity, and I just kind of said to myself, why Appian?

Well, what came out of me, first of all, the product is incredibly valuable. It's easy to use, and we empower business users to solve complex problems. They don't need to be an Oracle DBA. Anybody in this room could use our tool, right? And you can solve really difficult problems and run the orchestration.

And I'm going to do my best to speak a little bit louder than the sirens out here to kind of help you guys out.

But we solve really, really complex problems. You can see from what you've heard today from my colleagues and some of the customers and partners, we are integrated into the crux of the mission-critical problems, highly regulated industries where governance and performance has to happen.

And so the product is incredibly valuable. And it made me look at this and say, wait a second, can we sell value here? Do we have a great product? And everything I looked at – the product I'm talking about – I actually talked to customers. I talked to some employees here. I went to my network and the product works.

It's kind of like this unfound gem. Then I looked at the next thing, and I talked about how sticky it is. I started digging into the financials, and I hope you guys do this as well. Our customer retention rate is through the roof.

…And then when you talk about value, I focus on selling outcomes. Nobody buys software because they feel like it. They're buying it to solve a problem, to provide a strong outcome based upon a business case.

You kind of heard it today. It really kind of spans, right, anywhere from document ingestion with DocCenter to Agentic AI, which you can put in a process anywhere within a process. That's kind of the secret sauce – you can take our agents and put them anywhere in there.

And then modernizing applications that's been around for a long time. But one of the things I really wish some of you could actually see is a demo of our Composer, kind of coupled with either Anthropic or another LLM in front of it. It's pretty amazing what we can kind of do in a very short period of time. And when we show it to customers, it really dazzles them.

So then I had the opportunity to really transform this organization into selling more, but transform the organization based upon value was really important because customers don't want to buy just a new technical enhancement. What are they looking for?

I mean it's important, it's nice. They're looking for outcomes, right? And that's what we focus on today.

Let me talk to you about some of the things that were happening. It was more of a technical sale, spent a lot of time kind of talking internally, less focused externally, and then really focused on small deals. And I looked at this and said, what are we doing this for?

This is incredibly valuable technology. So what do we do now? We focus on value, ROI, business cases and outcomes.

Some of the things we didn't talk about today, you heard a little bit of – we kept some of the numbers off of these slides. But some of these use cases are providing tens to hundreds of millions of dollars in value to organizations.

And that's it. And we have a tool that we use to continue to be customer-obsessed. We do the right thing for our customers. We create the value somehow.

How do we create that? It's with our services team, it's with our partner ecosystem or the customers do it themselves. If you were at our Appian World, you would have heard on stage one of our customers talk about outcomes.

What they do is they take the technology people and the business people, they put them in a room and boom, they have a process and they create incredible efficiencies really, really fast.

Executive relationships. I want to be talking to the people and the executives and understand what are the problems they're trying to solve. And if we can help them, we'll tell them. And if we can't, we'll say, hey, we can't, we'll put them in the right direction. I don't want to waste their time or our time; it creates a lot of friction.

Go big. I’ve got to say this: last year, we sold more 7-figure deals than we've ever done in the history of our company. And I can tell you that trajectory is not stopping. And as you can see, we focus on that.

Let's talk about really what happened. So first I had to do is change the team. I'll tell you, last year, during a year of great results, 34 leaders across my organization were added to the team. We improved the team dramatically by doing that.

You can see my direct reports pretty much all changed, right? And then the next level down, a lot of transformation. I can tell you this happened throughout the organization at all levels.

We're bringing in highly skilled sales executives from Google, Adobe, IBM, Salesforce, Microsoft, ServiceNow. These are some of my directs that are throughout the organization, people who can sell with value, focused on outcomes, large strategic deals, aligned with the companies at the senior level.

When you do that, your product is incredibly sticky.

So really, what else do I need to focus on? Pipeline. We put a focus on pipeline. Why? Because pipeline is the lifeblood of sales.

You go into the numbers, you look at the yield on your pipeline, yield at different stages. These are indicators of where we're going. And I can tell you this much, I'm not going to share any numbers, but our pipeline is up dramatically.

Enablement. What are we going to do? We've got to create the strategy, point and aim our teams in the right direction and enable them on how to go do that. We hired a world-class executive to run the enablement, and we're having some great results in that.

Forecasting. I don't think there was enough operational discipline on how we inspect the deals, how we qualify the deals with the economic buyer to make sure that we're not wasting their time or our time.

And pricing. Last year, I created, with the work of the team, the extended team, what was called an Enterprise Growth Plan. What does that mean? That's really unlocking the ability throughout the organization for people to use as much as they want, wherever in the organization the value is going to come from.

It's interesting. There's a large financial services customer I sat with early in my time here at Appian. We trained them, and then they came back and they had a competition to show their Chief Operating Officer. And I can tell you right now, there's 4 others that have been funded since.

And they found millions of dollars in value at low levels of the organization. They didn't even know that was out there. So we just put it in their hands and let them go.

So this has been kind of a fun journey.

I really want to talk to you about the operating process and rigor we bring in. The first thing is we need to go spend time with our customers. And I tell the teams, we had a return-to-office policy. I have a return-to-customer policy.

I want my teams going and spending time with our customers. That's what I want. It's expected. People buy from people they like and trust, and we've got to get out there and build that.

And how do we do that? We focus on building pipeline. We listen to them. We actually do a lot of discovery work. We find out what their difficult, complex problems are. And to do that, we have to spend a lot of time listening and learning and see where we go.

I spend time making sure we're selling at the executive level. You can see that from some of the – a few of the executives that are here today. They're getting so much value they're coming here and speaking on our behalf.

Interesting. Large strategic deals – you'll hear that. They can come in many forms. But why are we focusing on that? Because we're quantifying the value and the customers understand the value and they're willing to sign up for this.

And you'll see that continuing to grow. Standard, Advanced and Premium tiers. In the Advanced tiers, we’ve got our AI capabilities. And right now, there's a tremendous amount of inflow from customers understanding our AI because really, as you probably understood – I suspect many of you read the MIT study – it talks about the value given within the process. It kind of – it was a softball for us, right?

That's exactly where the value comes – in the process – because the Agentic AI are contained, as you heard from Matt and others, within the process. It's not going out there willy-nilly. It's with a lot of governance. It's with a lot of regulations, and it's clear.

So we focus on that. Now it's very important to me to make sure that all the deals we qualify with economic buyers because that actually feeds up our forecasting, right?

There are a lot of reps sometimes in organizations that they think it's going to happen, but I want to go ask them, "Hey, if we can kind of achieve this for you, can you do this?" And we actually qualify the deals.

We bring the operational rigor that I was driving at Oracle here. Why? Because it works. And customers love it. They want to understand, too. They want to see where things are at, but they want to see what's in it for them and we clearly show them that with our business cases and our ROI.

Now there's a big focus on winning new logos. And I can tell you right now – I can't get into the numbers – but that's a 7-figure new logo in Q1. I can't talk to you about what's going to happen in Q2, what's happening, but I can tell you that in Q1, we won a lot of customers, large.

A couple of case studies. So this is a large insurer with what's called their star rating. Star rating – you have to have a certain level of rating or you can't be involved in Medicare and Medicaid. They'll just downgrade you and go to a competitor.

We kind of came in, kind of helped them. We made sure that we worked with them. We spent a lot of time with their current processes and actually completely turned around and have increased their star rating at this point.

And they've decided right now, because of our incredible technology and how we're helping them, they're right now currently in the process of moving 100 applications to us.

So what the slide shows you in the revenue here is just the basic growth. So when we first started off, I can tell you the number was not that large. They have committed to a multimillion-dollar deal that has ramped like this.

Why? They're getting tremendous value. The second thing they're actually doing because they signed up for an Enterprise Growth Plan – they're actually now looking at their competitors in our space, getting off of their competitors and going to us.

We and our partners are both doing it. It's not just all Appian doing the services. Our partners are doing the services as well because there's a lot of work to get off of these, and they're using some of our other tools like Composer to kind of help with this.

So it's an amazing flow, but you can imagine the size of this organization. This is not a small organization, and they're getting lots and lots of value.

The next one here is talking about a case that happened within a branch of the U.S. military. What I love about this deal is the quick sales cycle to the bigger deal.

We spent a good amount of time closing a transaction with them. We closed in Q3 of last year. Because of the quick impact that our team provided them, they then, one quarter later, signed up for a deal that was between 10% and 15% larger than what they did in Q3, a multimillion-dollar commitment here.

And why did they do that? Again, they did it because of the value we're providing them. The same organization was in Appian last week, sitting down with us strategizing on what's next.

So what happens is we can land and expand in these accounts because we show them the value. We show them the technology. Like in both of these examples, we go in, we develop a proof of concept with our team, and we show it to them and they love it.

It's really making – we make difficult problems go away with the value in the technology.

And I'll go to the next example here. This is an aircraft engine manufacturer. They have a massive, massive backlog of aircraft engines. They were using an old antiquated homegrown supply-chain system.

Think of all the parts that have to go into building an aircraft engine. Think of the logistics of getting these parts. Think of a part that comes in and it's broken.

And think of the complexity of this and the timing around this. They told me, every day we speed up their production of their engine line, it saves them – I'll just put it this way: it's a staggering amount of millions of dollars. I'm going to refrain from the exact number.

The value is incredible. We save them. We're now in one engine line, one of their lines, and we're going to go live full production there, and we have 5 more to go.

These numbers here – the number of this thing here – could be 5x what it is now, and that's the same with the previous 2 examples.

These are just 3 that I picked to show you today.

And I'll be 100% clear: I'm already talking and in negotiations with this organization to do a much larger transaction.

So if you kind of want to get into this right now, what's important to me is rep productivity. And when I talk to my sales managers, I talk to them about: your job as a sales manager is to get everybody in your team successful.

I'll share something with you inside the organization. I think the managers – I'm trying to get the managers to understand – your job is to get everybody to be successful.

And what I did is there was one manager in the organization whose entire team got to quota, so I sent them all to club.

They were blown away by that because I want my leadership team to know it's your responsibility to make sure you get everybody in your team to your plan. It doesn't matter if one rep just goes and crushes it on the team and they make the number. I want them all contributing because I want to make sure we help our teams and show them how to do this.

And I've got to tell you, it's working, and we're having a lot of fun from this.

So now 2 things are happening: ramping time and enablement. Our enterprise sales professionals who know how to sell value and large strategic deals based upon outcomes – those are my metrics because I'm adding headcount.

Not a lot of people are doing that these days, but we're doing that because of the tremendous growth. And I look at this right now: the ramp. I have a rep that was onboard for 4 months. In Q1, he sold a 7-figure transaction. That is not typical, but it is – it can happen when you bring in enterprise sales executives, pay them well and actually set them loose because this is what they do. It's in their DNA. They've done it before, and we're bringing it.

In addition to that, I want to say that there's plenty of opportunity ahead. I am extremely optimistic. I'm happy with what's happening. There's a lot of good work to do, but we are actually driving a sales organization that's inspired, they're energetic.

All the new folks that are coming in, they can't believe how good it is. And they're like, "How has this company not actually gone through the roof yet?" because they really are excited about the technology.

I'm excited about the technology, right? And what happens is that we have to continue to focus on the value, the ROI, the business cases, and we're selling outcomes. We don't realize that.

And the difference is it's a platform. And so somebody has got to build the value. It's not like an application – you think of SaaS-pocalypse. It's just not.

So what are we doing? Well, we're going to continue to grow the team. Matt's pushing me to continue to grow the team. And we – I'm not just going to get anybody. I want to get the best of the best, and we're fighting to do that.

A world-class operations leader. We're focusing on making sure that we're digging into all aspects of this. Why? Because we want to help the team succeed.

My belief is that everybody in sales leadership is there to help increase sales productivity and help more of our sales professionals do better.

We focus on selling value. I think you've heard me say it a couple of times. Why? Because that's what sells. When an executive is going to go make a multimillion-dollar purchase, they need to go in with a business case and see the outcome.

I'm asking my organization, account executives, to deliver at least one $1 million deal this year. And they're focused on it and they're building the pipeline for it and they're aligned with the senior executives.

And then I really focus on driving more AI adoption. Why? Because it just delivers so much value, right? And it's actually working. And the customers love to hear about it.

Now in addition to this, we want to actually continue to focus on the top of the funnel. There's more pipe out there today. I brought Scott on board. He was one of the people I hired. He's had proven success at many companies, great relationships.

Now we're doing account planning with our business partners per account, per region. We've got strategic partners kind of out there working for us. Our pipeline from our partners is up and increasing as well.

And we're also looking into launching new revenue streams. So in addition to the Enterprise Growth Plan, which has been a massive hit with people because they don't have to count licenses and they can just go continue to focus on building value, we're starting to sell pilots around consumption.

Because if you think about what we actually do, it's not the easiest thing to figure out how to price our technology. But what we do is we meet the customer where they are in their journey. And we're having a lot of fun with this because when it comes down, somebody may want this, they may want that.

We just want to make sure we sell them a contractual value that actually meets where they are today. Sometimes people start with one and end with a different one, but then most of the time, they want to eventually move to an Enterprise Growth Plan because they see the value in that.

And really, what I'm doing in the sales organization is focusing on – if I was in your shoes, I'd say, "Hey, Mark, what are you focusing on in AI in the sales organization?"

We're starting to use AI to kind of qualify leads, to make sure the leads that are coming in, in different kinds of avenues – making sure we're going to be using it for that. We're starting to pilot opportunities in the BDR space to bring in leads in that way.

We're looking for efficiencies – we're already using it with an organization, a tool, to help us with discovery and to find out how to make sure. A lot of discovery work has to happen in the sales cycle – figure out the problems customers are dealing with and to get them. And we'll continue to evaluate this.

But I want to kind of wrap up by saying: myself and my team 100% believe in our technology. It's incredibly sticky. It's incredibly valuable. And if you have any questions, any, feel free to reach out to me and ask me.

Thank you for your time. Serge?

How are we doing? Home stretch, we're almost there. There's coffee outside. If you need to stand back and stretch, just do it. We're almost there. Really appreciate the patience and the attention.

Okay. So I'm going to talk to you about 3 things.

Number one, provide you a little bit more context about our ARR growth and sort of how it divides in various different ways.

Second of all, understanding our land-and-expand strategy, where our customers start and how we see them grow over time.

And finally, talk about how we can drive sustainable growth in this business. And then, of course, Matt will come and join me, and we'll do some Q&A.

So first on ARR. This is the history. We've grown pretty consistently over time. And last year, we've cleared $600 million in terms of ARR. And now we're going to double-click it in multiple different ways.

So first, looking at it by product, and this is familiar to you guys because we do report cloud revenue. So it shouldn't be a surprise that we're predominantly a cloud company and have been for a while, actually.

And you can see that we're roughly 80% of our ARR is in the cloud, and that's up just slightly over the last 5 years. And based on our guidance, that's going to continue going up.

What I will say, though, is the self-managed part of the business is actually hugely strategically valuable to us because in our highly regulated industries that are 80% of our business, customers want the option to self-manage. They want the option to be on-prem.

And as data sovereignty becomes a bigger and bigger issue, having that ability to self-manage is actually a strategic differentiator for us. So a small part of the business, but very important.

Okay. The next way to look at it is by industry. And again, here, Matt talked about it. The big 4 are roughly 80% of ARR and have been for the last 5 years. But there's a little bit of a mix shift there, and so I'll talk about it.

If you look at our financials, on the left-hand side, you see that our financial vertical has consistently grown over time. But the public sector has actually grown faster. So financial services are a smaller percentage of the business, whereas public sector has grown as a percentage of the business, and that's not a surprise for those of you who have been following us for a while.

We've had great success, particularly over the last 18 months, as the government has focused more on efficiency.

Similar story by theater. Our biggest theater still is Commercial North America. And as you can see on the left, it has continued growing over time. However, both our public sector – U.S. public sector – and our EMEA business have actually added more to the growth.

So it's more of a balanced portfolio by geography than it was 5 years ago.

Then this is my favorite cut maybe. So this looks at the contribution from customers who spend more than $1 million with us versus all other customers. And you can see that the significant majority of our business comes from customers who spend over $1 million with us.

So those are customers who are heavily invested in Appian technology, have internal resources, have a center of excellence. We're deeply integrated with all their other systems. They use Appian data fabric. And also at the same time, they are using us as a standard application development platform.

So they are bringing more and more workloads onto Appian, and those are exceptionally valuable and sticky relationships.

And when I – we disclosed some of these numbers, but here's a longer history. The number of customers who spend more than $1 million with us has doubled over the last 5 years. And you also see that it kicked up in 2025, and that's because of the focus that we've moved to up-market, large strategic deals, selling with value, stuff that Mark has just talked to you about.

So we've seen success more recently on that front as well.

And what's incrementally interesting is that even though we've grown the number of customers and we get more and more customers over that $1 million mark, the average size has actually continued increasing because we don't stop once you're a 7-figure customer. We make you a high 7-figure customer.

You see some of those – Mark showed you some of those ARRs. And we have a growing number of 8-figure customers as well.

So that's the story on ARR.

Let's talk about land and expand.

So first, we've been in business since 1999, so over 25 years, but we're still early in penetrating the market. And in particular, you've heard us say we belong at the high end, we belong in the mission-critical use cases.

But even if you look at the Fortune 500 and the Global 2000, our penetration is still low. So 16% of the Fortune 500 – quick math, that's 80 companies. And so a lot of penetration to grow.

And even in our key verticals, so if you just look at the Fortune 500 in insurance, financials and health care, still a long way to go.

And Mark has been talking about some of our more recent success when it comes to winning new logos and particularly large new logos, 7-figure new logos.

Okay. So that's the opportunity. That's the opportunity set at the high end of the market, still plenty of way to go.

The average size of the customer that we're bringing in has grown. And this is, again, Mark talked about in the past, we were more focused on volume. We're now more focused on value. We're more focused on selling on value, on the sizes of the transactions, and that's showing 40% higher average size.

And what's even more fun is what happens afterwards. So this is a composite growth curve of our customer base. And what I mean by that is look at every customer cohort in every year that they've made it.

So all the customers that have made it to year 2, which is all the customers except the ones that we've acquired last year; all the customers that made it to year 3, year 4, year 5.

And you can see that our customers grow over time and keep growing over time.

So my favorite part of this chart is that in years 5, 6 and 7, we're still getting value. We're still upselling. ARR is still significantly growing.

In fact, if you look at the incremental ARR for our entire company last year, over one-third of it came from customers that we acquired in 2020 or earlier, which just shows sort of the opportunity that we have even in what you would consider a mature customer base.

Okay. And now for the drivers of sustainable growth.

First, let's zoom out on revenue. Some quarters will be better than others. But if you take a look at over the last 6 years, we've delivered consistent growth.

And you see here our 2026 guidance. So we're forecasting in the middle of the range, $825 million. So we're getting closer to that $1 billion mark, right?

Meanwhile, we've continued improving profitability.

We at Appian are very proud of this chart. So as you can see, we were significantly negative on EBITDA not that long ago. But as we focused on efficient growth, as we frankly pruned some of the investment areas where we weren't seeing the right returns, we've seen a significant turnaround.

And this year, we're forecasting right around $100 million in EBITDA for 2026 at the midpoint of the guide.

Similar picture with free cash flow, so operating cash flow minus CapEx. We were significantly negative not that long ago. But as we focused on efficiency of our growth, we see significant improvements.

And what's interesting, these numbers include the cost of our litigation with Pegasystems, which is not trivial. So for example, the $60 million in 2025 is burdened by $10 million cost of litigation, which obviously isn't a forever cost.

We talk about the weighted Rule of 40. This is the idea that we weigh our cloud growth twice as much as our EBITDA margin and calculate our weighted Rule of 40. And this is a very important metric because some of us are compensated on it.

As you can see, 2 out of the best quarters in the last 3 years were 2 out of the last 3 quarters. So we care deeply about this number.

So now we're going to switch gears a little bit and think about how the past translates into the future by OpEx line item, starting with our biggest expense, sales and marketing.

So in sales and marketing, we've shown significant operating leverage from 43% of revenue in 2023 to 32% in 2025. And to help put that in context, we're providing a comp set here.

We're looking at software companies that are $500 million to $1 billion in size and then obviously much larger. So we're more sales-and-marketing intensive than the median software company because we have a long sales cycle and because we're selling a platform.

But that doesn't mean that we cannot continue delivering leverage and generally providing this trend over time.

Now what's interesting is sales and marketing isn't just what percent of revenue it is, but also how do you use that money to drive revenue growth.

So we think about it in multiple different ways, as you would expect us to.

First, this is our go-to-market efficiency metric that we talk about every quarter, and we're proud to say that it's been improving over the last 11 quarters. And that is looking at our billings and dividing them by sales and marketing expense.

Another way that we look at it is to look at the relationship between net new software ACV – so the new software business that we bring – and divide that by our cash sales and marketing investment.

I think of this as the purest return on your sales and marketing investment. And you can see that we've improved significantly over the last 2 years.

And there are multiple ingredients to that. Mark has talked about some of them. So the ramp – the rep productivity has significantly improved. Our reps are ramping faster.

And while we're doing all of that, we're actually keeping a close eye on our expenses. And so that means that we're getting a better return. That means we're getting a much faster payback on our sales and marketing expenses.

And you've heard me say that we've earned the right to grow our sales and marketing organization after 2 years of not growing it. And this is the reason why – because we've improved the returns.

And now the goal is, of course, to keep improving returns while growing the sales org. You heard Mark being very excited about that.

Next up, R&D. So here again, you've seen some scaling from 27% to 22%, but we are significantly higher than the peer companies, both our size and the larger ones.

And again, this is because we actually have a very broad surface area when it comes to R&D. We are a platform. We're not a single use case. It's a full stack set of capabilities that we're upgrading.

And hopefully, after listening to Sanat and Jake speak, you have a bit of a better sense as to why that is.

But it doesn't mean we take this for granted. It doesn't mean that we don't see a significant opportunity to have operating leverage at the R&D line.

There's actually 2 ways we're driving this. First, and over a longer period of time, we've been more aggressively hiring in India, in particular, because of the labor benefits that we have there. And you see the jump that we're expecting in 2026. In fact, all of our hiring effectively is happening in India at a significantly lower cost.

And then more recently, I really commend our R&D team for aggressively pushing to use AI in the development process.

And you see here a measure of engineering productivity – it's pull requests divided by cycle time – and we're indexing it to the second half of last year. And just in the beginning of this year, we've seen significant improvement.

And that's not to suggest that we're done. It's just the promise of using AI to really completely reconsider and reinvent the software development life cycle.

And what that's going to do for us is not only help us provide leverage in our R&D expense, but actually, for the same number of dollars, deliver more innovation in the market.

And now is the time we want that innovation because we're having great success with AI, and we want to keep pushing it.

Okay. Next, G&A. We have provided savings here. In fact, versus the median company our size, we're more frugal when it comes to G&A.

And then if you break that down further, not surprisingly, we have a disproportionate investment in information security because of all the use cases and the regulated industries that we support.

If you look at our other G&A functions – so whether it's finance, people, IT – they're actually quite lean. Nonetheless, with use of AI and other tools, we continue to expect seeing operating leverage in this line as well.

And I'll save the best for last. Stock-based compensation. As a percent of revenue, we're far below not just our immediate peer set, but also much larger companies.

You've heard us say this over and over again: we're very careful about dilution. And because we're very careful about dilution and because of the improved cash profitability and cash-flow generation, we're in a position to start returning capital to shareholders and actually shrinking our share count.

So last week at our earnings call, we announced that we're increasing the size of our buyback after a strong start to the year from $50 million to $100 million, and that puts us in a position to start shrinking our share count.

And obviously, this is the average for the year, so the exit run rate is going to be even more. And this is yet another way in which we can continue delivering value to our shareholders and increasing profitability per share.

Okay. So let's talk about how we think about our growth algorithm using 2026 as an example.

First comes revenue, of course. We're forecasting $825 million at the midpoint of the range, 13% growth. Subscription will grow a little bit faster than that.

And you heard about all the tailwinds that we're seeing in the market in terms of AI, improvement of processes, legacy modernization. We see a great runway to continue growing revenue. We're not going to $1 billion and stopping there. We're growing past that point.

Next up, EBITDA. And so here, this year, we're forecasting just over 100 basis points of margin expansion after 2 years of – a total of – over almost 20 percentage points of margin expansion.

And you take that 13% revenue growth and just over 100 basis points of margin expansion, and you have 30%, 31% EBITDA growth. So significant incremental growth.

Then non-GAAP EPS. We're forecasting $1 per share at the midpoint of the range. We have some incremental drivers there.

First, we're delevering. What that really means is just our interest expense is going down while our EBITDA is going up, so more is flowing through the bottom line.

And second, we just talked about it – buybacks. We're shrinking the denominator. And that's how you take a 31% EBITDA growth and turn it into roughly 60% EPS growth at the midpoint.

So as you think about these drivers – revenue growth, margin expansion, delevering and buybacks – all of them have room to run. All of them put us in a position to continue compounding value for our shareholders. And what I mean by that is profitability per share.

So 140 slides later, we're back at where we started. And so these are the 4 things that we're hoping you remember.

Number one, Appian is mission-critical. You've heard our customers. You've seen examples. We talked about complex, we talked about cross-functional, mission-critical, and we talked about working in regulated industries where compliance is exceptionally important and accuracy needs to be high.

You heard that we’re an essential AI enabler. You heard from our customers how they're using AI within their processes while still meeting their requirements, which are significant and are not going away.

You heard Mark, the success he's had in driving sales efficiency, the excitement that we see and still the room to keep growing there.

And then finally, the multiple ways that we can grow profitability per share. So if you're going to take a picture of any slide, please take a picture of this one. And you can keep us honest on these 4.

So with that, we're out of slides, but we're not quite out of time. So I'm going to ask Matt to join me on stage, and we're happy to take some questions.

On this Investor Day. It's been a while since we had one like this. So great to see you sort of lay out the strategy and the plan, and I was really impressed with the sort of technology differentiation that you guys pointed to.

I wanted to ask a couple of different questions. The first one is on where growth is going to come from. So we have these 4 major verticals that account for 80% of the business.

And I think historically, there's been times to expand beyond those 4 verticals. It feels like in this age of AI, focus is paramount and it feels like these 4 verticals are where Appian delivers the most value.

So I just want to – first for you, Matt – just sort of sanity check, gut-check on going deeper in these 4 as the growth algorithm in terms of penetrating your TAM versus maybe….

Going broader? Maybe start there.

Yes, that's great. We don't think we need to go into new verticals to get terrific growth. We are focused primarily on the verticals that we've been on.

You look at the pipeline right now in federal, and I think we've got a growth story right there.

Awesome. And then I guess my follow-up question would be – it's sort of a CRO-Mark-related question. I'd love to understand a little bit about this Enterprise Growth Plan.

Like how long of a period of time do customers get the sort of all-you-can-eat consumption? What happens after that Enterprise Growth Plan expires?

And then from a pricing perspective, one topic that wasn't as clear on is like how do you see pricing evolving maybe along with this move up-market?

Yes. So I'll take a crack and then Mark can grade me afterwards.

So the first thing I would say is Enterprise Growth Plan is an all-you-can-eat multiyear plan, usually focused on our largest customers who are ready to standardize, who are ready to bring a lot of use cases to Appian.

So that first example of the health services provider and the 100-plus applications that they're bringing – that's what we're looking for, right? Those are the enabling conditions, if you will.

Second of all, it's just license, right? So we don't give them infrastructure. So it's not like we're facing some sort of margin issue with them. It just really aligns our incentives really well with the customers because we let the contract get out of the way of them really driving usage of Appian, and we really see that happening.

And what happens is at the end of it – we haven't gone to the end of any of them yet – but we structure it such that we see continued growth after that point to continue encouraging them to use the platform and growing the usage as long as they see the value.

You may have heard Mark mention value once or twice, and that's kind of the point. And with Enterprise Growth Plan, it's kind of like the cleanest way to discuss value with the customer as opposed to getting lost in the P's and Q's.

And then more generally, as we think about the pricing umbrella, we have multiple models that we charge. So obviously, we have per-user, we have per-app, we have consumption, we have Enterprise Growth Plans.

We charge for certain pieces separately like infrastructure. And the goal is to meet the customer wherever they are in their journey.

But – and I'm sure you guys do this – when you talk to actual economic buyers, people who sign checks to spend money on software, P times Q is interesting, but what really matters is the value.

And are you delivering multiples of value that they're seeing? And some of the examples – like the aircraft engine manufacturer – we deliver multiples of value of what they paid us.

So they're happy to pay us, whether that's expressed through an Enterprise Growth Plan – that one wasn't actually – or per user or some other flavor. It actually doesn't matter.

The one area where we're particularly excited, maybe not in the very near term, but over the medium term, is the consumption element of AI.

So as we're seeing customers be more ambitious and having more success with their use cases, they're getting to the point where that initial allotment of consumption will not be sufficient.

And that's a great opportunity to engage with them to sell them more AI usage bundles, effectively, and get them to keep growing with Appian.

And by the way, when they get to that point, that's a much easier conversation because they are seeing the value. Otherwise, the use case wouldn't be growing.

Yes, they have 2 options, right? And they both include additional growth for us.

They can actually certify their usage and continue paying us a CPI plus an increased growth rate on that. Or they can say, "I want to keep doing this Enterprise Growth Plan," and we will go back to them with an offer and we'll actually add a significant growth rate on top of that because of the value they're getting from it.

One of the things we're seeing a lot now is them getting off of our competitor technologies and coming to us in this – with software rationalization.

A lot of customers you're talking about right now – they're consolidating platforms in this space. And fortunately for us, we've been in a really good landing spot for that. Does that answer your question?

Ryan Minch from Barclays.

I enjoyed today as well. I have two questions. One – and actually, it's Mark that I kind of wanted to get involved again as well since we don't see him that often.

If you think about the build-out of the sales organization, there's been a lot of progress there in terms of making it enterprise-ready, et cetera. But it's usually a journey. And so you need to fill the positions, everyone needs to settle down, et cetera.

Where are you on that journey in terms of having it all settled and everything cued?

If you want, you can use like a baseball analogy here.

Yes. No, I appreciate the question. I think it's a really insightful question because when you're transforming a sales organization, are you at the beginning, the middle or the end?

I believe right now I'm in the eighth inning, heading into the ninth inning. The team we have in the field right now is very, very good. We'll make a couple of small tweaks.

But last year, it was a real focus on driving large strategic deals so that we can actually hit the numbers, drive the growth and transform the sales organization.

So you'll see – I mean, that's what happens, right?

And what we're going to continue to do – like we just got – I’ve got to be careful what I say here – but we just literally hired in the last week 5 very, very good enterprise account executives.

So we're continuing to add headcount, and we're making sure that I'm not just hiring people that don't have the skills to do this.

Some people aren't going to be happy with me – I'll try to get them to club – but that's a good sales organization. But I think we're probably in the eighth inning of this because now it's just small changes here. And they have some normal attrition, which honestly, my sales force is very low attrition because people see the out of the possible, they see the money they can make. We have good comp plans, and that's a question I was going to ask. We pay the teams well, but we expect a lot out of it. But I would say we're in like the eighth inning.

Okay. Perfect.

And then on the product side, if I look at the presentation, there's a lot of interesting stuff like the Appian data fabric. I saw OCR as well.

What's the – how do you think about your right to win? Because Appian data fabric will be very, very strategic for accounts. A lot of other guys will try to kind of play there in that market.

Like think about what's driving it for you that Appian will be the one because you're not going to start as the largest vendor. You're going to be a vendor for the client.

And similarly, for like – if I think OCR looks really interesting, but I always thought that's kind of what the RPA guys are doing. So just maybe talk to that a little bit.

Yes. Appian data fabric has a few interesting implications. We've always attempted to spin it off as its own product.

I think that when we talk about the AI stack, we've got actually 2 bids to be part of that. One is we're the deterministic layer and the other is we're the enterprise-wide data source.

And interestingly, they serve such complementary purposes that sometimes I feel like what we've really got is the yin to AI's yang, kind of the balance, kind of filling the vacuum that AI doesn't provide.

So we will keep it as coherent as possible.

Pat McIlwee with William Blair. Thank you guys for doing this. Great presentation today.

Matt, something you said at Appian World was just because you can replicate some of this functionality with probabilistic AI doesn't mean you should, right?

And something Mark talked about just now was value-based selling of the solutions. So my question is really, how do you present this to your customers when you go out and talk to them?

And in the context of seeing a number of enterprises blowing through their token budgets this year, how do you go out and show them the value that you're providing for the cost and what that looks like relative to the kind of risk-adjusted ROI of trying to replicate this with more generalized technology?

It's such an important point that you're making there about the token budget, about the cost of AI, which is frankly the elephant in the room right now because nobody is really talking about the cost of AI because it's not passed on to the customer.

Today, AI is heavily subsidized, but someday – and maybe in line with the Anthropic IPO or so – someday the price of AI is going to reflect the cost.

And when it does, this is going to be 10x the concern that it is right now. We're blowing through a lot of tokens right now. People don't feel the pain. When they do, they're going to be more interested in a portfolio approach.

Not every job should be delegated to an agent. Some of them should – if you need an agent's judgment, if you need its intelligent adoption, then yes, it should go to an agent.

But a lot of jobs should go to a rule or a bot or an API or a process in some other way. And so we bring the whole portfolio to those moments and the economizing consumer of digital workers will wish to use the portfolio and create a balance.

That doesn't feel like a main driver today. I mean you mentioned it, so it's not totally off the radar, but it's going to be a much bigger driver a year from now, I expect.

And Pat, since you were at Appian World, you probably talked to some customers. Our customers intuitively get this. Some just got it from the beginning; others went and spent money and got burned.

But this idea of a portfolio and the right tool for the right job is resonating, and that's frankly what gets us in the room. That's what gets us talking about value.

Great. Steve Enders from Citi.

Maybe following on the kind of the last point, but I think part of the presentation, there was a lot of focus on application modernization and getting customers to move things from an old architecture and the proliferation of coding tools out there.

What's the pitch for why a customer should decide to pick a platform rather than deciding to build custom code utilizing coding agents?

Yes. Okay. So you should use the platform because the platform is a reliable, modernized vehicle that will keep you safe in the future.

It's also exceptionally reliable, and we're going to guarantee that. It's connected to modern functionality like Appian data fabric and common shared applications.

You can merge all of your applications onto the modern platform. So basically – and it's all that and plus the speed and the security with which you can make the migration.

I think that some code stacks are going to turn into new code stacks. I don't propose that everything should be converted into an Appian application.

But for those that need the most reliability or would benefit from the power we bring or need to be combined with other applications and use common resources, I think we've got a great value proposition for that set of applications when they are converted from legacy status.

All right. Makes sense. And then on, I guess, the go-to-market approach again, it seems like there's a lot of focus on continuing to drive within the existing customer base and upsell those. But how are you kind of thinking about the segmentation between that proliferation within the customer base versus focusing on the net new logos? And how is kind of the sales force segmented to target that – what is it – 80% of the Global 2000 in key industries that you're not in yet?

Yes. Well, we don't do 100% farmer split. We do value new logos, right? So there's a benefit for that. There's a remuneration for that.

And we see a lot of upside in the logos we've got. So we're doing both, and we're doing both with the same account executives.

Yes. And where you think you'll probably see more specialization is not by hunters versus farmers, although that's a possibility, but more by industry verticals.

So right now, we do some of that, but we can do more of that over time, particularly as the sales force grows because our rep population is very small compared to the opportunity that we see ahead of us.

We're not going to get there in a day. It's about building a consistent journey, but that's the journey that we're on.

Great. This is Devin from KeyBanc Capital Markets. Really great presentation today.

I started to follow up on kind of the topic of pricing. I know we talked about a lot focusing on value. But when I look at kind of that slide of you guys showing AI usage is growing exponentially, right?

I guess the question is, are you guys perhaps leaving some value on the table? Or maybe just give a little bit more details on what are you guys doing exactly to capture more of that? Are you embedding more consumption components to capture the upside there?

Yes. So the first thing I would say – that chart was all production. So we exclude tinkering and proof-of-concepts and so forth. So the growth that you're seeing is real customers using it in production.

The last couple of quarters, in particular, is driven by DocCenter as the use case that is the broadest and where we're seeing the biggest traction at this particular moment.

And so it's a journey, right? And that's why it's important to think about our AI monetization strategy in 3 pieces.

First, you want to get customers onto the Advanced tier, which gives them access to AI features in production. And that also gives you a certain amount of usage that you get to use for that incremental 25% to 35% uplift that you pay us, okay?

And we talked about on the call, 40% of our customers have some portion of their ARR estate on the Advanced tier.

The second is we continue to grow ARR from those customers and move more and more of their estate on the Advanced tier. And Matt showed the slide that showed Advanced tier ARR, roughly $100 million in the first quarter, and that kind of continues growing up and to the right.

And then the third is what you're talking about. If that consumption keeps growing, hockey-stick up and to the right, more and more customers will get to the point where their moderate sort of amount of consumption that's going to build over time is no longer sufficient – and then we go back to them.

Okay. I guess another question for you, Serge.

More room for improvement there. Is it fair to say you would continue to kind of be in this modest investment capacity phase while kind of expanding that 100 bps expansion maybe beyond '26? Is that the right framework to think about?

So we have ability to leverage every single one of our lines in our OpEx, while hoping to further improve those returns from that 0.6 – needs to keep going up as well.

So we think we can do all of that at the same time while delivering meaningful margin expansion over time.

I wanted to touch on – wondering what's driving that? How are you able to implement faster?

Is that in terms of the degree to which we saturate a customer opportunity? Once we're present at the end of one value-creation act and can be there, then it does accelerate them. But I wouldn't say that it's helpful in accelerating reps.

Yes. On the rep side, they're ramping faster because Mark and team are hiring better.

Second of all, because we've built real enablement muscles. And the time to value, I think you heard our friend Dan from PwC talk about this – it's because with AI tools, it's faster to capture what you can do and show what's possible.

And that's what partners and customers are getting. And if you reduce the cost of implementation of a project, well, maybe I can't afford that when it's a certain amount. But if it's a meaningfully lower number, I can stack more of them in my budget and drive further automation and efficiency in the business, whether in the form of revenue or OpEx.

And that's what frankly we're seeing.

But on the legacy app modernization opportunity, it was encouraging to see the TAM slide and how large of an opportunity that represents.

But could you just level-set us on how this has been a driver over the past couple of years? And how much of a step-change do you expect the AI unlock to drive over the next couple?

Yes. So I'll start with that. So that slide of logos of people where we deliver significant value – that's sort of the old-school modernization, frankly, a very people-intensive process.

Some of those customers were willing to do it because of the value that they saw at the end of it.

Now with AI, we're able to reduce the cost of that modernization, reduce the burden on the organization.

And you've seen a couple of case studies of customers who are seeing that. And as that happens, incremental portions of that TAM are going to get opened.

So thank you all for coming. Really appreciate it. We know that it's not easy to spend a chunk of your day with any particular company. So we are very, very grateful, and you know where to find us with the follow-ups. Thank you.

Good day, and thank you for standing by. Welcome to the Appian First Quarter 2026 Earnings Conference Call. [Operator Instructions] Please be advised that today's conference is being recorded.

I would now like to hand the conference over to your first speaker today, Brian Denyeau from ICR. Please go ahead.

Thank you. Good morning, and thank you for joining us. Today, we'll review Appian's first quarter 2026 financial results. With me are Matt Calkins, Chairman and Chief Executive Officer; and Serge Tanjga, Chief Financial Officer. After prepared remarks, we'll open the call for questions.

During this call, we may make statements related to our business that are considered forward-looking. These include comments related to our financial results, trends and guidance for the second quarter and full year 2026, the benefits of our platform, industry and market trends, our go-to-market and growth strategy, our market opportunity and ability to expand our leadership position, our ability to maintain and upsell existing customers and our ability to acquire new customers.

These statements reflect our views only as of today and don't represent our views as of any subsequent date. We won't update these statements as a result of new information unless required by law. Actual results may differ materially from expectations due to the risks and uncertainties described in our SEC filings.

Additionally, non-GAAP financial measures will be discussed on this conference call. Reconciliations to -- of GAAP to non-GAAP financial measures are provided in our earnings release.

With that, I'd like to turn the call over to our CEO, Matt Calkins. Matt?

Thanks, Brian, and thanks to everyone for joining us today. In the first quarter of 2026, Appian's cloud subscriptions revenue grew 25% year-over-year to $124.5 million. Subscriptions revenue grew 19% to $160.3 million. Total revenue grew 21% to $202.2 million. Adjusted EBITDA was $26.6 million.

Our weighted Rule of 40 scored 42, the highest level since we introduced the metric last year. Our go-to-market efficiency metric posted its 11th straight quarter of improvement. Appian continues to build on our success in 2025. We met or exceeded financial expectations in Q1 and raised full year guidance. Serge will share the details.

Last week, Appian announced the results of a study done with the Harvard Business Review on the state of AI in the workplace. It captures this unique moment in which every organization intends to use AI, but many struggle to get value from it, especially in the most important use cases. HBR found that AI is used more for personal efficiency than it is for strategic applications.

If an application is customer-facing or makes business decisions, it's probably not benefiting from AI. Appian's purpose is to bring AI into mission-critical applications, at large regulated companies where errors are not acceptable. We make AI reliable enough for such use cases by wrapping it in a deterministic framework of process technology. AI is a probabilistic technology unreliable by nature, while the most valuable use cases require complete dependability.

HBR's study shows how corporate users know what's needed to make their AI reliable. 92% know they need guardrails for AI, though most have not created them. Most intend to integrate AI into process, though only 18% have done it. Organizations now understand how to equip AI for serious use cases even if they haven't done it yet. HBR's conclusion states and I quote, "The next phase of AI maturity will depend on embedding AI directly into the core of how work gets done." Appian has been embedding AI into the core of how work gets done for years, with our leading process automation technology. My conversations with customers indicate that we've helped them move faster than the market as a whole. Nearly 40% of Appian customers have purchased our AI-inclusive license tiers. Driven by AI demand, our 2026 pipeline is above our expectations and a key factor in our increased guidance for the remainder of the year.

Excitement over Appian AI was evident at our annual user conference, Appian World, which took place last week in Orlando. Our theme was serious AI, meaning AI used for strategic and valuable work. Our point, of course, was that serious AI requires process. Over 1,000 customers, prospects, partners heard from Appian experts and peer organizations, including Citi, Pfizer, Merck, GE Aerospace, GE Healthcare, NASA, AARP, Regeneron, Munich Re, CIBC Mellon. Customers reported that AI transformations are increasingly a Board-level priority. AI alone operates at a low level of reliability.

But with Appian's framework, AI can work and write applications at a high level of reliability. We've created technology that complements AI, enabling it to be used in the most valuable situations. Appian DocCenter is a great example of deploying AI within process. DocCenter automatically extracts data from incoming documents, then takes action accordingly. DocCenter runs at scale with over 95% accuracy, significantly higher than the 60% accuracy of traditional document recognition technology. Our customers processed more document pages in Q1 this year than they did in all 2025 combined.

Production use cases span all major industries. I'll share a few customer examples. First, an international insurance company is automating processes and working to eliminate $100 million in operational costs by 2030. It named Appian its AI document intake standard after DocCenter processed complex unstructured physician statements with 98% accuracy.

Next, a global medical devices company manages its order to installation processes on Appian. This quarter deployed DocCenter to automatically compare order packages against client documentation. It can now process items 80% faster. Once rolled out globally, Appian will validate 100,000 orders annually and save the firm an expected $16 million in operational costs over the next 3 years.

Finally, a top oil and gas company has been an Appian customer for several years. It uses our platform to onboard customers and suppliers 70% faster than before. This quarter, its finance department chose Appian to spearhead its AI transformation and purchased a 7-figure software deal. Appian will automate the procure-to-pay process, starting with invoice payments. DocCenter will extract data from millions of supplier invoices annually and automatically reconcile them against the company's order management system. Appian will provide significant labor savings and help the company achieve its goal to reduce operating costs by $400 million by the end of 2027.

Legacy modernization is a fast-growing component of our business and perhaps the most popular topic at our conference last week. C-level executives respond immediately to the promise that we can migrate their legacy apps to our modern platform. According to McKinsey, 70% of Fortune 500 software is over 20 years old. We've been doing modernization migrations for a decade with good results. The U.S. Air Force saved $80 million after modernizing its tech stack with Appian and Hitachi consolidated over 500 systems into a single central Appian application.

Legacy modernization may be an idea whose time has come. New AI technology has expanded the opportunity by lowering the cost and increasing urgency. The cost is lower because natural language development is now a mainstream way to compose applications in Appian. The urgency is higher because products like Anthropic's Mythos threaten to expose security weaknesses in all applications, especially old code stacks without modern support. Many applications cannot be vibe coded written by AI alone.

As I often say, code may be cheap, but mistakes are still expensive. Important applications will require a greater degree of reliability and precision, which Appian provides. We make AI enterprise-grade reliable in writing apps, just like we make it reliable in doing work. For example, a major European automotive manufacturer manages supply chain operations, finance and warranty claims on our platform. In Q1, it named Appian as its core modernization platform and purchased a 7-figure deal for more software licenses.

The company's sprawling tech stack includes over 3,000 outdated and incomplete applications. Now Appian will unify the enterprise as the organization decommissions legacy systems. The manufacturer aims to reduce its application landscape by about 40% as it builds enhanced workflows on Appian. Customers have strong interest in Appian's Agentic AI. Like all types of Appian AI, our agents are informed by our data fabric and deployed within the guardrails of our process.

For example, a leading telecommunications company is using Appian to unify its digital advertisement operations. This quarter, it decided to automate compliance reviews and purchased more Appian licenses. Every network and streaming provider has unique rules about when and what type of content can be positioned on their channels.

Before Appian, the company validated content manually. Now Appian data fabric will unify client policies so our AI agents can reference a holistic data set. Our agents will verify thousands of ads every day and flag outliers that need human review. Early results suggest Appian agents will achieve 98% accuracy and require 33% fewer resources. The AI economy asks for transparency and openness. Appian is a long-standing believer in these values, as shown in our data fabric that unifies distributed data sources without moving them.

We've embraced what I call the 3 rules of the AI ecosystem: be useful; be open; and be safe. Our technology utilizes MCP inbound and outbound. Data fabric is an ideal data source for AI agents because it is comprehensive, open, performant and secure. You can now deploy, develop -- you can now develop Appian applications without ever opening an Appian interface, entirely from an AI command line in a product like Cloud or [indiscernible].

Our AI-enabling layer is also AI agnostic, preventing AI lock-in and empowering our clients to switch AI platforms in the background without losing any of their capabilities. Appian is off to a strong start in 2026. Our position as an essential enabler of AI continues to drive business momentum as customers gain real-world value from our platform.

With that, I'll hand the call to Serge.

Thanks, Matt. I'll begin with a detailed review of our first quarter results and then finish with our outlook for the second quarter and full fiscal year 2026. Starting with Q1 results. We had a strong quarter of new business driven by continued AI traction and ongoing momentum in our focus on the high end of the market. The standout performer was our EMEA region. Cloud net new ACV bookings were approximately 82% of total net new software bookings in Q1, consistent with the prior year. Appian exceeded the guidance ranges we provided on our key metrics of cloud revenue, total revenue and adjusted EBITDA. Cloud subscription revenue was $124.5 million, an increase of 25% year-over-year.

On a constant currency basis, cloud subscription revenue increased 20% year-over-year. Total subscription revenue was $160.3 million, an increase of 19% year-over-year. On a constant currency basis, total subscription revenue grew 15% year-over-year. Professional services revenue was $41.9 million, up 31% compared to the first quarter of 2025. Total revenue was $202.2 million, an increase of 21% year-over-year. On a constant currency basis, total revenue grew 17% year-over-year. Our cloud net ARR expansion was 115% in Q1 compared to 112% a year ago and 114% in the prior quarter. As a reminder, we present net ARR expansion on a constant currency basis.

Now let's turn to profitability. I'll be discussing our results on a non-GAAP basis unless otherwise noted. Gross margin was 74% compared to 75% from the year ago period and 73% in the prior quarter. Our subscription gross margin was 86% compared to 87% in the year ago period and consistent with the prior quarter. Professional services gross margin was 29% compared to 25% in the year ago period and 23% in the prior quarter. Total operating expenses were $125.6 million, up from $110 million in the year ago period. Adjusted EBITDA was $26.6 million, ahead of our guidance range of $19 million to $22 million and compared to adjusted EBITDA of $16.8 million in the year ago period. This outperformance relative to our guide was largely driven by greater-than-expected revenue.

Net income was $19.8 million or $0.27 per diluted share compared to net income of $9.8 million or $0.13 per diluted share for the first quarter of 2025. This is based on 74.4 million diluted shares outstanding for the first quarter of 2026 and 74.5 million diluted shares outstanding for the first quarter of 2025.

Turning to our balance sheet. As of March 31, 2026, cash and cash equivalents and investments were $206 million compared to $187.2 million at the end of last year. For the first quarter, cash provided by operations was $48.8 million compared to $45 million for the same period last year. In the first quarter, we purchased $21.8 million worth of our stock.

Turning to guidance. Starting with the second quarter of 2026, cloud subscription revenue is expected to be between $126 million and $128 million, representing year-over-year growth of 19% at the midpoint of the range. Total revenue is expected to be between $191 million and $195 million, representing year-over-year growth of 13% at the midpoint.

Adjusted EBITDA for the second quarter of 2026 is expected to be between $5 million and $8 million. Non-GAAP earnings per share is expected to be between negative $0.02 and $0.02 per share. This assumes 74.2 million fully diluted weighted average shares outstanding. For the full year 2026, our cloud subscription revenue is expected to be between $515 million and $521 million, representing year-over-year growth of 18% at the midpoint of the range. Total revenue is expected to be between $819 million and $831 million, representing year-over-year growth of 13% at the midpoint.

Adjusted EBITDA is expected to range between $97 million and $105 million or an approximately 12% margin at the midpoint of the range. Non-GAAP earnings per share is expected to be between $0.94 and $1.05 or approximately 60% growth at the midpoint. This assumes 73.9 million fully diluted weighted average shares outstanding.

Our guidance assumes the following. First, we anticipate our non-cloud subscription revenue to be down in the mid-single digits in Q2 related to timing of renewals versus Q3. For the full year, we expect non-cloud subscription revenue to be flat to slightly up. Second, we expect professional services revenue to grow in the high single digits in Q2 and low double digits for the full year. Third, total other income and interest expense will be approximately $3 million in Q2 and $12 million for the full year 2026. Fourth, our guidance assumes FX rates as of early May. Please note that we expect FX to benefit our reported revenue growth rate by roughly 1% in Q2 and have a neutral effect for the rest of the year.

Finally, Q2 is our seasonally high quarter for marketing and event expenses, impacting our sequential EBITDA guidance. For the full year, we are raising our EBITDA guidance as we expect more than 1 percentage points of adjusted EBITDA margin expansion in 2026.

Before wrapping, let me also touch on our increased share repurchase program. As most of you know, we have always been careful about dilution. Thanks to a strong start to 2026 and our increased guidance, we are in a position to increase our buyback authorization from $50 million to $100 million. We plan to execute this buyback during 2026, which will reduce our overall share count this year, driving further growth in our earnings per share.

In closing, we're pleased with our Q1 results. We are excited about the opportunity ahead, and we'll continue to invest responsibly to maximize our long-term value. We look forward to seeing many of you next week at our Investor Day in New York. We'll be sharing updates on our product and strategy, and you'll have the opportunity to hear directly from our customers and partners. If you'd like to attend, please reach out to [email protected].

Now we'll turn the call over for questions. Operator?

[Operator Instructions] Our first call comes from the line of Steve Enders with Citi.

Maybe just to start, I want to dig in a little bit more into just the conversations you're having around Agentic AI with your customers. And I guess, where are we in terms of customers just starting to deploy agents in production? And maybe what kind of the initial conversations like, as they're beginning that and maybe moving from experimentation into actual production use cases?

I have heard lately that there have been a lot of concerns across the economy about the efficiency of agents, the return on investment that agents have provided. And I first of all want to say that our conversations are in contrast to that. I think we've been really deliberate and practical about the way that we've deployed agents, and they've been adopted as such. And we are, therefore, a high ROI agent vehicle. I've also -- I want to mention that some have considered the agent -- agentic decision-making capability to be, in some ways, a substitute for the process model's decision-making capability. And in fact, I find that agents are not substitutes but complements and need process more than any other form of AI, in my experience.

Agents are -- they need the guardrails, they need the tracking. They need the support and the teamwork that process provides. I have enjoyed seeing how agents have fit into a team and a portfolio approach. And at this point, when customers ask us where they should use a process to make a decision versus where they should use an agent to make a decision, I feel like we have a clean and time-tested answer to that, which is that it depends on whether you need adaptive intelligence at that point in the process sequence, if you have a lot of ambiguity. If you have a -- an extremely large context set that can't be contained within rules, then you're going to need some adaptive intelligence. But otherwise, you should stick with process to make those decisions because it's faster and cheaper and more accurate. And best of all, of course, is to have agents at your disposal, but also have the rest of process and automated decision-making at your disposal so that you may select the appropriate tool at the appropriate moment, which I call a kind of a portfolio approach.

I think the portfolio approach is going to be especially important as we go into the future and we depart eventually from this era of subsidized AI. Today, AI is underpriced, but we all know that, that can't last forever, and it's already pretty expensive. And over time, AI is going to rise to its cost of provision more or less. And when it does, there'll be all the more pressure to find a cost-effective and equally accurate manner of making a lot of these decisions. And that's where the portfolio approach that the process provides will come in. So anyway, good feedback on our agents, good usage and particularly, I think, differentiated ROI from what I understand to be happening in the rest of the market.

Okay. That's very clear, and it's great to hear. Maybe just switching gears a little bit to the outlook. It sounded like the pipeline and the build there is coming in pretty solid. Just maybe what are you seeing in terms of top of funnel, like what's driving those incremental use cases? And I guess, what does that kind of imply as we think about the guide, what's underpinning, I guess, either productivity rates or close rates and the assumptions there, especially compared to some of the volatility that we're hearing about in the macro situation right now?

Yes. Yes, we seem to be cruising through that volatility on a macro basis pretty solidly. I think what's driving our pipeline is the fact that we've got the answer to the biggest question in business now, which is how do you apply AI to strategic applications. Everybody wants to do this. Study after study shows that it's the new frontier in AI, how can you attach it to complex, error-intolerant, mission-critical regulated processes and get value, and we're doing it. Time and again, we have the answer to that question and the enthusiasm and the electricity at our show last week showed us that people are delighted with the actual returns they're getting on our investment, and they're deploying AI places they wouldn't have been able to deploy it otherwise, because of our deterministic process layer.

So I think that's what's driving the pipeline is the fact that we've got a reliable answer to this large unanswered question.

Steve, the only thing that I would perhaps add is a particular area of strength is DocCenter. We see it as the first very broad use case, both across industries and geographies. It just applies to so many situations. And as Matt said, customers are leaning in, and that was also obvious last week in Orlando.

The next question comes from the line of Raimo.

Congrats the great quarter. What are you seeing at federal was -- you have a fair amount of exposure there, and that was a big discussion point, but it does seem like the world is changing there again. Can you speak a little bit what you're seeing there and how that's playing out for you?

Great. What was that critical word? It dropped out on my phone?

Federal. Federal.

Thank you. Thank you. Okay. So yes, we've seen -- I feel really good about where we stand in federal right now. This has been an arena where efficiency has mattered more than ever, where technology has been a means to an end and not just cost cutting for the last 5 quarters. This is an arena in which we can win. And I feel like we've got momentum. We've got pipeline. We've got access to legitimately larger deals than we have done in the past. I am excited about where we stand in federal and very pleased with both the -- our capabilities and our performance.

Okay. Perfect. And then one for Serge. Serge, the good profitability performance this quarter as well. So it's like a nice combination of revenue growth is getting better, but profitability also kind of improving as part of that. Can you talk a little bit about like how sustainable is that? Like what drove that? What can we expect?

Sure. So first, I'll just kind of remind you that we've been doing a good job of balancing growth and profitability for a while now. So if you look at 2024 and 2025, between those 2 years, we've expanded our margins by almost 20 percentage points. And I think of 2026 as a year in which we're building foundations for sustainable and efficient growth going forward because, first, we're returning to growth in our sales org, not because it's going to have a dramatic impact on this year's numbers, but because in order to sustainably grow the company, you need to grow your field operations. And obviously, we all have aspirations for Appian to be much bigger than it currently is.

Secondly, we're continuing to invest on the R&D side, mostly in India and also investing in AI capabilities, and I really applaud our R&D leadership for using AI to rethink how to develop software.

And then finally, building backbone systems and processes, including AI, especially Appian's AI, to, again, put us in a position to grow efficiently going forward. So in that context, we are showing margin expansion this year despite the incremental investments and the return to investing that we're showing, and it's absolutely sustainable going forward.

The next question comes from the line of Pat McIlwee with William Blair.

This is Jacob on for Pat. I just wanted to ask about cloud NRR ticking up to 115%. Is this being driven more by AI tier upgrades and the 25% price uplift? Or is this more customer adoption across just new workflows and more penetration within the base?

Yes, it's both. I would say it is the 2 themes that we've been talking about for over 1 year now. One is AI adoption. So we're continuing to have success in upgrading our customers to the advanced tier. Matt just mentioned that nearly 40% of our customers have some percentage of their ARR on the advanced tier. And then the second one, honestly, is just our move upmarket, selling large strategic deals. Our sales team is doing an excellent job at that, and there's still plenty to go.

Got it. And then one on go-to-market. You talked, I think it was last quarter about it being the strongest for commercial in North America in 3 years. Are you seeing some of that translate into Europe or APAC regions?

Yes. In fact, we called out Europe as the standout performer this quarter. Some of our largest deals, including a couple that Matt mentioned in his prepared remarks, actually came from that region. And look, the story with Appian and the go-to-market is that we've done a lot of work to transform our sales org, including new leadership in selected places. Our EMEA leadership started, I want to say, roughly middle of last year, and they're putting incremental rigor and focus on value in places -- in place, and we're seeing -- starting to see evidence of that in Q1.

The next question comes from the line of Sanjit Singh with Morgan Stanley.

Congrats on the cloud revenue acceleration this quarter. Matt, I thought you made a really excellent point on the era of kind of token subsidy and subsidization potentially going into a rearview either now or very shortly. I was wondering if you could unpack for us a little bit about how you guys are going to be a destination to drive more efficient AI and why that would be the case relative to some of your competitors and your peers?

Absolutely. For us, it's all about the portfolio. We've got so many ways to make a decision, so many ways to automate a job. You don't need to rely exclusively on AI, which obviously has 2 huge faults. One is that AI is not the right tool for every job. And the other is AI is the most expensive of your portfolio in many cases. So for efficiency and for accuracy sake, you want a portfolio of tools at your disposal and our approach to process does that.

Sanjit, I would just add one more thing. The portfolio applies even in the case of AI. We have customers sometimes coming to us and saying, "I want an agentic use case." And then when we unpack it, we realized that, that would not be efficient. And instead, what we have them apply is more narrow uses of AI and better control in the process that results in at least as good of accuracy and much better performance and ROI.

The next question comes from Devin with KeyBanc Capital Markets.

Matt, you briefly mentioned it in your remarks, but would love for you to just talk a little bit more and give a few highlights coming out of your Appian World Conference. What were some of the major product updates? How are customers doing those updates? And maybe just speak to how the conference has been for you guys?

Yes. Well, for me, the conference is sitting in the back room with one major customer after another rotating through. And I hear after the fact what happens on the main stage. But I do pick up a lot of enthusiasm in the hallways. I love the fact that people are talking about constructive numbers. ROI is everywhere, enthusiasm around what they're building is everywhere. We've got a very confident group of users, and they're making value. So that's all terrific. I love how it's becoming clear how they benefit from AI. In years past, I don't just mean for us, I think even more broadly around the industry, there's been enthusiasm, but on the lack of specifics, a lack of certainty around AI. And I feel like that has come into a sharp focus, and now we have dependable lines, how to use it. We know just what we're doing. DocCenter is an example, everybody can follow and the use cases are becoming crystal clear. I think it's also clear that a deterministic framework must be part of the AI stack.

By the AI stack, I mean technologies that are necessary for AI to grow and succeed. Some people think there is no AI stack if there's only AI, but I disagree with that. I think that we're capable of offering something here that AI truly needs. We're not going to be the only ones offering a process orchestration deterministic framework. There will be others. There'll probably be a lot of lightweight options, but we've got a heavy weight. And it's ideal for complex and mission-critical work.

I love the kind of maturing understanding of the technologies that it will be necessary to deploy AI successfully and to expand with it. And that's another thing that we have a lot to say to and that we're going to benefit from in concert with our customers as we grow.

I appreciate the highlight there. Really great to hear. And then maybe just one quick one for Serge. Really strong start to the year here with 25% cloud subscription revenue in the quarter. When I look at kind of the implied second half cloud guide, it seems like you're expecting a bit of a deceleration there. Can you walk through some puts and takes there? Why couldn't we see the strong high teens, 20% plus growth kind of sustaining into the second half year, just given the strong pipeline commentary you guys have been talking about?

Yes. So I would say 2 things. One, we're happy to be able to raise guidance for the full year this early on, and we raised it from 16% to 18% at the midpoint after the first quarter here. And then as I think about sort of the shape of that growth, maybe the most helpful way to think about it is we did just do 25%, but on a constant currency basis it's 20% and we're very happy with that number. It's the fastest in 2 years for us. But we don't see very much currency benefit for the rest of the year. So I think of comparing that 20% for the -- in the first quarter compared to 18% for the full year, and that hopefully gives you a little bit of a better sense in terms of the linearity, if you will.

[Operator Instructions] The next question comes from Derrick Wood with TD Cowen.

This is Cole Erskine on for Derrick. Matt, you talked about MCP access to the platform and how customers can use Appian and the data fabric without ever going directly into the platform. I mean, can you just talk about how this -- how you're seeing this change to usage patterns so far? And then on the economic side, is there any different puts and takes on monetization if you're -- if a customer is using Appian through Quad interface versus your first-party?

Yes. Okay. Well, let me start with just how the ecosystem is shifting in the AI era. I see a enterprise future with fewer rigid predefined lines of communication and more fluid, unpredictable -- It's like we're going to take all of our kind of boxes and institutions and blend them up or make a primordial soup out of all of these entities across the enterprise.

Therefore, I think every object has the responsibility to be ready to field questions or ask questions as part of improvised networks of communication. So that's our intent with Appian to be very open and transparent and always extremely secure using credentials, et cetera. So we're a full participant in that kind of economy. And it is extraordinary now that you can develop an entire Appian application from front to back without ever opening an Appian interface. That may not be the best way. I would say that is not the best way to develop an Appian application, but it is possible. And the fact that it's possible is a demonstration of just how open we've made this product.

I also want to point out just how valuable data fabric will be as a means to survey the entire enterprise worth of data for future agents. As agents get more savvy about where they can find data and where they can get it in a performant manner and interact with data, they'll focus in on those data providers that can give them more than one silo at a time, and they can give something with a fast response time. So we are -- we're well positioned to be part of that emerging ecosystem. So that -- we're excited about that. As for how you monetize it, well, I think it's important you just put a charge on all external inbound requests. So that's how we do it.

Thank you. As I'm showing no further questions, this does conclude the question-and-answer session. Thank you for your participation in today's conference. This concludes the program, and you may now disconnect.

Since we've got some lunch, super happy to have the Chief Financial Officer. I must also say the relatively new Chief Financial Officer from Appian, Srdjan Tanjga. Srdjan, thank you. Welcome back to the TMT Conference, but this time as the CFO of Appian.

Good to be here.

Awesome. Let's -- Before we get into the conversation, I'm just going to go to disclosures. For important disclosures, please see the Morgan Stanley research disclosure website at www.morganstanley.com/researchdisclosures.

So with that, maybe to kick off the conversation. You joined Appian in the middle of 2025 after spending over a decade in MongoDB. For those new to the story in terms of Appian, can you pinpoint the problem or problems Appian is solving for customers?

Yes. So Appian is a process automation platform that focuses on mission-critical use cases, especially in highly regulated industries. And that's a mouthful of catchphrases. So maybe I thought I'd just make it real with a few examples.

So for example, one of the largest asset managers in the world, one that has a lot of people running around these halls today is using us -- has automated a process to onboard and manage their customers and before that, they were using largely a manual process. So this was a cost saving and revenue growth exercise. Or a top Australian bank is using us for our credit card dispute resolution, and they were replacing an internal app that was clunky and it wasn't scaling. Or a medical equipment manufacturer that's using us from order to install process for their equipment, and they had a point solution from another vendor that also wasn't performing so they placed it on Appian or since we are a big government player, large civilian agencies using us to -- for automating a process to identify and resolve fraud. And before that, they were doing manually about pulling information from multiple systems and obviously, involving a tremendous amount of person hours.

If you take those examples and kind of boil it down, we work with large enterprises and the public sector to automate mission-critical processes, they usually spend across different silos of information inside the company or frequently involving the customer. And we usually replace either manual effort underperforming custom-built application or any number of legacy solutions. We've been in the business for over 25 years and we've guided to roughly $100 million of revenue, and we think it's a very exciting opportunity ahead of us.

Yes. I've been around for every single Appian quarter. And I think in your answer, I heard the word 'Process' multiple times. And I think there's still some of that lingering impact from -- when we did the IPO of 2017, before your time around being a low-code platform. So I think there's still -- there's a portion of the market that still thinks that Appian just builds websites and those types of things and not necessarily tied to a process.

Let me talk about that because it's a relatively recent outside or third insider. It was a moment where it sort of dawned on me and how wrong this is. And so -- and what I mean by this is this idea that we're -- there's this low-code, no-code space, that over time has become associated with, call it, a citizen developer who takes a few hours of training and then go build something relatively rudimentary to help in the day-to-day job. And that was what people in this room, myself before coming to Appian would have probably thought about it.

And then we realized almost all of our software is implemented by a third party, either ourselves or any number of our partners, [ GSIs ], smaller companies and so forth. And customers pay us from 5 to 8 figures for these implementations. And I think the reason why that -- once they kind of put that together, the reason why -- they realized to me is like, what we're doing is not easy and to implement and build the software in our platform is actually requires expertise that most customers do not have, all but relatively small of our largest customers have.

And I see that , I think, puts the moniker low-code into perspective because it's low-code in a sense that like you're not hiring $250,000 developers and keeping them on your platform to build custom code for you, you're using a third party that implements a composable, reusable solution. But what we do is hard and very sticky, as you can see from our numbers.

Yes. So let's dive into because that's not only the core debate with Appian, but core debate across software is sort of defensibility in the age of AI and the risk of AI and disintermediation. When we look at like from your customer conversations, what specific use cases or system requirements, make customers conclude they need the Appian platform rather than building AI-native automation solutions are working with one of the research labs to build their own sort of genic autonomous solutions when it comes to automating their workflows.

Yes. So let's dive right in. So I've been in and around Wall Street for close to 25 years, and I say that Wall Street time and Main Street time, the clock takes differently and sometimes completely differently and unrelated. And I can't remember a time when it was a bigger dichotomy between investor conversations and actually what we hear from our customers.

So in rooms like this and in the room that I've been all day and going back to after this, there's questions about AI becoming self-sufficient and obviating the need for software, including Appian. There's a question about agents running other agents reporting to sort of across different silos and enterprises going to some things called control towers. New competitors are emerging to displace people like us, you've been in the business for a long time. And that's what I hear in investor meetings and kind of find myself discussing.

Customers are in a completely different place. Customers are still looking, for the most part, for the first successful use case of AI in production. I don't mean give your employees a CoPilot or a tool that makes them productive, makes them write better e-mails. I'm actually talking about at scale with high accuracy inside a process that runs thousands if not millions of times a year. And so -- and the reason why customers are struggling with that because AI is a -- I know you've learned this term now, but I'm going to repeat it anyway, a probabilistic technology that needs to be fit inside of a deterministic system to produce the outcomes that is needed when you're doing something mission-critical, like customer onboarding, like procurement, like budgeting like the kind of stuff that Appian gets involved with.

So with them, the conversation is different. The conversation is, I want to see value. I believe in your vision in terms of delivering that value, meaning as AI as a node in the process as opposed to some replacement of the process. You guys have the credibility to do that because I've worked with you and my peers have worked with you for a long time. So let's partner together and do that.

And so I'll give you an example. A North American insurance company who's approached us about doing the first in production case of our product called DocCenter, which is AI-enabled document extraction. And again, there are plenty of tools in the market, low accuracy what we're doing inside a particular process is capable of getting to high 90s or better accuracy. So we partnered with them, first use case, 400,000 documents per year. It took us a couple of months roughly to implement it because, again, you want to tune it, touch it that it's accurate enough, and they're over the moon. And we're talking about the second one. The second one is 1.2 million documents per year. And so most customers are still before that first use case.

When they are engaged and when they're ready to talk about AI adoption, we see our win rates being meaningfully higher than they are normally and we're happy with our win rates as they are, which again speaks to our vision of -- processes as an essential enabler of AI is really resonating with the customer market. And then what we're going to see over time is more use cases, ability to upsell customers, and we generally think it's a great tailwind for everybody.

Yes. When we think about one of the aspects about Appian's business is 80% of your revenue, roughly 80% of your income comes from like highly regulated industries where customers value compliance, audibility, reliability. When you think about what's going to keep Appian defensible for the next several years, is it the governance framework? Is it your implementation, domain expertise? Is it the support? Or is there something more fundamental to how your platform is architected?

Yes, you're correct. We operate in the most demanding, most highly regulated, most risk-averse industries that are out there. So 80% of our business comes from government, financial services, insurance and health care. And so -- and we have, again, a long history of subject matter expertise and individual solutions provided in that space.

As you think about sort of our framework around process generally is that you want to deploy a best tool at every node of the situation. So historically, those are business rules engines than there were RPA bots, there was process mining. AI is another worker to draw into the process. At the right moment, under the right circumstances. You certainly don't want to use AI indiscriminately simply because it's not the best tool for the job. You wouldn't make AI do math for example.

And so we bring that framework to our customers. And then on top of that, we provide them with incremental functions like security, like auditability, like compliance and certifications, which, frankly, you would not ask AI to create. And that comes all in the context of complex workloads that need to have higher level of accuracy.

And so those are the things that we think are particularly difficult to -- for AI to ever replace. Not just any particular near-term moment in time, but generally speaking.

So let me kind of take that to an extra credit level of answer. And so one of the things that I've heard in my meetings today and generally speaking, is some flavor of, okay, I get it that this is a near-term positive for you guys. I get that AI fits in the process. But what gives you confidence that it's going to be true 5, 10 years down the road. And that's always a difficult question to answer because it's hard to just prove a negative, particularly when the market seems to be as bearish and as scared as it is right now. But I'll offer you two arguments.

The first one is I think you implied in that question is some sort of capability of AI to become self-sufficient. So no need -- it's going to self-govern. And fundamentally, as a probabilistic technology, it's just very, very difficult to imagine a world in which that happens. So it always needs a set of guardrails and protections around it in order to deliver the outcomes that the enterprise want.

So then the second question in this sort of infinite bearish sort of scenario is, okay, fine. So AI needs that. But why can't another player in new players, new breed provide it. And then we're just talking about competition. And the market has always been competitive. And what we do is exceptionally hard, which is why you see very, very few companies have successfully succeeded and scaled versus many who have tried. So if another competitor comes and needs to build that enterprise readiness support confidence from the customer, particularly in our verticals, I would flip the question. I would ask you, why wouldn't you logically avail yourself of all these tools, all of which are available, and we partner with all of them. to implement properly inside the process in a way that we've done it for a long time and generate value that way, why reinvent the wheel?

That makes a ton of sense. Let's talk a little bit about the AI monetization story at Appian. And I understand that's still pretty early. But on the last earnings call, you noted that AI usage on the platform is up 14x year-over-year. So bigger picture perspective, what are the AI capabilities available to customers today? And how is that monetized?

I'll start with the framework, and then I'll walk you kind of through the progression. So for us, even before Gen AI became popular and became usable, we were implementing earlier versions of AI and ML as a node in our process. And then obviously, when the Gen AI opportunity became clear to the general public, we sort of rolled out a series of features with increased complexity to effectively deploy AI capabilities in the right way.

So first came a set of things that we call AI skills, where you can effectively call an LLM inside the process to produce the exact output that you wanted to produce and that was very popular as sort of like the early use case for customers. Then came DocCenter, which we already talked about a little bit, which is perhaps the most horizontally applicable use case of AI. And there's generally a feeling like this is easy and can be done out of the box and nothing could be further from the truth, especially in enterprises that use decades of all documents to actually extract value from it. And so we've launched DocCenter in late 2024 in a number of successful cases in production across industries last year, and we're really pushing that as something that is broadly applicable it should be a driver of more adoption of our advanced year in 2026.

And then more recently, we've announced Agent Studio, which is a more comprehensive agentic offering to provide more autonomy and more use cases, and we're seeing first customers come to production, and we hope to tell you more of those stores as we go through the year at Appian World at an Investor Day.

And then the final step is what the product will composer, but more generally modernization. You've heard about it, talked about as well. AI offers the promise of modernizing legacy technology that go decades-old portion of the software stack. They just kind of sit there and deploy resources and are very inflexible and difficult to manage. than nowadays, I can at a lower risk be transformed into a modern platform like Appian. Very, very early days. We are partnering with customers that we're seeing some early traction. But that's like -- kind of the Appian journey with AI from the past all the way into the future.

And when you think about those capabilities that you laid out, caused those different dimensions, is that -- that's mostly available in the advanced subscription here today? So for customers wanting to consume these AI capabilities, there's an upgrade or a land potential on the advanced subscription tier. And so the question is, there's also a premium tier. So how do we think about the road map of the premium tier? What's going to be offered in premium versus what's offered in...

Yes. So one thing that I would argue is perhaps different for us versus many of the other companies who are claiming the AI mantle is that we charge you explicitly for it from the outset. If you want to put production use case -- a use case in production, you need to pay us 25%. That's the average realized price of what customers are paying us to go from standard to advanced tier. And then you get to deploy in production, then you get to get incremental use cases. We said two quarters ago that 1/4 of our customers are paying us for the advanced year as evidence that we in fact are monetizing. We're past the product market state of our AI modernization story. And -- but for the time being, the game is still adoption. We want to demonstrate success and we want to be the trusted vendor that the customers do their first, second, third AI use case with. We can sell more of the advanced tier to our customers who already have some licenses on the advanced year. Obviously, we can drive that number of 25% higher. And so that's still the medium term, if you will, goal.

But you're right, we have a premium tier, which is another 25% to 35% uplift. We actually have a relatively limited number of features in there. Surprisingly, we do have a handful of customers who are already paying us this.

But as we achieve sort of seeding the adoption and move into more modernization we will put more features into that tier, and then we'll repeat the game. That's what I like about the playbook in that we know how the game is played over a period of multiple years, and we're well along the way of demonstrating the first step of that process is working.

Understood. From a pricing strategy perspective, the market's been concerned on seat-based pricing models. In your government business, you actually don't price per seat, you guys [ price for app ]. But in the commercial opportunity, there's still significant exposure to seat-based pricing. So as we look at pricing over the next 12 to 24 months, how do you think pricing is going to evolve? And what's the time line for the company to potentially see consumption or utility revenue start to hit the income statement?

Yes. So I think of our pricing tools as sort of a matrix. And what I mean by that is on one axis, you have all the different ways in which we charge. And those are per user, per app. We have enterprise-level agreements that are sort of unlimited in nature. We have consumption both as an overage to other models as well as individual ones. And then you have sort of ways within each of those models to drive incremental pricing. So those tiers, it's pure price increases. We increased pricing every year. So we have multiple tools at our disposal to kind of drive the customer we want them to go.

The one thing I will say though is -- and this is what's different about Appian today than it would have been the case two years ago. And doesn't get discussed as much as I think it should be, which is our go-to-market transformation. We've always had a good product. It's always very sticky. Our customers rely on us to solve the most difficult problems and that's generally just quite remarkable to see when you sit in the room with them. Where we haven't been as strong consistently is in our go-to-market execution.

And what we've done -- and you know this because you've been with us for a long time. But roughly two years ago, we began to more aggressively focused on the upmarket. We focused our efforts there. We actually reduced our sales order about 18 months ago in order to just focus on the top end. And what that really means is selling value. So for example, in the first quarter, we talked about a customer who signed a seven-figure deal with us. It's an aerospace manufacturer and in the process of designing the solution with them, prototyping it, if you will, we conclude that we can save them $60 million, and they agreed.

When you have an agreement that you're saving somebody $60 million, then the question isn't, "oh it's this many users than this price." It said, "I'm going to do this. I can do this for you. We both agreed that I'm uniquely positioned to do this. So I deserve a portion of that." And [ P times Q ] might change. But if you're selling value, and that's the marching order #1 for a sales org in 2025 and 2026 and mindset shift to sell value. And if we sell value, like the units will resolve itself.

Yes. That totally makes sense. Let's talk a little bit about some of the growth opportunities in specific parts of the business and specific verticals. Let's start with federal. So last year, there was a big concern about DOGE and the impact of what DOGE could have on software spending overall. I mean you guys did fantastically well last year when it came to U.S. Fed growing well above the growth rates of the business. I have it at a sort of mid-20s growth in 2025 and accounting for 25% of total revenue. So as we go from '25 to '26 post DOGE, what do you see as the prospects of the federal business going into next year?

Yes. So DOGE was an unequivocal positive for us. And I imagine if I had been here a year ago in this seat that I would be receiving quite a bit of skepticism on that point. But what it did is focus the government on efficiency, particularly when it comes to their technology spend working directly with the vendors as opposed to the intermediaries and really beating the drum beat of automation. Automate or die. That's the [ world around ] easy when it comes to software these days.

And the reason why -- and obviously, that plays to our core strength, the efficiency to streamlining, to eliminate manual processes, consolidating legacy platforms, legacy solutions into a single modern platform and we've seen that demonstrate itself. There was a little bit of disruption in the first quarter where we weren't sure like who's who. But since then, we've just executed really well. And I will also point out in the fourth quarter, much of our revenue was driven by the federal space where we exceeded our expectations despite the fact the government was closed for half the quarter.

And so as we think about it going forward, I should say one more thing, a particular achievement from our perspective, which didn't help the numbers in the fourth quarter, but it's an indicator of the journey that we've made in the government, but arguably more broadly was the framework agreement with the Army. So we issued a press release that we have a framework agreement of up to $500 million in spending with the Army over the next 10 years. And that's really a hunting license to go and find new use cases and more quickly pursue ability to get more demand onto our platform. So to me, that's an indication of, a, our success with one of our best customers, meaning the Army specifically, b, some of the changing sort of tailwinds, which I think are structural. And we think all of those are positioning us well for growth next year and in the future in the federal space and the pipeline is looking very strong into next year.

Which kind of goes to like when you think about the overall growth rate of the company, what potentially hopefully gets better is like the commercial business. And you meant -- I think in Q4, it was one of your best commercial bookings quarter. So through the lens of like what you're seeing from the sales productivity side, is there a potential for the commercial business to start to get on a similar growth path as the federal business?

Yes. So we've had better performance in federal over the last few years compared to the commercial, which isn't a function of the end market, it's a function of our execution. And as we think about all the changes in the go-to-market that we've done, that's where more of the changes have been focused on the commercial. So when we call that commercial North America, the reason for that was because that's the first commercial theater where we made meaningful changes in terms of leadership in terms of process, that was done at the beginning of 2025. And Q4 is a quarter but it's the largest quarter, and the performance was significant. We said best growth in commercial North America in more than 3 years. And so that's an indication of when you sharpen your execution when you focus on selling by value, what's possible.

So as we look at it going forward, whether it's federal, whether it's North America, whether it's EMEA, whether it's APAC, we think we have the ingredients in place, product, which has always been there and then improve go-to-market execution, which is going to kind of carry the growth going forward.

And so said another way, that commercial momentum that you saw in Q4 wasn't because of some product release. It's basically multi-quarter effort around go-to-market focus and sales productivity.

Great.

As I mentioned before, about 80% of subscription even comes from government, financial services, insurance and life sciences, what's the runway in these 4 industries? I get a lot of questions like, can Appian kind of be the pseudo vertical company and in terms of meeting the growth and profit expectations that you guys hope to deliver can we just focus on these 4 opportunities? What's your sort of perspective on that?

I say we have multiple levels of growth. First, I would say is plenty of room for penetration inside of the existing customers and inside of the existing vertical. And that's in the context of the amount of processes that still need to be automated, new or legacy that we can go after and that we are very well positioned to go after, point number one.

Point number 2 is there's plenty of new logos in that space as well. So we sell to some of the largest players in that space, but there's plenty of white space, if you will, in terms of ability to acquire new logos.

And then the other thing that I would say is, as you think about AI as a note in the process, it sort of increases the TAM of automation. And as we get past this early adoption stage where people are still concerned about actually getting value, we think that it will turn from fear to greed in terms of everything that could be done, and we're very well positioned about that.

And then the final thing I would say is there's nothing magical about these 4 verticals and if you fully expand the period of time. We have success in manufacturing, we have success in retail. We focused our go-to-market investments where we are seeing the best productivity over time. But as we build our execution muscle, that aperture will also expand that will further be additive to growth over time.

Yes. I think you mentioned when we're doing -- when we're having the AI discussion like things like DocCenter feels like a horizontal.

Yes. Very horizontal.

Horizontal play that can drive penetration outside of just those core verticals. Let's move the conversation to profitability and capital allocation. 2025 was a pretty big year for margin expansion, you guys have been very clear that you guys want to get to a moderate pace of investment in sales count and engineering capacity. We think over a multiyear time frame, how should investors think about the operating margin trajectory? And how should investors think about the pace of margin expansion beyond 2026?

Yes. So let me talk about history first and then maybe a little bit about the present. So Appian, and this predates me so I don't get to take much credit for this. It did a tremendous turnaround when it comes to this focus on profit ability. So right around the time when we decided to focus upmarket, we generally decided to improve our focus and efficiency across the company. And one thing about Appian is that when we choose to move, we move rapidly and you've seen this. We've gone from negative 8% EBITDA margin to positive 11%. And even in my time there, I think my first guidance was for 7% at the midpoint, and we ended up closing the year at 11%. And we basically kept OpEx flat.

However, what's also happened under the surface is that our productivity, particularly in our go-to-market work has improved to the point where I think our sales and marketing -- payback on our sales and marketing dollars has become acceptable. Now our LTV to CAC has always been strong, but our sales and marketing payback wasn't great, which was always an impediment to growth. It's improved so sufficiently that we've earned the right to grow. That was my sort of internal drumbeat when I showed up and I saw the numbers, I said, "If we can hit these numbers, then we've earned the right to grow moderately", which is what we're doing. We're investing in go to market. We're investing in overseas R&D. We're still expanding margins after two years of dramatic expanding margins.

But again, because of the movement in time that we find ourselves in this moderate pace of investments while still expanding margins is very, very important. We obviously just guided for 2026, so we're not going to expand beyond that. What I will say is that we see the opportunity to do both. We see the opportunity to drive healthy revenue growth while continuously driving margin expansion is really the combination of two that we think is very important.

Awesome. We have a lot of questions on -- the topic of capital allocation and particularly a couple of different topics. And it's kind of across software. So I wanted to get the Appian perspective. One, the importance of share buybacks as share prices and software, including with Appian have come down, what level of share dilution should investors expect going forward? And how important is it for the company to get to GAAP profitability?

Fun fact, we were GAAP profitable last year. It's $1.2 million, but hey, it's in the green, let's start there. This is exceptionally important to us. And we've always been very cognizant and careful about dilution. Our stock-based compensation as a percent of revenue is less than half of the average company our size. And that actually matters as you think about sort of compounding growth over time. So I know some people try to think about it as like free cash flow minus [ SPC ]. So you can use that framework, it's the same answer. I'm more comfortable with just thinking about it on a non-GAAP basis. So we've just issued a $50 million buyback. That buyback is not a reaction to our stock price being where it is. It's a reaction to where we've come as a company. It was of improvement in profitability last year was the really -- the first year in which we generated meaningful cash flow. And what we said is like, yes, it's a $50 million buyback, that's important, but think of it as the beginning of consistent capital return policy, which we're now in a position to communicate to our investor.

The other interesting thing because we dilute, it's still relatively little, that $50 million buyback essentially off that dilution for us. So if you then take a step back and think on a multiyear time horizon, which is how we're running the business, we think we have like 4 ways to deliver value. One is continued revenue growth, and I'll just use numbers from this year as an example, 11% at the midpoint, 16% cloud, 11% at the midpoint for the total.

EBITDA is going to grow faster than revenue. So that 100 basis points of margin expansion means low 20s growth in EBITDA. And you have net income that's going to grow faster than EBITDA, pro forma net income because we don't need capital to grow so we will delever in absolute and relative terms, and that will mean that net income grows faster.

And then finally, as we buy back shares this year, we're roughly offsetting dilution. But over time, free cash flow is going to grow more than dilution. So we're going to start shrinking the share count, which is hard for most software companies to do. So as a result, pro forma EPS is going to grow faster than any income. And this year, we're guiding to at the midpoint of the range -- sorry, pro forma EPS growing 46%. So as long as we can deliver on all 4 of those metrics over a period of time, we think we can really compound value and deliver an interesting return to -- obviously, to our customers through our innovations, to our employees as well as to our shareholders.

Awesome. So there's a last question for you. It goes back to the guide. I think we exited Q4 at about 16% constant currency cloud growth. I think the guidance assumes a similar level of growth. What gives you the confidence that cloud growth sustains throughout 2026?

Yes, I'd say a few things. Number one is we actually had a very good Q4 in terms of new business, but it was somewhere back end loaded. So you'll see more of that hit us in 2026 than it helped us in 2024. So that 16% is a little misleading that way.

Second of all, there's a little bit of benefit of currency in Q1. So as you think about -- the [ two 16s ] are not comparable. So like one is constant currency, the other one is total. But then fundamentally, it's about execution and our confidence to go and do it out there in the market. We have a pipeline. We have a sales org which has done a remarkable job of delivering a good year while rebuilding. And so now the goal is to grow and continue improving productivity, and we think we can do it.

Awesome. Thank you so much for giving us the update on the Appian story.

Excellent. Awesome.

Good day, and thank you for standing by. Welcome to the Appian Fourth Quarter 2025 Earnings Conference Call. [Operator Instructions] Please be advised that today's conference is being recorded.

I would now like to hand the conference over to your first speaker today, Brian Denyeau from ICR. Please go ahead.

Good morning, and thank you for joining us. Today, we'll review Appian's Fourth Quarter 2025 Financial Results.

With me are Matt Calkins, Chairman and Chief Executive Officer; and Serge Tanjga, Chief Financial Officer. After prepared remarks, we'll open the call for questions.

During this call, we may make statements related to our business that are considered forward-looking. These include comments related to our financial results, trends and guidance for the first quarter and full year 2026, the benefits of our platform, industry and market trends, our go-to-market and growth strategy, our market opportunity and ability to expand our leadership position, our ability to maintain and upsell existing customers and our ability to acquire new customers. These statements reflect our views only as of today and don't represent our views as of any subsequent date. We won't update these statements as a result of new information unless required by law. Actual results may differ materially from expectations due to the risks and uncertainties described in our SEC filings.

Additionally, non-GAAP financial measures will be discussed on this conference call. Reconciliations of GAAP to non-GAAP financial measures are provided in our earnings release.

With that, I'd like to turn the call over to our CEO, Matt Calkins. Matt?

Thanks, Brian. Thanks, everyone, for joining us today.

In the fourth quarter of 2025, Appian's cloud subscription revenue grew 18% to $117.0 million Subscriptions revenue grew 19% to $162.3 million. Total revenue grew 22% to $202.9 million. Adjusted EBITDA was $19.7 million. For the full year, Appian's cloud subscription revenue grew 19% to $437.4 million. Subscriptions revenue grew 18% to $576.5 million. Total revenue grew 18% to $726.9 million, adjusted EBITDA was $76.8 million.

2025 was a successful year for Appian for several reasons. First, we executed our strategy to sell big deals to leading organizations. The number of customers that purchased over $1 million of software this year grew 50%, nearly doubling the value of our 7-figure transactions.

I'll share 2 quick examples. A European pharmaceutical research organization purchased a 7-figure software deal to digitize its clinical trial site selection. Appian will accelerate its selection process with AI, improving patient selection efficiency and reducing trial costs. Separately, a North American aerospace manufacturer purchased a 7-figure software deal to automate a core manufacturing system and save the company nearly $60 million over the next 3 years.

The second reason Appian had a successful 2025 is that our position within the U.S. public sector strengthened partly due to structural changes. We closed big deals as the new administration have emphasized efficiency and changed how it purchases and implements technology. For example, a U.S. military branch named Appian its cornerstone platform to modernize operations and increase efficiency. In Q4, it signed a 7-figure software deal to unify systems and deploy them to over 100,000 users.

The federal government has shifted to partner more directly with software vendors and reduce its reliance on intermediaries. Appian stands to benefit as indicated by the enterprise agreement that the U.S. Army awarded us this quarter. The Army is already an 8-figure ARR customer. This new framework allows it to purchase $500 million in Appian software and services over the next 10 years. This agreement shows the Army's ambition and commitment to use Appian to modernize systems and transform operations with process and AI.

The third reason Appian had a successful year is that we continue to increase our operational efficiency. We've now increased our go-to-market efficiency in 10 sequential quarters. You know this metric means a lot to me. I always mention it. Appian generated 11% adjusted EBITDA margin for the full year 2025. And compared to just to negative 8% just 2 years before. We created $63 million in operating cash flow compared to a loss of $110 million 2 years ago.

Credit these efficiency improvements to tighter resource allocation and sales, global diversification and back-office AI enhancements. We're creating an operating model that's built to drive further margin expansion going forward. Appian's strong financial performance puts us in a position to start consistently returning capital to shareholders. Today, we're announcing a $50 million stock buyback.

Finally, I'll tell you the best thing about 2025. The best thing is that it's become common knowledge over the past 6 months that AI needs process, also known as workflow. Without a process framework, AI cannot add value to complex work streams or collaborations. Market analysts and researchers like Gartner and MIT published papers on the topic. Customers, prospects and partners have all confirmed the trend. Our competitors shifted their messaging, began talking about workflow and added rudimentary process technology. This trend validates Appian's long-standing position on the issue and recognizes the synergy between AI and process that we built our strategy around.

I'll take a moment to explain why AI needs process. AI is probabilistic, which is to say it's slightly unpredictable. Most important work at the large organizations Appian targets requires total reliability. So AI needs a deterministic framework like our process layer. That deterministic layer provides direction and guardrails and certain functionality you wouldn't ask AI to write on its own. Code is becoming cheap but mistakes aren't. So the more important to the work, the more essential is the deterministic layer.

In the coming years, AI will do a lot of work and write a lot of software, but it's not going to do it alone. Where AI goes process must also go. Our technology is an essential enabler of AI.

Appian has been a process leader for more than 20 years. We were a pioneer in this market back when they called it business process management. We led providing BPM in the cloud. We led in building a process-centric suite, our unitary platform provides workflows, data fabric, process mining and built-in security. We led again embedding AI in our processes. We've earned trust at the largest firms and are executing mission-critical processes to the highest standards. 2/3 of the world's top 10 life science firms, asset managers and non-Chinese banks are Appian customers as well as all 15 cabinet-level agencies and military branches in the U.S. government. These groups use our platform for complex mission-critical processes like customer onboarding, claims management, patient intake, regulatory compliance and procurement.

Exponential growth in our AI traffic shows that our platform is becoming an AI vehicle for large organizations. AI use on our platform grew 14x year-over-year. AI use on our platform grew 14x year-over-year, and we are monetizing that growth. Customers must upgrade to Appian's AI license tier, which comes with an average price increase of 25%. A meaningful number of customers make this upgrade every quarter, including this quarter. Much of our revenue profit and pipeline growth in 2025 is a result of our synergy with AI.

Most of the 7-figure software deals we booked this year were driven by a desire to access our advanced features like AI. Here are 3 examples. First, a leading pharmaceutical company deployed Appian AI into an existing application this quarter. The application tracks interactions between the company's sales team and health care practitioners to ensure compliance with international regulations. We're deploying an Appian product called Doc Center that uses AI to parse incoming e-mails, documents and other communications. Doc Center uploads data, prepopulated forms, triggers workflows and accelerates response times in this case, by 88%.

Next, a top advocacy organization representing over 100 million Americans and 7-figure ARR customer, named Appian an enterprise standard this year. It recognized the importance of deploying AI within an Appian process after evaluating various AI vendors. In Q4, it purchased a large upgrade to access our latest AI features. The group will deploy Appian AI agents to reconcile tens of thousands of invoice payments annually reconciliations used to take over an hour per invoice, now the organization expects to complete tie outs in just minutes.

Finally, a network of European banks signed a 7-figure software deal this quarter to access our latest AI features. The group already runs know your customer and loan overdraft processes on Appian. In Q4, they named our platform as an enterprise standard for modernizing core processes. The conglomerate will use Appian Doc Center to classify and extract data from dozens of documents to open cases for processing. The banks expect to save more than EUR 20 million over 3 years as they scale operations.

Recent market moves show investors are concerned that AI poses an existential threat to software firms, including Appian. There are 2 main worries. First, the AI will do all the work that software used to do; and second, that AI will write all the applications. I'll address each point. First, Appian leads in the technology that AI cannot thrive without. It's becoming understood now just how much AI needs process. AI is probabilistic technology, not reliable enough for the highest value use cases. Unpredictability is an indelible part of AI's identity. Years of improvement will not make it otherwise, nor will enterprises ever decide to accept AI-level unreliability.

A deterministic layer is essential. Something to direct the work, something to detect and remediate the errors, something that can produce perfect outputs from imperfect efforts. Process and workflow is that technology. Long before AI, process orchestration was developed to best utilize that other unpredictable worker, the human being.

As repeated studies attest, AI is not yet transformative in the enterprise. In PwC Research last month, most CEOs report AI having no impact on revenue or cost. But the impact is coming when AI is connected to valuable work streams, and Appian is leading the way. With the help of a process layer, AI will be a very productive worker indeed and very widely deployed, providing a framework so that AI can address the world's most important work. It's like selling pickaxes in a gold rush.

Second, about AI writing code. We sell to our customers value and safety, not code. Approximately 80% of our revenue comes from highly-regulated industries and the government sector. Customers buy Appian for performance, precision and peace of mind. We sell compliance to regulations, reliable customer service and accurate decisions. We sell the reassurance of a community of practitioners and 24-hour expert support. AI-generated code cannot provide these things.

Only with Appian's deterministic framework, can AI create applications and perform work to meet the most exacting requirements. This is the reason why no Appian buyer has ever suggested to me that they would vibe code a critical system. They know better.

This current concern about AI-generated code reminds me of the open source scare years ago. Open source seemed to threaten the pricing power of the entire sector. But in the end, it proved that code isn't the center of value in enterprise software. The value comes from the community and the support and the corporate commitment to reliability. Since open source became a popular term in the late '90s, the global software industry has grown by a factor of over 5x. Appian has faced open source competitors in our market. they appealed best to low-end buyers and had no impact on our growth. I expect AI-generated code to be adopted in mistake tolerant and low-value use cases. To write enterprise code, AI needs a platform like ours that facilitates careful specification, developer collaboration, revision and the strategic reuse of preexisting assets.

In conclusion, the more organizations use AI, the more they need process orchestration. Process mitigates AI's shortcomings. Together, AI and process can address the world's most critical jobs, but AI cannot do it alone.

Before I end my segment, I'd like to welcome Dave Link to Appian's Board of Directors. Dave is an expert in scaling enterprise software companies and applying AI to complex globally distributed systems. He is the CEO of ScienceLogic, an AI-driven observability and IT operations platform. I'm excited to welcome him to our team. I also want to thank Jack Biddle for his exceptional contributions over the course of many years on the Appian Board.

And with that, I'll hand the call to Serge.

Thanks, Matt. Before turning to our fourth quarter results, I want to cover some changes that we are making in our reporting in order to give investors better insights into our financial performance.

First, we have reclassified certain IT, cybersecurity and facility expenses from our G&A expense line item into other line items in our P&L. There is no change to our total expenses, just which line item they are shown in. We believe this new presentation of our financial is more comparable to those of other software companies. Second, we are introducing a new metric, cloud net ARR expansion. This metric is calculated by taking the ARR of our cloud customers at the end of the prior year period and measures the ARR of those same customers at the end of the current quarter. We report cloud net ARR expansion in constant currency. We believe this metric gives investors a more timely insight into our business and is more comparable to how other software companies report expansion from existing customers. Going forward, we will no longer report cloud gross renewal rate and net revenue retention.

Finally, we refine our definition of a customer. We now aggregate entities based on their ultimate parent company or an equivalent government entity, whereas previously we counted at a more granular level. As with our other changes, we believe this new methodology is more common practice. Please refer to the earnings call supplemental deck for further information on these changes.

Now let me turn to our Q4 results. We had a strong quarter of new business driven by continued AI traction and ongoing momentum in our focus on the high end of the market. The standout performer was our commercial North America theater with the fastest new business growth in over 3 years. Cloud net new ACV bookings were approximately 76% of total net new software bookings in Q4 compared to 65% in the prior year.

Q4 cloud net new ACV growth was the strongest we've seen in almost 3 years. Appian met or exceeded the guidance ranges we provided on our key metrics of cloud revenue, total revenue and adjusted EBITDA. Cloud subscription revenue was $117 million, an increase of 18% year-over-year. We achieved the high end of our guidance even as FX contributed approximately $1 million less than what was assumed in our guidance. On a constant currency basis, cloud subscription revenue increased 16% year-over-year.

This quarter was more back-end loaded than normal in terms of new business, resulting in relatively little revenue contribution from new business in the quarter. Our constant currency cloud ARR growth, which represents the exit run rate was stable versus Q3. Total subscription revenue was $162.3 million, an increase of 19% year-over-year. On a constant currency basis, total subscription revenue grew 16% year-over-year. Professional services revenue was $40.6 million, up 36% compared to the fourth quarter of 2024.

Total revenue was $202.9 million, an increase of 22% year-over-year. On a constant currency basis, total revenue grew 19% year-over-year.

Our cloud net ARR expansion was 114% in Q4 compared to 113% a year ago and 112% in the prior quarter. The uptick was driven by a particularly strong quarter of upsells to existing customers in Q4. We ended the year with 140 customers with $1 million plus of ARR compared to 115 a year ago.

Now let's turn to profitability. Non-GAAP gross margin was 73% compared to 77% from the year-ago period and 74% in the prior quarter. Our subscription non-GAAP gross profit margin was 86% compared to 88% in the year ago period and 86% in the prior quarter. Professional services non-GAAP gross margin was 23% compared to 27% in the year ago period and 31% in the prior quarter. Total non-GAAP operating expenses were $131.5 million, up from $109.8 million in the year ago period.

Adjusted EBITDA was $19.7 million, ahead of our guidance of $10 million to $13 million and compared to adjusted EBITDA of $21.2 million in the year ago period. This outperformance relative to our guide was largely driven by greater-than-expected revenue. Non-GAAP net income was $11.1 million or $0.15 per diluted share compared to a non-GAAP net income of $13.2 million or $0.18 per diluted share for the fourth quarter of 2024. This is based on 74.9 million diluted shares outstanding for the fourth quarter of 2025 and 74.6 million diluted shares outstanding for the fourth quarter of 2024.

Turning to our balance sheet. As of December 31, 2025, cash and cash equivalents and investments were $187.2 million compared to $159.9 million at the end of last year. For the fourth quarter, cash provided by operations was $1.1 million compared to $13.9 million for the same period last year. For the full year 2025, cash provided by operations was $62.9 million compared to $6.9 million in 2024.

Turning to guidance. We are expecting to deliver another year of solid cloud subscription revenue growth and our third consecutive year of adjusted EBITDA margin expansion. Our focus is on consistent execution and capitalizing on the opportunity in front of us.

Starting with the first quarter of 2026. Cloud subscription revenue is expected to be between $119 million and $121 million, representing year-over-year growth of 20% at the midpoint of the range. Total revenue is expected to be between $189 million and $193 million, representing year-over-year growth of 15% at the midpoint. Adjusted EBITDA for the first quarter of 2026 is expected to be between $19 million and $22 million. Non-GAAP earnings per share is expected to be between $0.16 and $0.20. This assumes 75.1 million fully diluted weighted average shares outstanding.

For the full year 2026, our cloud subscription revenue is expected to be between $502 million and $510 million, representing year-over-year growth of 16% at the midpoint of the range. Total revenue is expected to be between $801 million and $817 million, representing year-over-year growth of 11% at the midpoint. Adjusted EBITDA is expected to range between $89 million and $99 million for an approximately 12% margin at the midpoint of the range. Non-GAAP earnings per share is expected to be between $0.82 and $0.96 or approximately 46% growth at the midpoint. This assumes 74.8 million fully diluted weighted average shares outstanding.

Our guidance assumes the following. First, we anticipate our noncloud subscription revenue to be roughly flat on a year-over-year basis in Q1 and in 2026 as our customers are increasingly opting for the cloud. Second, we expect professional services to grow in the teens in Q1 and high single digits for the full year. Third, total other income and interest expense will be approximately $3 million in Q1 and $12 million for the full year 2026. Fourth, our guidance assumes FX rates as of mid-February. Please note that we expect FX benefit to our reported revenue growth rates in Q1, but we expect FX to be roughly neutral to year-over-year growth for the rest of the year as we annualize the U.S. dollar depreciation from April of last year.

Finally, as discussed previously, after 2 years of relatively flat OpEx, we are returning to a moderate pace of investment in 2026. We are investing in the growth of our sales org as well as the expansion of our engineering capacity in India. Despite these investments, we are forecasting 1 percentage point of adjusted EBITDA margin expansion in 2026.

Before wrapping, let me also touch on our share repurchase announcement. As most of you know, we are very careful about dilution as evidenced by our stock-based compensation expense as a percent of revenue, which is less than half that of other software companies our size. As Matt mentioned, thanks to significant improvement in profitability over the last 2 years and becoming a meaningful cash flow generator, we are in a position to announce a $50 million share buyback. We expect this program will essentially offset the dilution from stock grants issued this year. We see this buyback authorization at the beginning of a consistent capital return policy for our shareholders. Our intention is to scale the size of our share repurchase program in line with the growth in our cash flow in the coming years. We will look to execute on this buyback during 2026.

In closing, we are pleased with our Q4 results. in particular, our traction with AI and believe we are well positioned to deliver a successful 2026. We are excited about the opportunity ahead, and we'll continue to invest responsibly to maximize our long-term value.

Before we move to Q&A, I'd like to invite you to our Investor Day in New York on May 14. We'll be sharing updates on our product and strategy, and you'll have the opportunity to hear directly from our customers. If you'd like to attend, please reach out to [email protected].

Now I will turn the call over for questions. Operator?

[Operator Instructions] And our first question comes from the line of Sanjit Singh of Morgan Stanley.

This is Oscar Saavedra on for Sanjit. Yes. Congrats on the great quarter, guys. Nice to see the cloud net expansion uptick quarter-over-quarter.

I was thinking maybe on the guide, it looks like Q1 guide is a bit of an acceleration from Q4. I imagine part of that is that expansion ticking up. But maybe can you help us understand a bit more the visibility and the confidence that you have given that acceleration?

Yes. So we're happy with how we wrapped up 2025, and we'll set up well for 2026 as evidenced by the full year growth rate of 16% for the year. Q1, I guess, 2 things I would say about it. Number one, it will benefit from strong new business that we had in Q4, which is why on a sequential basis is a robust guide. And then the other thing that I would call out is just that it is a quarter in which we'll still benefit from a meaningful FX tailwind. And that's clearly the primary difference between the full year guide and the Q1 guide.

And our next question comes from the line of Raimo Lenschow of Barclays.

Perfect. Congrats that was a great, quick Q4. Two questions, one for Matt, one for Serge. Matt, if you think about -- I'm totally aligned with you with your vision around AI and agentic [ needing ] like control layer. Like how do you think what gives you the right to be that? Because like, obviously, a lot of other people are kind of trying to eye for that because that kind of position will be very strategic as well. So talk a little bit about what Appian brings to the table that you can go to customers and say, like, I should be that layer. And then I have one follow-up for Serge.

Yes. Well, I want to say that we've been that layer for a long time. We've been that layer before a large language models exploded onto the scene. We've been embedding AI actors, you call them agents, in our software, in our processes for about a decade doing jobs as digital workers because we've been a platform that enables and governs digital workers. So we're not a Johnny-come-lately to the idea of governing a digital worker or an AI agent. In fact, it's been our business, and we've been leading for a decade. So I think it's a natural for us to inherit this position as well.

But I also want to say that because we have because we have such a strong governance layer such a unique ability to detect and remediate errors such a monitoring layer and a self-improvement and an optimization layer, I think we're really uniquely equipped for this moment. I think there's some other vendors that went all in on agents and then realized they needed a governing layer, whereas we come into this market with a governing layer and are, therefore, really well equipped to give agents the structure they need in order to succeed.

Okay. Perfect. And then, Serge, if you think about the slight increase in OpEx, you talked about on the sales org, et cetera, how do you think about the evolution of sales capacity from here onwards? And I'm asking because it does feel like a whole new world is opening and there's quite a few players in the software space that are now thinking about like we probably should think about sales capacity increases. Is this just one-off things? Or how do you think about that evolution here?

Thanks, Raimo. So I guess I'll start with a little bit of history. We've done a great job significantly improving our sales productivity and paybacks on our sales and marketing investment, really particularly last year. And that, frankly, gives us the right to grow our sales org because we want to do it in a financially responsible way. So that's point number one.

Point number two is kind of like your point, which is the market is large and growing, and we are very underpenetrated versus the opportunity. So to us, this return to growth of the sales org is the beginning of a long-term trend. But it's important to do it consistently over time. What you don't want to do is overextend because it's a difficult operational task. What you want to do is bring in people, make sure they're successful make sure that they reach their productivity and then do it again year after year. And that's fundamentally how you kind of put yourself in a position for multiyear growth.

Our next question comes from the line of Steve Enders of Citi.

Okay. Great. I guess I just want to start on maybe the opportunity around AI. And I guess, given the purview that you have in some of these larger customers, just what have you seen so far from how their budgets or their purchase decisions are changing as they're looking to incorporate AI and I guess maybe what does that mean? Or I guess, how do you kind of view the opportunity pipeline and given what that means for '26?

Yes. AI has been an unalloyed positive for us in our relations with our customers, maybe causing some consternation in the investing market. But for -- in our sales situation, it's an entire positive. It gets us into higher-level conversations. It allows us to speak strategically to the top topic that's on executives' minds. We are more likely to win according to internal analyses when AI is a factor in the decision. So it's helped our TAM. It's helped our access. It's helped our win rate. We're benefiting in all dimensions from AI.

And Steve, if I can just add, just to give you a sense of how that's sort of playing out over time. Customers begin with proof of concepts. Then when they are ready for production use case, they need to upgrade to our advanced tier to have access to AI and production. And we talked about in the past about how the percentage of our customers that is on that tier is growing. And -- but plenty more that we can upgrade to advanced tier. Then what we're starting to see is some of those customers have already upgraded coming for the second or the third workload because they're happy with the performance of the first one. And that gives us incremental opportunities to grow revenue there. And then over time, we will have incremental tiers as more functionality comes online. So that's kind of the process of upselling AI and how it fits into a company's budget.

Yes. Let me follow on that. We've got this thesis, and you've heard it because I just talked about it, that AI belongs in a process and that within the deterministic framework of process orchestration, AI can really attach itself to valuable work and create new value. So we've detected that some of our solutions are ideal vehicles for demonstrating that thesis. And I mentioned in my prepared remarks, this solution called Doc Center, which is a pretty straightforward usage of our technology. Just ingest documents, launches workflows, uploads data, rapid turnaround, high accuracy, but it's just such a good demonstration that we are focused on driving this into dozens or scores of accounts as quickly as possible this year as we can because everybody who sees this knows our thesis is correct.

And this is what I think we need to do in the market. We need to establish that our philosophy of making value out of AI is accurate. Every organization in the world right now is wondering how they can make use of AI. We're wondering whether AI can have a value proportional to its CapEx, and we have on our hands a demonstration of how to make that value. Just embed it within a process and it goes. And the results are tremendous and it's predictable and we can install it quickly. So where we see that we've got some of these winning demonstrations that establish how you could make value with AI, we're going to put the pedal down.

Okay. That's great to hear and appreciate the context there. Maybe on the Army enterprise agreement. I appreciate the color on the 8-figure customer there. But I guess kind of where do you see that spend potentially going? Or how do you kind of view, I guess, what incremental use cases or how you just kind of view that relationship developing moving forward with the enterprise agreement?

Yes. This is a threshold for us. This is an important moment in the growth of this organization. It represents a degree of confidence that an agency has not in the past shown in us. We've done a lot of great work, and we've done some big projects and delivered some wins, but we've never had a $500 million ELA like we do now with the Army. And that speaks volumes inside the Army. It allows us to speak to any part of the organization with great credibility, but it also allows us to go to other departments in the government and say, here's the department that knows us best. There's evidence that -- of what they see in us.

It also allows us to approach our partners and say like this is the kind of -- this is what we could succeed on together, and now we want you to help us somewhere else. So this is just a wonderful badge of seriousness and we're going to wear it all around Washington. Really pleased with what that says.

As for how we got it, I'd say a lot of the conversation is around modernizing legacy applications. And this is something I've spoken about on previous earnings calls, but I didn't get into it much this time. But I do want to mention that this topic is causing a lot of excitement amongst our customers and prospects. If I mentioned or demonstrate even better the technology that we have today to convert a legacy application into a modern Appian application. It typically stops the conversation cold. No matter what it is we were talking about, if there's a customer or prospect executive in the room, they want to stop everything and talk about legacy modernization. And we had conversations on that at the Army, who obviously has a number of legacy applications of their own, and that provided a lot of the momentum behind this award.

Our next question comes from the line of Derrick Wood of TD Cowen.

Matt, I appreciate the thoughts on the landscape of AI versus software, given all the concerns out there. My question is when it comes to building software applications and processes for your customers, how are you guys using AI internally to accelerate that value delivery. And then when it comes to the LLM vendors, like what do you think the challenges they might have in trying to build their own software orchestration and governance layer up the stack?

Yes. Okay. So they are going to have some challenges, and it makes a world of sense for them to partner with us in order to complement the power of their model. Look, the whole industry is under pressure right now. In 2026, it's a time of testing, where AI has to demonstrate that it can create commensurate value to justify the CapEx. And I think it's the question on everybody's lips and every organization is wondering about it and the 56% of CEOs who reported no value in the PwC survey last month are wondering about it.

Everyone is wondering where we will find the value. And of course, the answer is actually very simple. You just have to connect AI to the processes where the greatest value takes place, and that's a slightly complicated connection in order to pull off because AI needs role and responsibility and a constrained aperture functioning and checkups and revisions and learning and so on. It's just -- it needs what a process layer could have given it.

And so I feel like this top question that looms over the economy in 2026 is a question that I won't say we have the answer to it, but I say we have a lot to say. We have a lot to say about this question. I'm excited about the ability to prove that answer in concert with the large language models. Of course, we're agnostic. We work with all and many of them. And for that matter, with the clients who are all desperate to find an answer to this question, they're all eager for value, but not wanting to take a risk and to move first and to run afoul of the many pitfalls in AI, the unreliability and the dangers that come with that.

What was the first part of your question again?

Just how you're using AI internally to maybe help accelerate the value you deliver to customers?

Yes, that's right. Well, we're using it thoroughly, right? We're expecting major increases in all of our development capabilities this year because we're making a prolific use of AI. So I expect that to be excellent for our productivity and engineering. Also, we're using it in every deployment. So when our services teams are on site, creating new applications for our customers. They are invariably using AI which is terrific for acceleration for optimization, for recommendations on improvements, just a marvelous way to get to the endpoint.

And I want to clarify here that the endpoint is not a stack of AI written code. The endpoint is an Appian application, which provides the structure, the guardrails, the safety, the monitoring that AI alone wouldn't have provided. And also that Appian application, once you've used AI as the bridge to an Appian application, that application now has the flexibility, ability to evolve over time to match new strategic needs or to cultivate greater efficiency or to leverage new technologies. It is a living vehicle instead of what you could call new legacy, right? You don't want to go from old legacy to new legacy. You want to move to a living vehicle that can adapt as your business evolves.

Great. And then for Serge, I mean, you guys had 36% growth in professional services, I think that was the highest in 8 years. Your on-prem business was quite strong as well. It doesn't sound like you expect that to continue in the upcoming year. Could you just give a little more color on what drove that outsized strength that seems to be a little more onetime?

Yes. Let me take them in order because they're a different answer. So we've been very pleased with the demand we're seeing in our professional services business for -- especially in the back half of 2025. And it comes down to a couple of things. One is the world of AI because as Matt was just talking about, customers want to get the value but they're sensitive to get it at the levels of accuracy and performance that they are accustomed to. So when they choose our software, they usually also partner with us on implementation. Because we've done it before, we bring that implementation know-how, which is scarce in the market right now. And that helps us kind of sell both software and services when it comes to AI.

And then the second piece is federal. Our success there and the change in how the government likes to deal with vendors has helped our professional services business on the federal side as well. And that really drove our business next year. And frankly, we're expecting it to continue driving that business next year with 9% growth rate. We did see an uplift in demand, and we're going to continue seeing growth there, but it's not going to be a step function as we have experienced here in the back half.

And then on the on-prem side, we had a very strong Q4. Frankly, that was all federal. We credit to our teams when the shutdown ended, we were ready to go and we got the deals that we were going to get, frankly, even better than we would have expected had the shutdown not been there. So that's the story of the fourth quarter.

But then as you look forward, I can tell you what we see quantitatively and qualitatively. On the quantitative side, we just see a bigger mix of cloud in the pipeline than has been the case historically. And then secondly, when we talk to our customers, even our on-prem customers, they are looking for incremental deployments in the cloud. So for example, one of the largest deals that we have in the pipeline in Q1, we'll see if we get it or not, is for a customer who did one of our largest on-prem deals last year. And that's not them moving their Appian workloads from on-prem to the cloud. That's incremental deployment in line with their own IT strategy and moving to the cloud, which is why the description or the forecast for the on-prem business is what it is.

Great. Congrats.

Our next question comes from the line of Devin Au of KeyBanc Capital Markets.

All right. First one I have, maybe for Serge. Could you maybe speak to the framework of kind of the '26 revenue guidance, I believe last year, given some leaders of transition, some of the certainty around [ pub stack ] and changes around go-to-market. There could be some more conservatism being embedded in the initial guidance '25. Are you applying kind of similar framework here in '26? Or can you just speak to that a little bit more?

Yes. So how we forecast the business hasn't really changed internally, and there's no incremental conservatism or caution. From a macro environment, I think I can speak for the company even though I wasn't here a year ago. It feels a lot less uncertain than it did a year ago back when [ those ] were starting, and there was a lot of macro sort of headwinds or potential headwinds related to the international relations. So from that perspective, we feel like we have perhaps a better handle on the world out there than we did a year ago.

The thing that I would say specifically, though, is I divide the guide into cloud and the rest of the business. As you can see, the cloud, it's just ratable, it's frankly a little bit easier to forecast, which is why the range there is narrower for the full year as a percent of total business. whereas then we have a broader range as a percent of the business for the rest of it just because as you've even seen last year, both for different reasons, both on-prem and professional services can be lumpier. And that's why the range on the full year guidance as wide as it is.

Got it. I appreciate the context there. And then just a quick follow-up on the strength that we're seeing from [ pub stack ]. You continue to see momentum there, which is encouraging and it seems like Appian is really well positioned there. As you guys kind of return to sales capacity growth, could you just speak to like how are you thinking about the deployment of resources towards that vertical specifically and kind of how you guys are going to sustain and amplify the success there?

Yes. We are growing our capacity in the federal vertical, but we're growing it in other verticals as well. At the end of the day, I will just reiterate what I said the size of our distribution is a limiting factor versus the size of the opportunity. And we don't want to try to address that in a big bang because then you run the risk of deteriorating execution. So we're going to hurry up slowly, and we're going to build sales capacity year in and year out. But certainly, that's the case in the federal space as well.

Congrats on the strong results.

And our next question comes from the line of Lucky Schreiner of D.A. Davidson.

Great. I'll echo my congrats as well. I have a follow-up question on the guidance. Coming off a strong quarter, the cloud growth guide, there's a lot of deceleration baked into that throughout the year. So just I'm wondering, is that all FX related? The pipeline sounds strong. So is there maybe conservatism around deal timing or renting a sales capacity? Just curious what's driving your outlook on specifically the cloud growth guide.

Yes. So cloud growth is 20% at the midpoint for Q1 and 16% for the year. And the majority of that really isn't anything about the underlying constant currency business. It's really about the fact that we still get one more FX bump in Q1 before it normalizes.

Thank you. I'm showing no further questions at this time. Thank you for your participation in today's conference. This does conclude the program. You may now disconnect.

Okay. Perfect. Thanks for coming to our next session. I'm really happy to have Serge here from Appian. I need to remember that. Maybe let's start actually kind of with that kind of topic a little bit. So Serge, when you got to Appian kind of what was the excitement for you?

Yes. Thank you. First of all, thank you for having me. Thank you all for being here. When I got the call about the Appian CFO job, a few things were appealing. Number 1 was just even if you glance at the financial, it looks like a good business, right, high retention rates. If you read high gross margins, if you read a little bit about or talk to a few customers, you see that they're very satisfied and want to do more with us.

So that was step #1. And then step #2 was as I got to understand a little bit of the AI story, I thought that Appian had a very compelling value proposition in AI. And I've been -- honestly ever since AI became a part of our vernacular, and we'll learn names like ChatGPT and Claude and so forth. It seems to me like it's going to take a while for enterprises to really adopt it and there's going to be a number of hurdles as it does with every new technology, particularly this one because it comes with some risks that we haven't seen before. Like AI is a nondeterministic. AI can produce outcomes that are bad. And so it wasn't a surprise to me that the initial hype was going to take some amount of time to transfer into actual enterprise adoption. And where Appian comes in is when it comes to AI exactly at that moment of enterprise adoption in a way that works for enterprises.

And what I mean by that is we insert Appian into processes, new or existing -- sorry, we insert AI in the processes new and existing, and we arm with data across the enterprise, which are the 2 things that AI needs to be successful. AI needs guardrails and security and auditability in order to perform its task the way that the enterprise wanted to with the level of accuracy that actually an enterprise mission-critical application requires. And also, obviously, to become better over time. We need access to all the data, which we do through our data fabric offering. So to me, Appian and AI just makes a little sense. And then the more I've talked to the customers, the more I felt, and that's actually true. As I got to know the company better, the third element of excitement came in, which is that we've done a lot of changes on the go-to-market side to increase our focus at the top end and really focus on high-value use cases, 6, 7, hopefully, over time, 8 figures that we will align with the C-suite where we align with priorities because we are the high-quality product in the market.

We're the best-in-class product. And that transition, I would say, began roughly 18 months ago has accelerated last summer -- sorry, I guess, began 2 years ago at this point, accelerated in the summer of 2024. And as I was meeting our sales team and seeing how they're doing things differently and frankly, some of the things they're going to implement, it seems like there's a great opportunity to improve productivity and then hopefully, over time, we'll also grow the sales. So at a very simple level, best-in-class product, happy customers, strong AI value proposition and improved sort of sales and marketing effectiveness. It seems like an interesting proposition for us. Since I joined the company, I would say that all of those elements I feel the same or better about. And the thing that it has also just been lovely to see is just how good the culture is of the company and how focused we all are across all the departments in terms of getting better or getting more efficient, frankly, using AI internally, eating our own cooking. So it's a fun place to be about.

And then the follow-on question I had, like you were obviously worked at kind of fast-growing kind of slightly scaled up slightly larger organization. Like if you walk in there now, what was kind of -- what can you bring to the table? What was the team expecting from you in a way to?

Yes. So I think that there's 2 primary things. One is I've seen kind of roughly basically exactly this journey back in my days in MongoDB and what I've -- on some level, a lot of what I found at Appian today, reminds me of what I found in MongoDB 6 or 7 years ago. But I mean by that, it's a company that's kind of not start up, but not quite enterprise-grade. And so there's a lot of opportunity for us to scale our processes to automate internal work, in fact, using Appian frequently as our own solution and building cross-functional efforts such that we become faster at executing as we grow as opposed to slow down, which is what happens to a lot of companies. And I was fortunate enough to sort of see that process at Mongo and be a part of it. So I feel like I bring a decent amount of relevant experience that part of the story.

And then the second one is capital discipline, which, first of all, I got a credit Matt and the team kind of right about that time that they decided to focus up market, they also decided that being unprofitable is no longer okay. And you've seen the results we went from negative 12% EBITDA margin in 2023 to our latest guidance is roughly 10% for this year. So that largely predates me. I take a credit, but not the entire 2,000 basis points. But that capital discipline going forward when you return to growth, it looks a little bit differently because now we're going to return to moderate growth when it comes to investments. And then you need to be very explicit about the outcomes that you're trying to drive the ROI, stay making sure everybody is accountable being willing to pull the investment when it doesn't work. And that's also another cycle that kind of lived for a while. So I believe I can help on that side as well.

Yes. Okay. And then you mentioned go-to-market already, like I think because Appian was a little bit under the radar over the last 2, 3 years. A lot of people from -- when I talk to investors missed that a little bit. So can you speak a little bit like what Matt did a while ago and it looks like we are coming out the other end, so like it should be exciting from here.

Yes, fingers crossed. So Appian was spreading itself too thin, sort of across the application landscape. And I would say at the low end, a very simple use cases that are barely any automation on top of what already exists into place, all the way to exceptionally difficult, highly mission-critical. One of my favorite stories is we have our top customers come to the headquarters every once in a while and then all of us exact if we're there, we meet them. So I met with a part of the -- it's a governmental institution, but one not based in D.C. And they send like 20 people to the Appian headquarters. And they're implementing this application and with great support and partnership with us. And the way they describe it is like, if this doesn't work, U.S. financial system is in jeopardy.

It's important.

So my point is that that's on the other side. So if you spread yourself across that entire spectrum, you kind of aren't clear with yourself or with your customer where you're best at. And we're clearly best at the high end when it's complex, with scalability, high-quality performance and significant value can be generated versus some of the stuff that is at the lower end where there's also more competition and where we don't mind our win rates, but those never really turn into 7 and hopefully, over time, 8-figure deals. So really, the focus was -- let's go over the data and our product quality tells us we should grow. And then frankly, it was 2 pieces. One piece was we reduced our sales org in the summer of 2024. And for those of you who are students of basic math, you know that if you eliminate the least productive part of your sales or like the rest is by definition more productive, but without really doing it. You're saving money, and that's important.

What we've done really since then is started instituting leadership processes and discipline around how we approach our larger customers that has led to further improvement in sales productivity, and that to me is the hard part. That to me is the thing that's exciting, and again, it's been happening this year and better latter half of last year. So that's the momentum that we now need to sustain going forward while at the same time, expanding the org..

Yes. And then so there's the stuff that you can do internally, but then obviously, you have externally like what's going on in the world in terms of like things -- can you kind of speak towards kind of macro and don't go federal yet because that's the next question. But like what are you seeing like at the moment in a normal world? And then like a follow-up.

Yes. So I frequently see dichotomy between the world of Wall Street and the world of corporate -- and either way, good or bad, right? And so what's happened throughout this year was a tremendous amount of headline volatility, tremendous amount of market volatility, we have tariffs. We don't have tariffs. This piece agreement, this is not -- and on and on and on that obviously is very -- as a person who reads newspapers can be very unsettling at times. And obviously, we've seen significant generation in the market this year as well. However, if you look at our business, we have not seen that maybe yet, but we certainly haven't seen it yet translate into hesitance of customers to engage, hesitance of customers to pursue their IT and enterprise objectives. We haven't seen any changes in the deal cycles or win rates or really anything around that.

We talked a little bit about AI. There's an element. There's some combination of excitement, frustration and a little bit of concern that all surround AI, but the conversations are good. We've had conversations with almost all of our customers around like our AI offering and sort of presenting at a high level. We told you that roughly 25% of them are paying us for AI already. So that's supposed to show you that like at least so far, my macro crystal ball is nonexistent -- it will -- it hasn't impacted how the business works.

And then the -- now the federal part. So there was a -- you guys have probably from -- just from a history as well, like more exposure to federal than others. That was incredibly volatile this year with DOGE, the shutdown, et cetera. Like how is it playing out for you? And there's a negative aspect, but it's also there should be a positive one because if you want to get more productive as a government, more efficient, software and Appian should be in theory, kind of you're right in the middle of that.

So I'll divide that into kind of near-term blocking and tackling, which has been fun ride and then what that means in the long-term or what we firmly believe it does. So when the new administration came and DOGE rolled into town, things were disruptive in Q1. And people didn't know they have jobs, people didn't know who reports to who approves what. And despite that, we actually have a very, very good first quarter in federal. By second quarter and in third quarter, it felt more like business is stable. We had always fears. I can add -- this is true no matter where your customer is, but enterprise software, you're always concerned that like do you know who the last signer is. And when you get to the last signer and you find out that's not the last signer, then you realize the deal is not going to close. But frequently, you don't find that out until that very end. So we're always worried about like, okay, well, this all looks good, the demand is good. The conversations are healthy, but then somebody going to pull the rug underneath us. And they did not happen.

We had a good Q2, a good Q3. Overall, if you look at the entire federal fiscal year, that business grew faster than total totality of Appian, which like if I told you that, in January 20, you'd be on a way, right? So I credit that to the execution, that is our highest performing part of our sales org. And also just the relationship that we've built across the government because we are based just out of D.C. and some of our largest early successes as a company are actually there.

Then the shutdown came. Nobody did anything for 6 weeks. We talked a little bit about our guidance how we do expect that to result in some amount of disruption because people come back. And it's honestly you come back, you got to look at your inbox, you take a few days to dig yourself out of it, then it's Thanksgiving. And so now we're racing to close business in September, and we're more cautiously optimistic, but there was that piece of disruption, and we'll sort of have to see how in the end it plays out.

If you step back longer-term, at the end of the day, the focus on efficiency seems to be a secular trend, particularly when it comes to technology. And I mean that because one sort of the dust settles, it seems like it's obvious, right? Why would you have -- why wouldn't you push aggressively to automate your paper processes? Why would you work through intermediaries to actually deal with software companies?

One of the large parts of the government their CIO issued a memo that is called automate or die with like 150 applications I forgetting now the deadline, but the answer was like, either you modernize or you shut it down and go. And so that kind of -- and government at some level has greater flexibility when it sets its mind to something that an enterprise does because an enterprise can't break things, just worry about processes. I think there's more once there's a clear motion there's greater ability to run, and we expect to benefit from that going forward. So it's an exciting opportunity for us.

And then I wanted to switch gears a little bit. So you mentioned your AI story as well a little bit and how excited you are about that. Can you just frame it a little bit for us? Because here on stage, I'm talking to all of them, everyone is going to be big on AI. But like what does it actually mean? Like is it going to be -- are you going to be like building agents? Are you going to platform build agents? Are you giving data for agents? Like how do you dinner party, you were kind of describing what Appian does on AI.

At dinner parties, I just say we're an AI.

Yes, yes. Okay.

So -- but if we were to break it down, Appian automates complicated processes. That's what we do. Now think of processes as nodes where actions need to be taken and directions in which the process continues from there. And again, we do that for complicated processes frequently across enterprise frequently involving the external customer and on and on and on. And so -- and those nodes in the process, and we have plenty of lovely automation online in case you guys are curious to see, frequently, there was just a human, right? Human is a part of the process. In some situations, it's possible to replace that human or some of the other sort of tools that we've had before, like an RPA or whatever, with actually dropping access to Gen AI. So we don't build models. We allow customers to access them one way or the other. And for the purposes of performing very specific functions inside of a predesigned process. So what that means is that we arm AI with the right instructions, very clear instructions.

We are met with data that it needs to make decisions. And we give it risk parameters such that if it doesn't -- cannot make a decision that it doesn't. And then we work iteratively with the customer frequently using our own professional services org to actually get it to accuracy levels that are good because frequently, these applications start at like low accuracy levels, below 50%. Like that's not good. That's fine if I'm researching my skiing holiday in Europe because then I'm actually going to go to every hotel website, but it's not fine if you're going to release it on something that gets done thousands of times a day and actually runs your company.

And so think of it as -- and I'll give more specific about what that looks like. But think of it as AI is a node in the process used where it's advantageous to the process. For example, you don't need to ask AI to do basic math. Basic math is deterministic 1 plus 1 equals 2, the one thing AI can do. But if there's some amount of judgment and sort of the right kind of judgment, if you will, armed with the right kind of data, then AI could be very helpful and accelerate processes.

So what we've seen is success so far, the first success we've seen right now, the big one is actually document processing. And that's obvious from the perspective of people in this room, it's actually much harder to implement in the outside world because documents aren't clean PDFs built last year. Their medical records, they're 15 years old, they're sometimes handwritten. They are -- they can be in different languages, the photo copy was bad. So like to actually get a document processing system to work in AI is a nontrivial task. And they say, we're succeeding. We're having great customer references. And that's a use case is probably applicable across most of our customers. So that's going to be a big incremental push for us into next year.

The second area is our agent product called Agent Studio, which we just GA-ed in December, which again takes the idea of autonomous action by an agent. But under the sort of definition in guardrails that are kind of germane in an Appian process. But it gives the customer a way to build an agent that's specific for them given the parameters that it wants to run and then kind of help the agent get smarter over time because that's the other important thing. Like where you start is not where you're supposed to end. That's the beauty of AI that it ought to become better over time. And so that was our most successful beta in our company history. Several of our beta customers were basically trumping for us to go GA, so they themselves can go into production.

And obviously, just looking at the variety of use cases in the beta, I was impressed. I was impressed in terms of what customers are already trying to do with the platform. Now as with all things when something GA, that's really just the end of the beginning as opposed to sort of massive opportunity that we've got to go in the market, engage with the customers, build proof points. And then over time, that will sort of be the second kind of part of the story.

And then the third part of the story is modernization. We think that legacy app modernization is a market that will -- that has always existed, but it was small because it's hard to do. AI offers a tremendous amount of promise to make that process easier. And we think we're naturally suited to be one of the winners only because our destination platform is the best platform. And some of the products that we already built and our strong professional services are will make those projects go better and faster than competition.

And then from a CFO perspective, the one thing you need to, I guess, worry about more than the sales guys is like, okay, how do I monetize some of that? Like what's the pricing vehicle here, like -- are we at a stage where you can talk about that already?

Yes. So the first step that we talked about for the last year and change is you need to pay us more to have access to our AI features. So we have most of our customers on what's called the standard tier, and then we have an advanced tier. So if you want to run AI in production, you actually need to upgrade to a standard -- to the advanced tier, and that's a 35% uplift and obviously, very, very high margin. So that's the first way. So when we say customers are paying us for AI, that means that they are on the advanced tier. And the reason why that matters is because they're not paying us a little bit more. These are particularly for large customers. These are significant amounts of money that they're saying, less, I'm ready to go, and I know I will get the value. So that's exciting for us to see. Over time, there will be an element of consumption in our AI modernization. We have this concept of an AI action where when you actually ask an agent or any other sort of AI tool to perform in action for you that obviously consumes external resources that obviously also is where you can generate the most value.

So if you go above certain limits, you will then pay us incrementally for that usage as it builds. And we see that from time to time happening right now, but it's more of a -- as use cases become more robust and people get more ambitious in what they're trying to build that will be an incremental part of the growth story.

Yes. And then if you think about it, like how do you think -- is AI going to be like a growth lever that sits on top of that. And it does feel more listening to you, it kind of attracts the whole -- it brings everything up basically because it's kind of interval...

I think it's both. It's in a rolling. I think that's the right way to think about it.

Yes, yes, yes. Okay. Okay. And then the other thing I want to talk about is like now differently now that you're kind of looking at the organization, you have a cloud business, you have a self-managed business. Like historically, we -- like in my shoes, it was like, oh, got the self-managed off as quickly as possible, bringing in the cloud, and then you have more control, et cetera, and you had it with your previous company to some degree. Where are you on that? How is your thinking there for the Appian story?

So roughly 80%, 90% depending on the quarter of our new business is in the cloud. So our cloud transition on the subscription side is like it's very mature, like we're most of the way there. I, however, think that there will be an important element for self-managed, frequently on-prem deployments for a long time to come. Our self-managed business continues to grow, grow slower than the cloud, but it does continue to grow. And ultimately, you cannot tell a customer how they want to deploy. All you want to do is align to their IT strategy and make sure that they pick your technology. And that's particularly relevant for us like because our -- in addition to the government, our big verticals are financial services, insurance and life sciences, those are heavily regulated industries that are always going to be laggards going into the cloud. So we expect to -- some of the greatest wins that we've had as a company this year were actually on-prem deals.

So I think of that as definitely a part of the story and something that we're not in -- and by the way, we rarely see customers take an existing workload and move it from one to the other. That, I think, will happen in some long future, but we haven't really seen it happen very much. And so in the meantime, we're just driving adoption wherever we can. I will say one thing as the CFO, the on-prem business does give me a little bit of heartburn because 75% of it is upfront. And so it's just going to be a bit more lumpy than in the ideal world you and I would have liked. But at the end of the day, it all evens out and it's not the majority of the business, and it's a good business. So we're happy to take it.

Sorry, I'm not giving the playbook of a CFO, but is there like a multiyear, single-year aspect there?

Great question. No, is the answer. Thank God, at least we don't have that. So we actually, for our multiyear on-prem contracts have structured them with the customer such that we only recognize the first year upfront. We're not getting into the accounting minutia, like we've written the contracts such that we and our auditors are comfortable that we can just take the 1 year because then if we were taking multiyear deals of which we have many, that would create the volatility in their line to be even greater in which case, we would probably need to think about a different way to look at the business like ARR or something, but I'm happy to say we have to do that.

Yes, yes. I know a company that had that. Changing gear a little bit in the last few minutes. It's like profitability. I have to say before your time that there wasn't that much of a focus at Appian. It changed a little bit before you joined, like where are you guys on that journey?

Yes. So again, I actually think it's changed quite dramatically before I joined. It may have just become more obvious in the last 2 quarters that I've been there. But look, Appian in 2022 and 2023, continued investing aggressively when other software companies were pulling back, right? Like I forget now when the ZIRP era ended and everybody got the memo that you got to focus on profitability more than you have in the past. Appian chose to ignore that memo. And by the way, hindsight is 2020. But at the time, that's not an entirely rational decision, strong product, large market differentiation. If everybody else why wouldn't you [ ZAG ]. But the execution just wasn't there to support it. And the losses expanded. And then the company did the right thing and the hard thing, which was to pivot. And one thing that's great about Appian, Appian can pivot quickly when it gets conviction about something that I mean I give Matt a tremendous amount of credit for that.

And so we've had reductions in the size of our workforce. We became extremely scrutinizing when it comes to any incremental headcount. And that's the world that Appian has lived for the last 2 years, and that's why margins have expanded the way that they have and operating expenses basically didn't grow. And so the trick for us is now to find that middle state, right? The middle state between feast and famine and focus on moderate growth and sort of investments that will drive top line while also leaving us room to improve profitability over time. I think we can do that. And again, the attitude of the company is like we all understand sort of how important profitability is. And when I have discussions around where are we going to draw that line on the list of proposals that we're going to fund for next year's budget, there's no debate where that line is. Just the debate is what's above versus below it.

Yes, yes, yes. Okay. So the -- because the fear we always had like -- it's not that Appian didn't get the memo. We just kind of decided to ignore the memo. And that kind of was kind of what scared a lot of investors is like well, that was a decision and it felt on our side, it wasn't rational.

Right.

Like, obviously, you joined the team now, like how much of kind of how real do you think that drive is? Or it's a little bit with sales force, this kind of sales force there, you think like what's coming next. Is -- are we -- what's your thinking there now like being part of the organization?

I can tell you that that's not a conversation that is happening. When I said we need to expand margins, there's just nodding in the room. So that's what happens. Now I just do want to put in there, given how much we've expanded margins this last couple of years and frankly, the revenue outperformance was greater and the speed to reinvest that is always a little bit lagging. We expect a relatively modest improvement in margin next year. But over time, like we have to do both. We have to do both. We have to grow, and we have to expand margins and a company with our unit economics should be perfectly capable of doing that.

Yes, yes, yes. Okay. That's a clear answer. Okay. Then last couple of moments. Capital allocation, like when you kind of came into the business, looked at the situation there, like what's your thinking in terms of like where you want to go for here?

So this is the thing that doesn't get talked about as much, even with investors who know us, which is that this will be the first year that Appian has generated meaningful cash flow. And we needed to fund the growth when we were unprofitable. We did that in the early years through equity issuance. More recently, we've issued some bank loans. And -- but now we're in a moment in which like we're generating cash, and we are approaching a moment where we're like net debt 0. Like we still -- our net debt is still a slightly positive number. I would prefer for it to be a negative number, i.e., that we have more cash than that simply because that's a healthier way to run a tech company and also occasionally that ends up being a conversation with our customers just so that we can tell them like, look, we better on in the next 10 years, like we have the financial strength to support you. And so we're approaching that moment.

And then look, I don't think it's hard to see what we think about capital allocation because we've done it. We did a $50 million buyback last year. We funded with debt. And then we did 2 $10 million buybacks this year to offset dilution. So I'm not suggesting any sort of significant changes over time. But at the end, Appian will return capital to shareholders, especially since we've done in 26 years, we've done 2 tuck-ins as a company. So M&A is not core muscle that we have or need.

I mean, is there an argument to keep some powder dry on that one though, given how quickly AI is moving. I mean, we see it in other parts of the industry where it's like there's an AI start-up that could be interesting that kind of accelerates me.

Sure. And it's not that we don't look at anything. We have a small corporate development team that is constantly tinkering. The reality is just it's going to be hard, right? We are of a certain size of a certain valuation, particularly in the world of AI, you get to large valuations without anything resembling revenue. So that's always going to be hard for us to stomach. But could I tell you 400% certainty, there is in some world in which like a perfect round peg shows up for a perfect round hole, but I wouldn't even know what that hole is right now. So in other words, we'll be opportunistic, but it seems unlikely to us that we would do anything so.

Yes. Terrific. Hey, that's a good closing statement. I really enjoyed our conversation.

Good to see you. Thank you, everybody.

Good day, and thank you for standing by. Welcome to the Appian Third Quarter 2025 Earnings Conference Call. [Operator Instructions] Please be advised that today's conference is being recorded.

I would now like to hand the conference over to your first speaker today, Brian Denyeau from ICR. Go ahead.

Thank you. Good morning, and thank you for joining us. Today, we'll review Appian's third quarter 2025 financial results. With me are Matt Calkins, Chairman and Chief Executive Officer; and Serge Tanjga, Chief Financial Officer. After prepared remarks, we'll open the call for questions.

During this call, we may make statements related to our business that are considered forward-looking. These include comments related to our financial results, trends and guidance for the fourth quarter and full year 2025, the duration and impact of the current U.S. government shutdown, the benefits of our platform, industry and market trends, our go-to-market and growth strategy, our market opportunity and ability to expand our leadership position, our ability to maintain and upsell existing customers and our ability to acquire new customers. These statements reflect our views only as of today and don't represent our views as of any subsequent date. We do not intend to update these statements as a result of new information unless required by law. Actual results may differ materially from expectations due to the risks and uncertainties described in our SEC filings.

Additionally, non-GAAP financial measures will be discussed on this conference call. Reconciliations of GAAP to non-GAAP financial measures are provided in our earnings release.

With that, I'd like to turn the call over to our CEO, Matt Calkins. Matt?

Thanks, Brian, and thank you, everyone, for joining us today. In the third quarter of 2025, Appian's cloud subscriptions revenue grew 21% to $113.6 million. Subscriptions revenue grew 20% to $147.2 million. Total revenue grew 21% to $187.0 million. Adjusted EBITDA was $32.2 million.

This quarter, Appian made big strides on our 2 efficiency metrics. Our go-to-market productivity ratio rose to 3.5. This is the ninth consecutive quarterly increase, see Slide 4. We keep getting more from our sales and marketing dollars, an intended outcome of our strategy over the last few years. In Q3, our weighted Rule of 40 score was 39, up from 31 last quarter. As a reminder, Appian's weighted Rule of 40 puts double weight on cloud subscriptions revenue growth compared to adjusted EBITDA margin.

The AI revolution took an important turn this summer. Businesses started to realize that AI isn't as valuable unless it's connected to real work and that you need a process or a workflow to make that connection. The most important factor was probably the July MIT report that told us that 95% of AI implementations were getting no return. As MIT wrote, "The standout performers are those embedding themselves inside workflows. And people are getting the message. Looking at Google Search trends, the term AI moderately increased over the past 12 months, but the combination of AI in terms like process and workflow spiked this summer.

News outlets like Forbes and Fast Company are publishing headlines like why AI isn't delivering the value you expected and good AI innovation means focusing on workflow, not cutting jobs. This trend also matches my personal experience and customer conversations. I see corporations quickly losing interest in stand-alone AI deployments and favoring the use of AI in the context of a process.

This new realization is not a surprise to Appian nor to you if you've been listening to our calls for the last couple of years. We have said consistently that AI forms one corner of an essential trio of technologies and AI triangle, if you like, in which AI is dependent upon each of the others. I've never had trouble convincing people that AI is only as good as the data you give it. It's obvious that AI agents cannot research cases, reach conclusions, solve problems or get smarter without 360-degree access to data. Our data fabric remains the gold standard in providing data to AI without having to migrate it.

Only recently have people started naturally agreeing with my second assertion that AI is only as good as the work you give it. AI will add more value if it works on the more valuable tasks. And the most valuable tasks involve many workers and many steps and are coordinated in a process. As such, process is an essential tool in connecting AI to meaningful work. With industry-leading process technology, we offer the missing link between AI and today's most important jobs.

When you combine AI, data and process, you can address bigger work and create bigger value. We call this serious AI. It's an exciting crossroads at which to do business, and we are not new here. Appian has orchestrated enterprise business processes for over 2 decades. We are recognized by industry analysts as a leader in process orchestration, business workflow automation, digital process automation and most recently, the inaugural Gartner Magic Quadrant for business orchestration and automation technologies.

I'd like to share with you a few examples of serious AI. First, a global pharmaceutical company and 7-figure cloud ARR customer manages its global anti-bribery and corruption practices on our platform. Appian already automates the company's highly regulated process for approving interactions with external health care practitioners and vendors. However, cycle times are slowed. By the many human reviews the customer built into its process. In Q3, it purchased a 7-figure software deal to deploy Appian AI. Now our agents will ingest hundreds of thousands of requests, validate compliance and recommend a status. This major pharma company expects to speed up the critical process by 80% with Appian.

Next, a U.S. military command became a new Appian customer in Q3 and will use our platform to automate end-to-end warehouse fulfillment processes. Upon receipt of goods, Appian AI agents will extract shipment data and open a case on our platform, so warehouse workers can validate the package and approve it for distribution. Meanwhile, back-office staff will track fulfillment status and coordinate logistics. Before Appian, the organization had prolonged distributions because its processes were disjointed and manual. Now the combination of Appian AI and data fabric will reduce processing time from weeks to minutes.

Customers see strong quantifiable value using Appian AI in their core processes. Organizations have reported 36% reduction in invoice processing times, 83% faster patient intake, 3x faster audit processing and 95% automation of the order management process, good ROI, and they're willing to pay for it. Today, over 1/4 of our customer base pays for Appian AI. Of those paying AI users, nearly half use our AI-powered intelligent document processing or IDP. This is a really powerful offering. Appian IDP agents can ingest a wide range of complex documents from unstructured e-mails to medical reports and insurance claims. Our agents read documents with 95% to 99% accuracy, which is significantly better than the 60% accuracy rate of traditional document recognition technology.

For example, one international insurer uses Appian IDP to optimize its underwriting processes and save millions of dollars a year. These strengths are why Gartner ranked Appian #1 in automated processing use cases for IDP in their 2025 Critical Capabilities report. Appian IDP agents take autonomous action. They explore data from across the enterprise to inform their decision-making on each new document. Once they contextualize and understand the incoming document, they launch actions in the form of Appian processes.

For example, a global insurer purchased a 7-figure cloud software deal to automate its underwriting process and became a new Appian customer in Q3. Appian's AI agents will ingest e-mails and policy forms, open cases and automatically decline ineligible submissions. The insurer expects to improve its auto declination rate by 20% and grow its written premiums practice to $10 billion within the next 3 years using Appian.

Early in 2024, we launched an AI product for improving government procurement. You may remember it, it's called ProcureSight. We collected a ton of publicly available government procurement data sets and connected that to our process technology so our users could publish smarter RFPs. 96 U.S. government agencies and sub-agencies have adopted it so far. Now customers are purchasing advanced levels of the offering to embed the AI into their procurement workflows and connect it to both public and private data sets.

For example, a U.S. Air and Space agency already automated its contract writing process with Appian technology. This quarter, it signed a 7-figure deal to automate additional phases of its procurement process with more prebuilt Appian solutions featuring AI.

I usually talk about releases after they happen, but we have a big one in 10 days, and I'd like to share it with you now. We're launching a major feature called Agent Studio that's going to enable the most powerful agents we've ever made with easy code-free natural language configuration. This is a highly anticipated feature judging by the oversubscription on the beta program and the widespread pre-GA deployment. The technology is poised for broad usage on release to triage customer complaints, initiate credit checks and loan applications and conduct background reviews.

Most of you are familiar with Appian's multiyear quest to focus on the top end of our market. Appian has a particular advantage upmarket, and it shows in the data. Appian suits the needs of the executive buyer and the large enterprise and the mission-critical use case. Compared to last Q3, Appian booked over 50% more new 7-figure software deals. We're pleased with our federal sector performance this fiscal year, which grew faster than our overall business. Our upmarket strategy will continue to drive momentum in the U.S. public sector once the government reopens.

I'll share 2 big wins from Q3. First, a major restaurant franchise operator plans to open 1,000 new locations next year. In Q3, it purchased Appian Cloud licenses and became a new customer after the Chief Technology Officer of a peer organization endorsed our platform. The customer's restaurant opening process used to take several months because the franchise and third parties worked across siloed systems. Now it will use Appian to unify the enterprise and create a comprehensive workflow to reduce opening time lines by 40%.

Finally, a U.S. military branch and existing Appian customer is under executive mandate to modernize core systems and improve operational agility within a fixed time period. This quarter, it purchased Appian software to decommission and flexible systems supporting its complex incident management process. Now Appian will provide a consolidated and more feature-rich application to manage hundreds of thousands of cases annually. Appian's serious AI offering and upmarket strategy continue to drive our growth while expanding margins. I think you can see in the fact that 25% of our customers now pay for AI and in our 50% increase in 7-figure deals. and in our 9 quarters of rising go-to-market efficiency and in our adjusted EBITDA margin of 17% the power and timeliness of our business model.

With that, I'll hand the call to Serge.

Thanks, Matt. I'll begin with a detailed review of our third quarter results and then finish with our outlook for the fourth quarter and full fiscal year 2025. Starting with our Q3 results. Appian exceeded the guidance ranges we provided in our key metrics of cloud revenue, total revenue and adjusted EBITDA. Strength in the quarter was driven by the traction we are seeing with AI and continued momentum in our focus on the high end of the market, as Matt mentioned in his remarks.

Cloud subscription revenue was $113.6 million, an increase of 21% year-over-year. On a constant currency basis, cloud subscription revenue increased 18% year-over-year for the fourth straight quarter of growth in mid- to high teens. Total subscription revenue was $147.2 million, an increase of 20% year-over-year. On a constant currency basis, total subscription revenue grew 17%. Professional services revenue was $39.8 million, up 29% compared to the third quarter of 2024. As a reminder, services revenue can be volatile quarter-to-quarter.

Subscription revenue represented 79% of total revenue compared to 80% in the year ago period and 78% in the prior quarter. Total revenue was $187 million, an increase of 21% year-over-year. On a constant currency basis, total revenue grew 19%. Slide 8 of our earnings presentation shows the history of our constant currency growth rates.

Our cloud subscription revenue retention rate was 111% in Q3 compared to 117% a year ago and 111% in the prior quarter. Our international operations contributed 40% of total revenue compared to 36% in the year ago period. Cloud net new ACV bookings were approximately 90% of total net new software bookings in Q3 compared to 88% in the prior year. Q3 cloud net new ACV growth was the strongest we've seen so far this year.

Now let's turn to profitability metrics. I'll be discussing our results on a non-GAAP basis unless otherwise noted. Gross margin was 77%, unchanged from the year ago period and compared to 75% in the prior quarter. Our subscription gross profit margin was 88% compared to 89% in the year ago period and 87% in the prior quarter. Professional services gross margin was 34% compared to 30% in the year ago period and 33% in the prior quarter.

Total operating expenses were $113.6 million, up from $110.2 million in the year ago period. The 3% growth in OpEx reflects both our continued focus on efficiency as well as approximately $6 million in marketing, training and consulting expenses that were originally forecast in Q3, but we now expect to incur in Q4. Adjusted EBITDA was $32.2 million versus our guidance of $9 million to $12 million and compared to adjusted EBITDA of $10.8 million in the year ago period. This outperformance relative to our guide was largely driven by greater-than-expected revenue as well as the timing of expenses I just referenced.

Net income was $24.4 million or $0.32 per diluted share compared to a net income of $1.8 million or $0.02 per diluted share for the third quarter of 2024. This is based on 74.6 million diluted shares outstanding for the third quarter of 2025 and 74.2 million diluted shares outstanding for the third quarter of 2024.

Turning to our balance sheet. As of September 30, 2025, cash and cash equivalents and investments were $191.6 million compared to $159.9 million at the end of last year. For the third quarter, cash provided by operations was $18.7 million compared to $8.2 million cash used by operations for the same period last year.

Turning to guidance. For the fourth quarter of 2025, cloud subscription revenue is expected to be between $115 million and $117 million, representing year-over-year growth between 16% and 18%. Total revenue is expected to be between $187 million and $191 million, representing year-over-year growth between 12% and 15%. Adjusted EBITDA for the fourth quarter of 2025 is expected to be between $10 million and $13 million. Non-GAAP earnings per share is expected to be between $0.04 and $0.08. This assumes 74.5 million fully diluted weighted average shares outstanding.

For the full year 2025, we are increasing our guidance for cloud subscription revenue and total revenue. We're also increasing our overall adjusted EBITDA range for the year. Cloud subscription revenue is expected to be between $435 million and $437 million, representing year-over-year growth of between 18% and 19%. Total revenue is expected to be between $711 million and $715 million, representing year-over-year growth of 15% to 16%. Adjusted EBITDA is now expected to range between $67 million and $70 million for an approximately 10% margin at the midpoint of the range. Non-GAAP earnings per share is expected to be between $0.50 and $0.54. This assumes 74.6 million fully diluted weighted average shares outstanding.

Our guidance assumes the following: First, we anticipate term license revenue to be flat on a year-over-year basis in Q4 and to grow in the mid-single digits for the full year. Second, we expect professional services to grow in the teens for both the fourth quarter and the full year. Third, total other income and interest expense will be approximately $3.2 million in Q4 and $13.8 million for the full year 2025. Fourth, our guidance assumes FX rates as of early in November.

Finally, I want to explain how the ongoing government shutdown is reflected in our guidance. We've been cautious in our approach, and we assumed a modest amount of disruption in our guidance. However, we do assume that the government will reopen in the coming weeks. Since we don't know how long the shutdown will last, we wanted to help you understand the impact on our guidance in a hypothetical scenario that the shutdown continues through year-end. In that scenario, we believe that we could -- there could be up to $10 million impact versus this revenue and EBITDA guidance.

The vast majority of this scenario impact will be to our term license revenue, a meaningful portion of which is related to renewals. We would expect only a small potential impact to cloud subscription revenue and professional services margin in this scenario. We are confident that whatever impact we might see from the shutdown in Q4 is just a function of timing.

In closing, we're pleased with our Q3 results, particularly our continued improvements in profitability. We're energized by the opportunity we see ahead of us, and we'll continue to invest responsibly to maximize our long-term value. Now we'll turn the call over for questions. Operator?

[Operator Instructions] Our first question comes from the line of Sanjit Singh with Morgan Stanley.

I wanted, Matt, to talk about, I think Serge mentioned that cloud ACV bookings this quarter was the strongest of the year. I was wondering where -- like where that strength was derived from, whether it was particular industry, whether it was a strong Fed quarter? Was it an enterprise? Just love to get some color on that. And then I have a follow-up.

Yes. First of all, I'm not going to attribute it to any sector. I think it was a broad strengthening. I think it's the continuation of our upmarket strategy and the traction we're getting with AI. That's where I'm going to have to attribute it.

Awesome. And then on the go-to-market side, right, we're seeing kind of sustained growth rates in cloud, the margins are headed higher. You've seen a multi-quarter improvement in sort of go-to-market efficiency. Where do you think we are like if we use the baseball analogy in terms of that sort of go-to-market transformation? Are we sort of well along the path? Or do you still see go-to-market productivity enhancements continuing to effectuate for the -- going into next year?

Yes, Sanjit, let me take that. So we're very pleased with the progress that we're making. As you noted, we've now seen cloud subscription revenue growth stable in the mid- to high teens on a constant currency basis for 4 quarters in a row, and we've continued seeing the improvements in our go-to-market productivity metrics. And what that really means is just that our focus upmarket and resulting in higher sales productivity. And so we're making great progress in our move-up market.

Where we are in terms of innings, I would say maybe fourth or fifth inning because what we're going to do now going into next year is returning to growth in our sales org. So we focused our efforts on our existing sales org over the last, call it, 12 to 18 months, and we've seen significant improvements in execution and focus and improvements. And now the goal is to return to growth and continue improving productivity while we also grow the size of the org. Because to us, the key goal here is really to create a sustainable compounding growth engine. And for that, we need to both grow our coverage, which we have plenty of space to do as well as maintain and continue improving our productivity.

Our next question comes from the line of Steve Enders with Citi.

I guess I want to start on just the Fed side, and I appreciate the call out for Q4. But I guess I want to understand a little bit better just the impact that maybe you were seeing from the efficiency focus from the government this year? And maybe how does that play out in the big budget flush quarter in 3Q versus maybe how you were originally thinking about it?

Yes. Let me say that I love what's happened to the government overall in its purchasing patterns this year. And the shutdown is a temporary thing, but the changes the government has instituted in the way they approach technology, those are more long-lasting. Efficiency has become the priority. The government is willing to see software as the answer. It's open-minded about using AI. It's willing to do direct deals with an organization like Appian, like a midsized software firm. These are all enormously positive changes. And then we've got the temporary negative of the shutdown. So overall, I'm really bullish about the government business.

Okay. That's good to hear. And then I want to ask on AI Studio and that release coming out in the next couple of weeks. I guess what has been the feedback so far from the early customer program? And how are you kind of envisioning the monetization for that product as that starts to get rolled out into general availability for customers?

Yes. First of all, let me say that not only do we have the most oversubscribed beta program we've ever had, we also had the most positive feedback from the most different users we've ever had. So this is a much anticipated release. It's really taking the -- we're pioneering what's possible with AI agents. And I could go into detail, but I'll keep this short. We're very excited about the release. We feel like it's going to make a big difference for a lot of customers, and it puts us in the in the vanguard of innovation, what you can do with agents and process together. So that's really exciting. What else did you ask about?

Let me jump on modernization. So monetization will go in the form of continued expansion of our AI advanced tier. So Agent Studio will be available in the AI advanced tier. And as Matt mentioned, roughly 1/4 of our customer base is now paying us for AI. So incremental features like Agent Studio will continue pushing that number higher. And then secondly, there will be elements of consumption as a part of Agent Studio. And as customer use cases, in particular, drive, that will be an incremental driver of growth in the long term, but it's an important incremental lever.

The next question comes from the line of Raimo Lenschow with Barclays.

Congrats from me as well. I had 2 quick questions, one for Matt, one for Serge. Matt, if I think AI and AI adoption, we obviously are in a bit of a race. A lot of vendors are trying to do -- stake out their claim here and trying to be like the center of the universe. Can you think -- can you kind of discuss a little bit what kind of -- what do you think the determining factor will be for someone that can deliver versus someone that just has more marketing slides? And then I have one follow-up for Serge.

Yes. Okay. So the AI market is going to be interesting in the next couple of years. First of all, I think it's essential to differentiate between those who are creating the AI and those who are creating a complement for AI. Our technology is explicitly intended as a complement like a car is to an engine. We mean to enhance the capabilities of AI and to bring that power into the hands of our customers. So specifically, we take that AI and we give it connection, which is to say we connect it to data across the enterprise, connect it to workers. We give it secondly, coordination, which is to say we tell it when it's its job, when does it take a turn, who does it give the work to when it's finished. That coordination makes it part of a valuable process.

And finally, we give it governance, which allows it to -- you get oversight and auditability and changeability and guardrails. Those are essential components and they stake out process as a technology as being a really essential complement to AI going forward. I think that realization began in the summer, but it's going to continue. And I think we're going to see process accepted -- process software accepted as a complement to AI software.

Now in terms of how we differentiate from others who would provide process software, I'd say we've got a natural advantage here in that we've been focused on process and data fabric for a long time. Lately, it's become obvious that those things are essential complements to AI, but we were in this and leading this long before it was known. And therefore, we've got a big head start over anybody who's seen the writing on the wall and is now going to scramble to try to create process software or a data fabric like we already have.

Yes. Okay. Perfect. Makes sense. And then, Serge, for you, the -- obviously, there was timing in the profitability number this quarter, and that's why the guidance is for Q4. If you think about the overall profitability performance, though, that's kind of something that you guys will be measured on. Like where -- what -- how do you see if you kind of take away the timing differences? Where -- what path we are on there? And how do you think it from here now that you've been in the job for a little bit longer?

Yes. Thanks for the question, Raimo. And I think you're right. I think the better way to think about our profitability is to sort of zoom out from quarter-over-quarter dynamics related to timing and seasonality and focus on the full year. So we're guiding to EBITDA margin at 10% at the midpoint of the range, which we think is a significant milestone from us, particularly when you think about where we've come from over the last couple of years. And that's really the credit to focus on efficiency and improving productivity across the company, but especially in our sales org.

As we look going forward, the key for us is to develop -- to deliver sustainable revenue growth and continued margin expansion. I would say, though, as we think about next year, in particular, in the context of what we've done this year and how much we've exceeded our own expectations when it comes to margin, I would expect more modest margin expansion ahead.

The next question comes from the line of Devin with KeyBanc Capital Markets.

First one I have is I just want to dive into your international performance there. I think you mentioned the mix uptick in 40% total, which kind of implies growth in the 30% range, which is a very nice acceleration there. Any color you can kind of give us on what's driving the strength there? Any differences in types of sectors gaining traction? And are you perhaps seeing more uptiering or AI adoption in international versus the U.S.?

This is a broad wave, but you're right, it's AI, and it's succeeding at connecting higher in organizations, having higher-level conversations, addressing larger projects, creating more value. We do that by connecting AI to real work and making it pertinent to decision-makers at the top of the organization. I've got to say that's the biggest driver.

I just want to say on the more pedantic side, FX was also helpful here because obviously, dollar has depreciated versus foreign currency. So that's part of the drive. But to Matt's point, not -- by far, not the whole thing.

Yes. No, that's helpful. And then just one quick one for Serge. Professional services, the gross margin there, really strong, close to 35% in the quarter. Any additional color you can give us on what's driving that uptick? And how should we think about that level in terms of sustainable gross margin for professional services moving forward?

Yes. We're very happy with our performance in professional services, both in revenue as well as in margins. And the way that I think about our professional services org is that it's a highly strategic asset for us. Our customers like the high-quality implementation, and that's why they're willing to pay a premium, and that's why we have the margins that we have. And then the other thing is that professional services drives ARR growth and increasingly AI adoptions because our customers are more likely to turn to our professional services or for AI implementations.

When it comes to margins, we are very happy with the performance in this quarter. I will say that the margin is particularly high because we've exceeded our bookings expectations, and that's resulted in much higher utilization of our team, frankly, probably a little bit higher than sustainable. People are working weekends and not taking vacations. So maybe the 34% level isn't quite sustainable, but we think high profitability going forward is also in the cards.

[Operator Instructions] Our next call comes from the line of Derrick Wood with TD Cowen.

Matt, we're seeing some software vendors move into forward deployed engineer models to help accelerate adoption. How are you thinking about the services model in the context of Appian and AI deployments and potentially kind of lean in around FTEs?

Yes. Well, look, our model well predates the phrase forward deployed engineer, which I always thought was a bit amusing. But we have gained benefits as an innovator from emphasizing CS over the years. We have always put CS forward. They've been a differentiated innovative force. They've been able to develop our new technologies into fruition and demonstrate to customers. And it's helped us -- it's an accelerant. It's helped us move faster and deploy our new benefits into the user base. So Appian is a technology-led organization. We've always won through superior technology and the CS force has helped us bring that -- the benefits of that technology to our users.

It also, along the way, allows us to get greater retention, greater expansion, better relationships with our customers, a direct pipeline, and it's a terrific route for talent to come up through the organization. So we are unapologetically a services featuring software organization. I think it's worked great for us, particularly in our position as an innovator.

Great. And Serge, can you help us understand just the bridge between your cloud net revenue retention rate at 111% and cloud growth at mid- upper teens over the last 4 quarters. Is the delta from migrations that aren't counted in the NRR? Or how should we think about the relationship between these 2 metrics?

Yes. Thanks for the question. So the net retention rate was 111%, which is stable compared to the quarter ago. And we talked a little bit about this metric in the past. Expansion from our existing customers is very important to us. Obviously, it's one of our key drivers of growth. But it's not the only one, right? We also go after new customers, and we've been very successful over the past year, in particular, of winning some large new logos straight out of the gate, which I found very impressive given the -- where we are in our upmarket journey.

And the -- so it's the net ARR retention is a part of the growth story, but not the whole growth story. The other thing we talked about in the past is that our net revenue retention is a bit backward looking, meaning that we look at revenue over the prior 4 quarters for customers who had been with us in the 4 quarters before that. So it tends to lag trends in revenue. And that's why you can see a discrepancy between those 2 at times, but it has nothing to do with the migrations. We don't see very much in the way of migrations from on-prem to the cloud. In fact, we see continued healthy growth on-prem, which you also see this quarter.

The next question comes from the line of Jake Roberge with William Blair.

Just wanted to double-click into the expectation to grow sales headcount again next year. Can you talk about how you're expecting to balance those investments with just the continued margin expansion?

Yes. So as I mentioned, and I'll kind of go across the board, but starting, of course, with our biggest expense line, sales and marketing. We've achieved improvements in productivity and our efficiency metrics that you see over the last 12 or so months, roughly in flat headcount. And that is sort of the right way to approach it if you think about it, because as you move upmarket, as you focus on better execution, we've also brought in new sales leadership, you don't want to be doing that while at the same time growing headcount. You want to do one thing at a time.

But now we're at a moment in which we feel confident because we made significant improvement when it comes to our productivity that we want to return to what I would describe as moderate headcount growth. And the key goal for us, again, is to build a sustainable growth engine. And to do that is, first, you need a large market, we have it. And then secondly, you need to expand your coverage and continue growing your productivity. We've done well on productivity. Now we got to return back to expanding coverage.

So as we think about -- obviously, we're not providing guidance today. But as we think about sort of the mix between growth and margins for next year, we expect to deliver both. But margin expansion, particularly in the context of what we've just accomplished over the last 2 years, we're forecasting 10% EBITDA margin at the midpoint of the range for this full year versus negative 10% 2 years ago. We are going to be more modest as far as our margin expansion because we're increasingly confident in our ability to create this sustainable growth engine.

Okay. That's helpful. And then we're hearing that this legacy app transformation opportunity is seeing a pretty meaningful uptick in interest right now. I know you've been building some new solutions in that area. Can you talk about how you're thinking about addressing that opportunity moving forward?

All right. First of all, that was not an entirely clear question. There was some breakup on the line, but I think I heard enough of it to answer it. correctly, and you could just tell me if I miss any detail. You're asking about the modernization opportunity, which is essentially the conversion of legacy applications into a modern platform. And we intend to be -- we are now, but we intend to be a leader as this market grows to large scale. I do think it's going to be a substantial, a meaningful new software market. I think we're extremely well positioned for it.

We've been in the modernization market for a decade already, and we have performed some admirable and publicly recognized modernization projects. But today, of course, the volume is going to increase dramatically because AI makes it easier to extricate and understand existing legacy applications and also to rewrite them in a new platform, in this case, Appian. We've got a number of advantages that I think will really differentiate us in this market. First and most importantly, that if you're going to port an application from a legacy platform, by all means you should port it to a platform that is the opposite of legacy, a flexible growth tracking, scalable, feature-rich platform like Appian would be ideal for porting your old applications.

Secondly, we handle the job of recreating an application in a really collaborative way. So there's a deep collaboration between AI and the developer, where AI will propose things and the developer can modify. And we just saw another demo of this yesterday. It's spectacular. I'm really impressed with the technology we put together. I think there's nothing like it. We're going to create a dialogue, which is just what you need for the most important applications. This isn't a simple delegation and you move one stack of code into another stack of code. It's about reinventing an old application, making it better, maybe combining it with other applications along the way and creating something that suits the modern moment. That's not just a mere translation. Even if AI could do the whole translation, it would be insufficient. It's a collaboration.

And so that, to me, is the golden key to getting modernization right is creating a rich collaboration so that those old applications aren't just ported. They're not just translated, they're recreated for -- on a better platform for the moment, right? So that's what we're focused on. I love this market. We didn't talk about it much in the prepared remarks today because we understand that we got to -- we have to emphasize agents this time. We got a new launch there, but our excitement is undiminished.

And this does conclude the question-and-answer session and the program. Thank you for your participation in today's conference. You may now disconnect.

All right. Awesome. Well, thanks, everybody, for being here today. Welcome back from lunch. Day 1 of the Citi's Global TMT Conference. I'm Steve Enders, part of the software research team here at Citi. And with us for the session, we have Serge from Appian. Serge, thank you so much for being here.

Steve, thanks for having us.

Maybe for those who might be a little bit newer to Appian, can you just give a little background on the company and what is it that Appian does?

Excellent. It's no better place to start. So Appian is a platform that allows customers to automate and orchestrate their most important enterprise processes. So that's a mouthful. So maybe the best way to explain that is through a few examples. So for example, one of our largest customers is a global pharmaceutical company that applies us as an enterprise standard workflow tool to manage processes such as clinical trials, logistics deployed to 50,000 of their employees. We're in a branch of the U.S. military, is in the process of consolidating a number of their legacy systems that do finance procurement, supply chain management onto an Appian platform. It will eventually be deployed to hundreds and thousands of users and will save the government tens of millions of dollars.

We're one of the largest asset managers in the world. I've actually seen them here in the conference earlier today, uses Appian to onboard and manage their customer relationships and they replaced a number of manual processes when they deploy our platform. Or just to give you a little bit more color or context, medical device manufacturer uses Appian to automate life cycle of a device from order, all the way to installation. We replaced an internal homegrown tool that wasn't scaling.

And then as a final example and to add a little bit of AI to the equation, a mortgage lender is using Appian to audit tens of thousands of applications annually, and they use our agents to check, cross-check information across multiple different documents with 98% accuracy and then loop in the human as needed to facilitate the process save time and money.

And if you kind of take a step back from all of that, what you hear is automation, what you hear is orchestrating end-to-end processes, replacing manual effort, replacing homegrown solutions, point solutions or legacy applications in this period of providing better agility performance, flexibility, and that's ultimately the value that we bring to our customers.

Yes. Perfect. No, I think it's a great place to start here. You recently came over to Appian from MongoDB. I guess, what led you to Appian? Why did you decide to move over to Appian?

Yes. It actually begins right where we ended the last question. It begins with the product. The product is very good. You see it in our retention rates. You hear it when you talk to our customers. Our customers see us as a mission-critical platform that they concentrate their spending on and they like partnering with us. They like our professional services team. Again, the product is very, very good. So if you're going to have a foundational asset on which to build the company, the quality of product is as good as it gets. So I was very happy to see that. I find that out in my research, and that was the first thing that attracted me.

The second thing I would tell -- I would say is that our AI value proposition really resonates in the marketplace right now, and I think is what the enterprises need, which is way to deploy AI that generates tangible value, but very importantly, subject to their own accuracy, security and auditability requirements. And there's a lot of the disconnect in terms of the promise of AI and what it can actually deliver and what the customers are comfortable using AI for these days and Appian fills that gap today in a way that we believe will resonate even more in the market going forward.

Three is the ability to improve execution across the board, but particularly with our move up market on the sales side. We've seen evidence of continued improvements under the new sales leadership that we think we're in the early innings of what can be done there. So as you think about ways to generate value and sustain a sustainable, efficient growth, sales execution is a critical component.

And then the final thing is the culture. It's a company that wants to win. It's a company that is intense and wants to be successful in this large and growing market, and I'm very excited to be a part of that.

Yes. That's great to hear. I do want to dig in a little bit more on the go-to-market. But before jumping into that, I think you've been on the job now for 3 or 4 months, I'm going to say.

3.5.

Okay. Right in the middle there. Where do you see kind of the most opportunity to help kind of operationalize the business and see areas where you can find the most efficiencies and just help Appian become a better overall company?

Yes. So let's talk about opportunities for growth and then maybe opportunities for efficiencies, and there's overlap, but there's also some differences. On the growth side, we're still in the early innings of our go-to-market, move up market and sort of continued improvement efficiency and effectiveness there. So I think in terms of hiring the team, we're making -- sort of upgrading our talent, we're making progress, improving all our processes anywhere from enablement of salespeople to forecasting to actual negotiation and closing of the deal, there's opportunity for growth and improvement across the board. And the way that you will see it manifest itself is ultimately, in sales productivity, efficiency of sales and marketing spend. And we've made progress on that front, but we think that there's more to go.

The second part when it comes to growth is going out there and telling our AI message better. As I said, we feel like there's a great desire by enterprises to hear how others are using AI in actual production use cases. And we have plenty of examples that we can bring to the market and give customers an idea of what's possible with AI today, which will generate more business for us. So that's the next opportunity.

And then on the growth side, we're increasingly feeling like the changes that were happening in the federal government are actually an opportunity for Appian. The drive to efficiency what DOGE has been doing, yes, it's introduced incremental uncertainty into the market over the last few months here. And obviously, we're in the midst of the biggest federal quarter, so we'll have to see how that plays out. But some of the changes that were made in terms of how the government wants to do business and what that means for our ability to work with the government is actually increasingly seeing like a net positive for us in the long term.

And then on the efficiency side is with increased sales productivity comes improved sales and marketing leverage. So as we continue -- we've seen improvements in productivity over the past 12 to 18 months, and we can continue seeing that, that will drive the efficiency of our marketing spend, it will increase the payback on our sales and marketing dollar, allowing us to invest more while expanding margins. Second is tasting our own medicine, applying AI across various processes, whether that is customer-facing functions, whether that is frankly generating our own code, where we're seeing some early success as well as all the back-office function across the company, we can use Appian technology to produce incremental efficiencies.

And then finally, it's just being smart where we hire globally and continuing to get benefits from a global and distributed workforce to drive better operating leverage. So all those add up to growth opportunities on the revenue side and opportunities to expand margins and balance revenue growth and margin expansion as we go forward.

I guess, looking specifically at the efficiencies that you have seen in the go-to-market, I guess, what was it that you that you've changed to be able to drive that? And where do you kind of see incremental opportunities to, I guess, kind of further enhance that approach and drive further efficiencies and leverage from here?

Yes. I would say that there's been 2 specific levers. So if you go back 12 to 18 months when we first started talking about a move up market, we had a clear effect of focus. So we've frankly shrunk our sales force. We've eliminated the least productive part of it. And we've improved sort of -- mathematically, we've improved productivity because we've decided to focus where we're seeing the most success. And so that was a difficult decision to do for any company. It was difficult for Appian to do at the time, but it was the right decision to do, not just for the purposes of improving our financial performance, but also for the purposes of driving focus inside the company and what we do day in, day out. So that's behind us. And that was sort of a onetime change that was needed and improved efficiency, but also improved our sort of scope and focus internally.

And then what we've done since then is really just build foundations for better scaling of our sales org in the future in terms of some of the processes that we talked about in the past. And whether that is hiring new sales leadership, whether that is actually replacing the Street as well, whether it is forecasting, qualifying deals, negotiating. And what you see happening is you've seen that we're getting bigger deals, even bigger lands with new logos. And what we've seen -- what you generally see with enterprise sales forces is the momentum begets momentum. And when you see success under your own success or your colleague success, when you see pipeline growing, it further increases sort of the confidence that we have to go fight for the value that we believe we deserve and that we get out there in the market.

Okay. That makes sense. Maybe we start to pivot a little bit into the AI discussion. I know that you have started to drive a little bit of AI revenue, but I still think we hear concerns in the market from investors around how does Appian monetize it? Is there a risk to the revenue stream from AI, whether it's a seat-based replacement or maybe there's different ways to build applications? Just how do you think about the, I guess, risk versus opportunity for Appian? How do you kind of capture the next use case within a customer? And I guess as you think about that customer, a lot of ways to build an application now, where does it make sense for Appian? Where is there maybe more of a code-based solution? Just how do you think about that?

Yes. So maybe we'll talk about AI at sort of a high level and then we can drill into 3 different buckets of opportunity for us because we see this heavily skews in the favor of upside for us as opposed to risks. And I do think there's quite a bit of misconception about this in the market. Honestly, not just about us, but software more generally. So as you think about some of the use cases that I talked about in the beginning, and the processes that we help automate and orchestrate, AI supercharges the value that the customer gets from that process when applied properly. And so whether that is further replacing manual steps in the process, whether that is automating processes that weren't automatable before, whether that is adding an incremental step that accelerates or simplify the process, AI has many ways of adding value inside the process, but critically inside the process. And because that's where we play, we think that the opportunity to us accrues in 3 different ways.

The first one is it increases the number of processes that customers are interested in automating, how they're willing to automate and the value that they believe they can get from that process. And because we ultimately -- no matter what the mechanism it is, we ultimately try to price to value. That means that the value that we ought to be able to extract from those applications is growing for us as well. So we share proportionally in the customers' game. And that's in terms of the types of use cases that are going to be built, some of them that were already seeing being built in the ROI that the customers are getting on it. And it's true for new and existing customers as well, which is very exciting for us. Once customers are ready to deploy AI in production, they find Appian quickly because of our approach and philosophy of how to deploy AI in a process in a way that is frankly sympathetical with the constraints that customers want to put on the AI technology. So it's the first bucket.

The second bucket is, and we talked about this a little bit on the last call, is AI offers the promise of accelerating modernization of legacy tech stacks because with the combination of services and AI, it becomes easier to extract application logic from existing, in some cases, very old applications and deploy it onto some new stack or in our case, Appian. And we have an incremental benefit -- incremental advantage in that process as well because what customers want to do is not just take old apps and build them into some new set of code or a new stack, but they also want to reimagine their processes. And that's where our process expertise comes into play. And like how do you think about if you remove the constraints from legacy architecture, how would you actually build this process from scratch? And the art of the possible there is significant. So that's further incremental use case.

And then finally, as building applications become easier, more applications will be built and our platform will benefit from that as well, which I want to touch base to this idea that now there's many ways to build an application and some of the fun buzzwords out there like wipe coding and so forth. I just want to be careful because although those tools improve productivity of whoever is working in it, it's difficult to see it actually replacing a platform on to which to build application because that platform requires an ability to communicate with the business user, an ability to visualize the process and ability to then introduce security, auditability, have the real performance and scalability. So although bits and pieces do become better over time, and I'm sure that over time becomes -- the AI benefit will accrue in multiple different ways, the complexity of the system and the interconnectedness of the data and the controls and the guardrails means that the platform is as needed as it ever has been.

Okay. So you're saying the platform approach, the auditability, security, governance, all that matters more and that's not going to be replaced by...

I would say it's not replaceable. It matters more is semantics, but it's not particularly helpful. This is where our sort of engine versus the car analogy is helpful. So you've heard us talk about this in the last couple of calls. So like AI is a new engine to put into an automation/orchestration process, but it still needs the process itself, and that's the engine versus the car analogy. And so it's -- all those pieces are exceptionally important. And if you talk to enterprise customers, they will all tell you the same thing, to actually get the value at an acceptable risk from an AI strategy, and we don't see that changing.

Okay. All right. That makes sense. You have been working on your AI product set, the solution set. And I think coming out of your conference, it seemed like the use cases were pretty compelling and a lot of new innovation there. How are customers, at this point, how are they thinking about where does it make sense to build? Where does it make sense to buy? And how does the platform decision-making process maybe change from AI?

Yes. So will we overwhelmingly hear from customers is they don't have the expertise to build. And proof of concept is one thing, but putting something in production and putting your job on the line is a different thing. And so in the build versus buy conversation, certainly, all the customers that we have exposure to, and we have a pretty solid exposure sort of across the Global 2000, the answer seems to be buy. So that's point number one.

Point number two is, when it comes to buy and buy what, customers are looking for use cases where they can get tangible value at an acceptable sort of trade-off between risks and performance. And what that means is they still want systems that are 98%, 99% accurate, where there will be a human in the loop, if needed, where the process is auditable because ultimately, when you take a step back, what you're trying to do with AI is you're taking a nondeterministic or a probabilistic technology and trying to shove it into a deterministic process to derive value without causing things to go haywire.

And so what we see is partnering with us to engage our professional services because they need help implementing these solutions. And most of what we're seeing right now are document-based use cases. So whether it's document summarization, extraction, comparing data. And those -- that can be significant value in the context of the types of workflows that historically were deployed in Appian. So customers are excited to go down that path, but many of them are not ready and those who are ready, need significant help.

That makes sense. I mean if I think about some of the use cases that maybe you're going after, I think there have been some traditional, either like OCR technology or some of those kind of like more document-based solutions. It seems like that's kind of where that's centered at. Just how do you think about, I guess, net new TAM opportunity that you're kind of going after that kind of opens up the aperture for dollars versus maybe there is some kind of displacement kind of going on in that market?

I think it's both. I think it's both and it's significant. So obviously, it's not new to us. So from some perspective, like it's semantics. But ultimately, we have great opportunity to gain share with our existing customers. A number of our customers, certainly, the government is this way, but they're not the only ones, are looking to consolidate on fewer platforms, get out of as many legacy technologies as they can and modernize on a platform like Appian. So that's opportunity for us to gain share in existing customers.

And obviously, if you know us, chances are you like us. And if you like us, the change are you'll give us more business. And that's the vector in terms of acquiring incremental use cases inside of existing customers. But what we're also seeing is that our penetration is low when it comes to new logos even in the Global 2000. And some of the -- frankly, one of the things that I've been positively surprised is the type of new logos that we're winning since I've joined Appian and the initial size of those deals. And AI is actually a big part of that role because if you have an AI use case that you want to bring forth in production, you are quite likely to come and find us, which is why 50% of our new logos over the last couple of quarters actually came at the tier that includes AI features.

Sure. No, that was -- it's great to hear. I think we're about halfway here. If there's any questions in the room, I want to make sure that we can get to those and address those. But I guess continuing on kind of the AI discussion, I guess, how does the Appian AI strategy evolve from here? Where do you kind of see the most opportunities to build that out further within the product set?

So I will call out 2 particular vectors of further innovation that are exciting. First is our AI Agent Studio, which is in beta and will be GA later this year or early next year. And that's a set of technologies that will allow customers to build their own AI agents in a way that is congruent with our overall platform and approach to process. And some of the early beta use cases are exciting and show an incremental variety of use cases that we can go after. And so obviously, as we mature that platform and that product, a number of use cases that people will feel comfortable deploying AI for will only grow over time. So think of that as like ways to win incremental applications either from new or existing customers to go onto the platform.

And the second one is our Composer product, which is also in beta, which will allow for a more seamless communication between the business user of an application and the actual person building it, whether that person is Appian professional services, customer resource or a partner. And that conversation is ultimately where the value is happening. It's like facilitating, making it easier to get the requirements, making it easier for the business user to explain what they want and the art of the possible to the person building the application and using AI to make that process more seamless will only further reduce barriers to adoption to Appian everywhere. And so I see us investing in both of those over time and that sort of expands both sort of the number of use cases and how quickly they can get on the platform if you think about those 2 as the axis.

And then the final thing, which we're in the early days, but it's what is the product that we can build behind the concept of modernization. This will always be a solution that involves both services and software, but is there software that we can build in addition to the Composer to extract business logic faster from applications to facilitate our services and partner services to accelerate the process of modernization with our various customers.

That makes sense. I know you've given some disclosures around , I think it's the advanced tier in terms of like AI contribution. How do you think about AI disclosure? How quickly maybe some of these capabilities are going to begin to impact the model? Like what does that look like on a go-forward basis?

Yes. So maybe we talk about the enabling factors and then we'll talk about the metrics first. So what we hear from the field is that the principal gating factor in terms of further AI adoption is actually customers being willing to bring use cases forward because of where they actually are in terms of their own comfort with their technology. And I think that's a bit of a misperception versus when you hear at conferences like these. So customers have deployed the productivity tools, so whether that's Copilot or Gemini or so forth. So that gives -- that's a way to turn around and show their Board, yes, here, we are using AI, we are doing broad-based productivity improvements through these tools. They run a bunch of POCs, most of which have failed, and then there's some subset of them that have succeeded and now it's time to move to production. But the gating factor is those that are ready to be production moved. And that's maybe surprising given that we've all been talking about AI and nothing but after the last 2 years, but that's like the reality of like a cycle of enterprise adoption where we are with it.

And so once the customer is ready, then they will show to us that either as a new or an existing customer, and they will want to get our advance tier features. And so what we've seen, and I mentioned already here today is that over the last couple of quarters, 50% of our new logos is coming from customers who want the advanced tier, which likely means they have a production-ready AI use case and they are ready to roll out. It's not the only feature, but it's the most prominent feature in the advanced tier. So we're happy to see that people are basically walking off the street and pick us as their AI solution.

And then the same sort of gating factor exists with our existing customer base, which is do you have a use case? And when there is a use case, getting the upgrade is not difficult because the ROI is there to justify it in terms of savings that they generate in the process. The question really just becomes, are you ready? Are you as a customer ready to roll out? And so they happen -- and so the metrics are the output of customers' readiness to engage with AI as opposed to like our ability to drive and incentivize through price or other means the adoption because ultimately, once you have a compelling AI use case and a way to safely and securely deploy AI inside of a new or existing process, our 25% uplift is not an issue. The ROIs is there. The issue is just like are you ready to actually go through that exercise?

Sure. I guess when you think about either the customer base or when you're finding new opportunities, just maybe where are we in that journey in terms of customers actually ready to deploy AI applications? Like are we still kind of like in the early stages of that?

Maybe not first inning, but second or third.

Okay. What does it take for that to begin to inflect for us to kind of turn the corner and kind of see a little bit more mass market adoption?

I think it's the linear continuation of all the things that were happening for the last couple of years. So the technology will continue getting better. By technology, I mean AI itself, but also the guardrail that people like us put around it. The implementation experience is growing. This is particularly where we think our professional services business gives us an edge because our -- we have a very strong professional services organization that customers pick and pay premium for when they need to implement the most important and the trickiest of sort of Appian applications which usually means AI. So we're building an edge on that side as well.

Third is customer internal acceptance of intolerance for the risk. And ultimately, there's a little bit of a FOMO needs to be happening. Customers need to see what other customers are doing. That's what we're trying to do. We're trying to put together a marketing effort that demonstrates the customer what others have been able to do by naming customers, by putting people on billboards, by showing this is what I've used Appian AI and this is the actual tangible value that I generated, which will give people sort of incremental ambition to go after those use cases themselves.

And AI is growing in adoption. We talked about it being a contributor to our strength in the first half of the year. And we expect that to continue, whether there's an inflection point or continued steady buildup, we're okay with either outcome as long as we sort of keep getting more than our fair share, which we believe we are right now.

Sure. Last question on the AI side and we'll shift gears. But I think one of the things -- and it's more product related. I think one of the things that we've heard historically about Appian is you're selling a platform, right? And trying to find like the right use case or like really kind of like narrowing down the message to we can help you solve for X, I think has maybe been a question from customers or partners that we've heard. So does this help simplify the messaging? Or how do you think about like what the real kind of like killer use case application is that Appian can go after and target here?

I understand the sort of the yin and the yang issue, if you will, right? Because if you have a solution that is sort of a hammer for a nail, that's perhaps an easier sort of incremental conversation or the first person that you find or the first deal that you qualify versus a platform, which has a number of use cases, but then where do you begin? I would argue that, that hasn't been a principal impediment to our growth. And the principal impediment to our growth has been more around the execution side.

And if anything, the promise of AI and the variety of sort of genetic use cases that is available today, but it will become more available over time only speaks of the value of the platform because you don't want to create a siloed AI estate the way that you created a traditional siloed IT estate. And so what we're hearing more from customers is that a desire to actually pick a platform, to pick a company to partner with, one, they can bring breadth of functionality as well as professional services expertise. And those things, again, favor us and favor the breadth of what we have to offer versus no, no, no, here's my hammer, let's go find all the nails to nail down.

That makes sense. I'm going to pause here to see if there's any questions in the audience. I'm going to go more into the financial side of the house. We have a mic coming just real quick.

In terms of the importance of metrics, how important is NRR to you? And then do you see a scenario where we see some acceleration over the next year or so?

So NRR is an important metric because it demonstrates our ability to grow inside the existing customers. I have 2 caveats with our own NRR metric. The first one is that we have chosen to do it on a pretty lagged basis in terms of -- and it's blended over multiple time periods and a desire to not -- most of what I'm looking for, not over-extrapolate any single move in any single quarter, we're sort of giving you a number that is more of a historical average of performance as opposed to like the latest and greatest point in the quarter. And that causes some challenges because it's hard to reconcile at times with our revenue growth rate. But nonetheless, NRR and growth in existing customers as a concept is very important. It's not a metric that we drive the business towards, but it is a metric that we care about as an output metric and obviously, it's a metric that we're going to keep reporting.

We don't guide towards it. We used to talk about this 110% to 120% range, which I decided to stop that practice mostly because we don't actually drive the business, so why would we communicate a range or something that we don't actually incentivize anybody to hit. But we expect to continue to see strong expansion from existing customers, but we also have been happy with how the new logos have been performing over the last couple of quarters.

Okay. That's great. I want to ask about the DOGE side, the federal government, just kind of the deal environment. But I guess, what are you seeing out there? What are the kind of the conversations like within the federal side of the house right now?

Yes. So why don't we divide that into the right now and maybe the future? So right now or this year, we've seen disruption at the beginning of the year when the new administration came and DOGE was implemented or established. There was some period of uncertainty that we measured in weeks or a couple of months maybe because it wasn't quite clear what was going on, who still had a job, who's reporting to whom, that whole thing. That's largely subsided. And it feels like we're operating in the business as usual environment right now when it comes to the federal space. We've been happy with our execution in the first half of the year. We talked about our federal business actually growing faster than our overall business, which you wouldn't guess reading the headlines, and I think speaks to the depth of the relationships we have and the quality of sales execution on that side of the house.

We are in the biggest quarter. Our quarters like most people's quarters are back-end loaded. So there's a tremendous amount of back and forth in terms of renewing and upgrading existing deals, new business that is out there in the federal government for us to win. Just given everything that's happened, although it does feel very much like business as usual right now, we'll have to see how the quarter goes up before we can actually say it was business as usual or no, something changed at the very end and the final approval that we thought we had, we didn't get or anything like that. But we have just short of a month to go, but right now, it feels like a normal federal quarter, hopefully closes out that way.

What I will say is if you take a step back, the drive for government efficiency has introduced 2 major positives for us that we believe will be a tailwind to our business regardless of how this particular quarter or year plays out in the federal. The first one is the desire to automate and consolidate legacy technologies. So there's a rallying cry in different pockets of the federal government, automate or die. And it is about processes that have existed for years and decades running on very old legacy systems, sometimes custom-made that the government was to get out of the business of maintaining. And they're looking for platforms on which they can consolidate that, and we have a track record of doing that. I was talking about the military contract that we're consolidating a number of legacy platforms where there's plenty of other opportunities in the fullness of time out there for us to compete, and we like our chances of winning. So if the government is really going to go through a monumental digital transformation effort, we like ourselves as being in the winner's column of that.

And then the second thing is government has made it clear that it wants to deal directly with the software vendors as opposed to through intermediaries. And the reason is simple, the incentives are aligned. Our incentive is to actually get software deployed so that it can generate software revenue for us and savings for the customer, in this case, government. And professional services is a means to the end. So to the extent that we get to talk directly to the government, we have 2 benefits. One is we can better explain the benefits of our product than anybody else. So it gives us a chance to sell directly as opposed to the intermediary. And then secondly is that we get to participate in the execution and the implementation, which will be incrementally benefit to how the product is implemented and therefore the value that we create. So it really does feel like beginnings of a virtuous cycle. But nonetheless, the current spending environment is still to be determined. And obviously, we'll know more here in the next 30 days.

I guess when you thought about the guidance philosophy for 3Q, I guess, in particular, do you take a bit more of a conservative approach given this uncertainty? And I guess, secondly, I think we've heard from some of our colleagues who knew you from Mongo who maybe saw you as a pretty conservative guider in general. But I guess is that how you think about guidance here at Appian? Or is there something maybe different about the Mongo experience that is separate?

Yes. So first, in terms of like how we make the guidance sausage at Appian, in the short to medium term, given our deal cycles, it's pretty straightforward. You have a pipeline, you have stages of that pipeline, you have conversion rates. You have historical conversion rates in the sales forecast, you triangulate those and you create the forecast. Nothing has really changed in that process and nothing really needs to change based on the win rates and the conversions that we've seen in the first half of the year accruing in the federal business. So to the extent that there's incremental conservatism, we didn't feel the need to introduce it into the guidance model.

As from my Mongo experience, the only thing I would say is conservatism always looks easy to call in the 2020 in the hindsight. But I don't think that we are introducing an incremental level of conservatism here either because of my newness or because of my nature when it comes to the Appian guidance.

Okay. That makes sense. We're in the final minute here or so. But last question for you. Just I think we've always got the question around balancing growth and profitability for Appian. And just how do you think about that moving forward? How do you think about top line versus margin?

I think it's exceptionally important to deliver both. I think we've done a remark -- and by the way, this all predates me, so I don't get to take any credit for it, but we've done a remarkable job turning our focus to profitability over the last 2 years. So we're guiding to 7%, 8% EBITDA margin for this year, which is roughly 2,000 basis points improvement over just 2 years. So that speaks to sort of where an Appian decides to focus on something, we get it done and the company should be proud of that.

As we think about going forward, though, we do want to continue showing margin improvements because a business with great unit economics like ours should be able to do that. But we don't want to shortchange the growth because we do see opportunity to grow our sales or because of better execution. We do see incremental AI adoption, so a need or an opportunity to invest on the product side as well as benefit from the go-to-market. We talked about [ DOGE ] being a long-term tailwind and sort of the opportunities that may come from that. So we want to find the balance, but it is important to continue showing measure of margin expansion in addition to revenue growth.

Okay. That's great to hear. I think we're out of time here. So Serge, I want to thank you so much for being here. I want to thank everybody in the room for attending today as well. So thank you so much.

Thank you very much.

Thank you.