Appian Corporation ($APPN)

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.