Welcome everybody to day three of the Citi Technology Conference. I'm Steve Enders, as part of the software research team here. With us this morning, we're pleased to have the good folks from Appian here. Bob Kramer, GM and co-founder of Appian, and also Jack Andrews, recently added on the IR side. Very pleased to have both of you here today. Thanks for having us, Steve. Good to be here. Yeah. Maybe just to start, Bob, I don't know how many people know you from Appian. I think, you know, Matt tends to be pretty out there, front, and vocal on it. So, maybe you can just tell us a little bit about yourself and what it is that you do at Appian. Sure. Yeah, so I founded the company in 1999 with Matt and Mike and Mark. I'm the general manager right now, which means I oversee a set of issues related to market operations and also technology and talent as well. Historically, I've run our consulting group, so I'm very familiar with our CS practice, and I also ran our engineering team for a long time, but no longer do that. Okay. And then, Jack, are you gonna intro yourself or- Yeah, to your point, I joined Appian about three months ago. My background's in software development and equity research and investor relations previously. Well, very happy to have you both here. Maybe for those not familiar with Appian, can you just provide a quick overview of the company and what it is that Appian does? Sure. Appian's an enterprise software firm. We are focused on process automation. We help customers, typically large enterprises or the government, design, automate, and optimize their business processes. We sell primarily to the Global 2000 and to large government agencies. We also have a business in the small and medium markets, and we sell across verticals. We have kind of prominence in a number of large verticals, like public sector, financial services, insurance, and life sciences. But we also sell broadly to the broader market into other industries because our platform is, it's quite flexible. I think one of the things that, you know, talking to investors and people in general, maybe struggle with Appian a little bit is fully understanding how customers actually use a platform like Appian. So maybe could you provide some, you know, examples of recent customer wins, or maybe we can dig into some of the key use cases that- Yeah. that customers are using Appian for. Sure, so customers tend to buy Appian for large-scale, complex, mission-critical processes. They're concerned not only about something that functions, but they want a system that's scalable, that's resilient, that's secure. These are all kind of very enterprisey concerns. Novartis is a recent example, a recent win. Novartis is in over a hundred countries. They're a major life sciences firm, pharmaceutical firm, nearly a hundred thousand employees, and they decided that they need to build a kind of global enterprise risk and compliance solution on top of Appian. Did someone over there just turn off their computer? We had some feedback in the- Yeah. So Novartis built a global risk and compliance management system. If you think about their problems, they have ethical rules they have to abide by. They have compliance rules they need to abide by. And those are kind of. They vary by government and by federal jurisdiction and state. And the process of managing all of those rules, understanding in real time, the state of their compliance efforts, is complicated. And so they. How does a big company do that? Well, they have to build a process that allows people to input information on what they're doing, and then that information needs to be vetted centrally through a set of business rules. So it's just a typical Appian process. It's global in nature, spanning, like I said, a hundred different jurisdictions. It's very business rule heavy, it's very process heavy, and it's mission-critical. It's kind of Appian does so many different things. Like, for example, the U.S. Air Force uses Appian for all of their procurement. So you have thousands of people who are buying all kinds of goods and services at the United States Air Force. The entire process of doing that is managed on an Appian platform. If you, you know, rent a truck at Ryder, or you have a truck fixed at Ryder, they've got tens of thousands of trucks. It's a big, complex process. The whole thing is running on Appian. So many of the companies that you interact with on a day-to-day basis, behind the scenes, you often don't see this, but behind the scenes, they're running their business processes on our software. Okay. I guess maybe ask a little bit differently. Like, if a customer or like, you know, let's call the Air Force example, if they weren't using Appian, how would they maybe go about trying to do that process? Like, what is it that Appian maybe went in and built out? Appian, at times, has been known, and we certainly came to the public markets as a low-code vendor, but when companies need to solve business problems like that, like this, they either buy something off the shelf or they write it themselves. And it turns out that buying things off the shelf, while it does work, is often insufficient, particularly for the large kinds of enterprises that we target. They tend to run in their own way, and a traditional enterprise solution would be insufficient. They'd have to end up customizing it. In some cases, they do that. So oftentimes what these companies would do as an alternative to Appian is they would write something in code, and that's slow, it's complex. Lots of those projects fail. It's very expensive. Appian's value proposition early on was, and it still is to some extent, that we allow you to build mission-critical systems in what we call a low-code way, which effectively is a much simplified and faster way to get value from your investments. You can do it with far fewer people, you can do it on a far faster timeline, and your likelihood of success is much higher. So Appian, when we were a smaller firm, and we were a bootstrap firm from the very beginning, we started the company twenty-five years ago. Appian was competing against large companies, and the only way we could really win, we believed, was to quickly demonstrate our value. So for us, everything is about delivering complex systems in the fastest way possible, and I don't think anyone else in the industry thinks about it in quite that way. The kind of merger of those two things, complex enterprise process and very quick time to value, that's what drives us. Got it. All right. No, that's great to hear. Do wanna try to keep this interactive, so if there are questions in the room, we will make sure to get there. But I think before we, you know, before we get into that, I want to touch on a few other topics here. As you mentioned earlier, you were the head of engineering for first decade or so at Appian. Can you maybe just talk about the core technology that you built that underlies the Appian platform? Maybe how has this kind of evolved over the past 25 years now? And what is it that really differentiates Appian versus maybe some of the competitors in the space? Sure. I think what primarily has differentiated Appian for a long time is, again, this focus on time to value. You can take a complex problem and rapidly deploy it, and now there's a lot of different things we do. I'm not gonna get into all of the details, although I'll give you a couple examples. One of the first things that attracted us to the enterprise process space was this idea that you could draw a picture. So you can... You know, if you've ever used Visio or a tool like that, you can actually draw out a process, but it doesn't do anything. It just sits there inert. And what we wanted to be able to do was to allow customers to draw processes that effectively became enterprise software, 'cause then you could understand your process. It was very easy to understand what you were doing, but it's also possible to make it executable. So what has always attracted us is the simplification of the complex. Another recent example, which I think people should know about, is what we call our Data Fabric. Appian has this capability called the Data Fabric, which effectively is a very, very simple way to interact with. In your traditional enterprise software landscape, you have many different disparate data sources. You need to connect to all of these data sources. You need to do that securely, and you need to do that in a scalable way, and that typically requires a lot of time and a lot of manual effort, a lot of human effort to do. Our data fabric essentially makes the that effort almost point and click, and we do it in a very resilient way as well. Insofar as most enterprises have, again, they have a fragmented data landscape, they need a very fast way to connect it all together and join it. That's what Appian does, kind of within the context of our system. We're doing many other interesting things in this area. Of course, we've begun to do a lot more with AI, naturally, but what drives us is this idea that customers can largely simplify the act of doing what has traditionally been a very costly and complex creation process. You mentioned AI in there, so maybe this is a good, good segue into that topic area. Maybe just to start, can you just give an overview of kind of what Appian's strategy is today with AI, where you're kind of baking it into the platform, and how do you kind of view the opportunity with AI? Sure. The first thing to understand about Appian's AI strategy, and we think this is distinctive, is that we are focused on what we call Private AI. So our customers, as I described earlier, tend to be large and very security conscious and data privacy conscious. They want to understand where their data is traveling and who's and what is being done with it. So from the foundational philosophy at Appian is that we do Private AI, which is to say that we keep all data within Appian security perimeter. We do this using technologies embedded through companies like Amazon. We never share your data. We never use your data to train our own models. So the reason we do that as a foundational philosophy is because many of our customers are, as I said, risk averse. They. In fact, many of them are trying to figure out how to govern AI right now, and it's difficult. So they rely on a provider like Appian as the safe and secure provider who ensures that their data is used in the ways that they can expect. That's the foundation. The second layer above that is that we are trying to do two different things. One is we're trying to use AI. This is the obvious case. This is the, you know, this is the case of all development, is just to help make our low-code developers go faster. Anyone who's in development tools business will understand that, and that's all they're talking about. The second, I think, and more interesting aspect of how we're using AI is to improve process. So today, when we think about AI, we tend to think about AI as it's a chatbot I'm speaking to, and and that's useful in the consumer case, but in the enterprise case, that's really not a typical use case. The use case in the enterprise is to embed AI alongside your business process. That's the most important to understand about why Appian is important in this AI space, is that most AI will be deployed inside the enterprise in the context of a business process. What does that mean? What it means is that you have workers who are doing jobs. Let's say that you're processing an insurance claim, as an example. You're gonna get materials that you need to analyze, and you're gonna need to extract data from those documents. You're gonna have to apply business rules to them and make meaning of them so you can decide what to do about the claim, and you need to do this very efficiently. Historically, this might be done by a claims adjuster. It still is, in fact, going to be done that way. But increasingly, some of the tedious aspects of that work are gonna be done using an AI. And AI, for example, is very good at taking an unstructured set of data and turning it into some structured data, which you can then use to apply business rules and processes to. So our theory is that most AI, the greatest utility for AI and where we are, is in automating and expediting business processes. And we're seeing quite good uptake from customers, and they've got a diverse set of use cases. But that's what they're doing with AI right now. That's what we're excited about. You asked one other question at the end, which I forgot, so could you repeat that? Yeah, I guess, maybe the way I'll ask it is, like, what is the business opportunity that you see with AI? Like, how do you actually begin to monetize it? Yeah. I guess, are there like specific capabilities- Sure ... that you embed into Appian, or is it? If you think about it a bit, the way that Appian thinks about process, and we consider ourselves the process company. All business processes are a combination of technical integrations, human beings who are making decisions and doing work, business rules, you have robots involved in the RPA sense, and you have AI. If you imagine all business processes are like that, they have all those component elements, and Appian's objective is to be the best at bringing all of those to bear on solving enterprise process problems. The way in which we monetize it is that customers increasingly use the technology to take those business processes and streamline them through AI. Now, what that does in terms of Appian's licensing model, in our transactional pricing model, it has obvious. I mean, we're charging more for AI, and of course, the transactions that you use, just like anyone who uses any other AI system knows, that when it comes to LLMs, you're charging based on a token basis. If you're using other kinds of AIs, you might be charging on a different kind of transactional basis. So, it's a very real phenomenon. I mean, we see a lot about it in the consumer space right now, but in the business space, you can be assured that almost every company is thinking about how to apply AI within their business processes, and Appian is trying to facilitate that, make it as easy as possible, make it as secure as possible, as governed as possible. Those are the things that Appian does. Okay. In your view, the way to go about it is, it drives more Appian adoption, it drives more use cases, and drives more usage of Appian, and that's how you kind of monetize that AI angle? Absolutely. Okay. All right, that makes sense. Maybe taking the other side of the question, you know, where do you kind of see the risks of what AI could do to Appian? Maybe it's, you know, something in the competitive landscape or just... Yeah, how would you kind of view the impact that AI could potentially have? Yeah, I think people are confused on this point. They think that AI, because it can, in some ways, accelerate certain development tasks, is a threat to Appian. I think it's a misunderstanding of what Appian is. Appian has been known as a low-code provider, because we're very focused on developer productivity, and of course, AI will help our developer productivity as well. But in truth, the real value proposition behind Appian is the capabilities we bring that you don't code. For example, the data fabric. It's not like you're writing a data fabric from scratch with AI. That capability has been invested in over 10 years by a huge team of people with a very unique vision. So I think that AI, on the margins, makes developers more productive right now, but it doesn't write applications. It doesn't have an intention as to write an application. It doesn't design applications. We think that it's most likely that AI will disrupt on the low end or for kind of lightweight, tactical development tasks. But when building systems of the kind that we build, you need precision, you need accuracy, you need all of the non-functional characteristics like scale and security. You're not gonna get those from an AI right now. You know, it's hard to say where AI is going to go, of course, and I don't want to predict that, but I would say that right now, it's probably going to be used primarily for very tactical uses to marginally improve certain developers' productivity. Okay. All right, that makes sense. I think I have about one more question on the AI angle, and then we'll pivot, unless there's some questions in the room on AI. But I think on last earnings call, I think you mentioned that the usage of Appian AI doubled in the past quarter or so. Maybe you can just walk through some of, like, the use cases that customers are leveraging Appian AI for. What it-- Like, how does that actually translate into customer outcomes of how they're using AI? Yeah, so I mean, we have a lot of customers who are doing kind of roughly the following. The kind of door into which data and information comes inside companies is almost always unstructured. It's often emails, it's often loose documents, it could be invoices and these kinds of things. Companies have whole teams of people who have to not only process this stuff, but they have to figure out, you know, the documents aren't always clear. They have to figure out how to route things for approval, or they need to apply business rules to figure out if certain things meet thresholds. Very common use case, and typically, you'd be surprised, but maybe you wouldn't be surprised if you work inside a business, to understand how inefficient they are. A lot of that work is now going to be taken over by AI. That's what many of our customers are doing. They're taking unstructured data, extracting it into a way that they can then use it to run through a business process. And, I mean, this is being used, like I said, in insurance claims; it's being used for invoice processing; it's being used for email processing. There's a variety of different applications, but they all take that same form. And then there are other kind of more obvious use cases that if you use something like a ChatGPT, you're familiar with. Some of our customers use Appian to take large data sets and summarize them down. So one university uses Appian. They maintain cases on their students, and they have caseworkers who just wanna understand quickly a whole history of that student's cases, that student's issues. Rather than reading through reams and reams of paper now, or documents or email trail, they can just synthesize it all or summarize it all in one place and quickly get up to speed. So that's the kind of thing which I think is probably a more common use case, which people are familiar with, but that's, again, done within the context of, in this case, a student case management system for some of them in the course of a process. Okay. All right. Makes sense. I'm gonna pause for a second to see if there's anything in the room here before we before we continue. All right. I'm gonna pivot a little bit towards some of the recent restructuring efforts that that you've gone through and called out on, on the two Q call. Maybe just to start, maybe just run through what the changes were. You know, what's the significance of that, and I guess, what's new kind of moving forward here? Yeah, thanks, Steve. I think that the changes are that what Appian is trying to do is continue to be a growth company, but execute that growth in a more efficient manner. And so, you know, some of the themes that we talked about on the most recent earnings call were just refocusing on larger enterprises, the types of use cases Bob's been discussing here, as well as, you know, refocusing on our key verticals, which would be financial services, insurance, life sciences, and government as well. So really, you know, where has Appian historically done best? Where can we get the greatest return on investment? And that was really the genesis of making those moves. Okay. I guess maybe why is now the right time to go through this, you know, restructuring initiative? Like, what were you maybe seeing in the market that ultimately led to you making this decision now? Well, I think it's a case. Again, this is something we've also talked about on earnings calls, that you know Matt, our CEO, has really immersed himself in the sales and marketing function for the last few quarters now. And basically, he's uncovered you know ideas and ways in which we can grow more efficiently. And so the reason to do something like this now is, I guess, the best analogy. I'm trying to think of a good analogy, but it's if you realize you have an unhealthy habit, you know, the best time to stop and make changes is today, right? It's not waiting for you know six months from now to quit a bad health habit, for example. And so that's really, you know, we identified some ways that we think Appian can operate better, and we're moving on that path. Okay. I guess maybe provide a little bit more, I guess, color on the decision to then reduce the revenue guidance for the year. I guess, are you anticipating any disruptions coming in the second half? Yeah. So what we said at the time of the earnings call was that we were not seeing any disruptions, but nonetheless, we wanted to take a prudent approach. Because we had touched our sales and marketing organization, we thought it was appropriate to, you know, take a more prudent approach to our guidance for the back half of the year. But at the time of the call, we were not seeing any disruptions. Okay, so it's just, I guess, the way to think about it is kind of assuming lower rep productivity or just kind of baking in some potential disruption? I wouldn't say it's lower rep productivity, but it's more just, yes, potential, potential disruption. Okay. All right. Makes sense. And I guess maybe just thinking about what this means for the sales strategy moving forward, what kind of changes here, what are kind of the things that maybe are tactically being augmented, or just how are things kind of changing? I can take that. Yeah. You want to handle that, Bob? I think there's two things we'd point out. First is, we're focusing our direct sales force more directly on the enterprise software space. Appian has historically catered to a lot of different sizes of companies, as I've said earlier, small and medium business as well as the enterprise. We think it's most prudent for us to focus on the enterprise customer case. The second is that we're also beginning to have more of a vertical focus within the firm. While we have sold to broad markets, as I indicated earlier, we also have found that our greatest successes tend to come from companies and financial services and insurance and life sciences. Those are the classic cases of firms that are dealing with significant regulation, many business rules, very stringent uptime requirements. All of these things matter to them, and those are tend to be Appian's value proposition. And, of course, we do a lot as well in the public sector, I should mention that. And finally, we continue to focus, in the context of these verticals, on specific solutions. We've met with some really good success with some of our solutions in the public sector, and we're beginning to see the same in our commercial sector. So those are all good areas for us to focus. Okay. I do wanna touch a little bit more on public sector. I mean, I think you have, you know, GAM, the government acquisition solution within that focuses specifically on that vertical. So maybe just talk a little bit about how that product set evolved, what led you to making a prepackaged solution specifically for that use case, and then I guess I have some follow-ups. Yeah. So, government procurement is complicated. Many different rules, and this again cries out for a kind of complex, or rather a solution to a complex enterprise problem. In many cases, you know, customers lead you to the right solution, and so we had been doing work in the procurement area with a variety of different, primarily defense groups for a long time. Ultimately saw that there was a real opportunity here for Appian to do something kind of more productized, which we call a solution. The Government Acquisition Management Suite is actually a collection of different solutions, things like requirements management, which is how a federal agency would collect requirements, vendor management, which is how they manage vendors. There's a variety of these kinds of things, variety of these different kind of point solutions, which all connect to each other. It's been a big success for us. This was an area of a software market that needed a more mature and innovative solution. The existing solutions were just, frankly, outdated and not modern, and folks loved what we were doing, and we've built on that success now. I alluded earlier to our successes at the U.S. Air Force, but many other large branches have also purchased our procurement solutions. We've also begun to actually move into the data side of this. We have something called ProcureSight, which is now a website that is available to federal agencies, and that's seen really good growth. We're giving it away for free at this moment, but agencies can use AI to kind of search for past purchases and understand buying behavior of other agencies, and we've now tied this into our requirements management product. So there's a lot of opportunities. This is just a singular point market. Like I said, Appian tends to focus on a broad set of markets, but it's an example of where we can go when we go very deep in a given market. It's good. I guess with GAM, maybe, you've talked about customers in the past, and maybe been using it for that use case, but now with the new productized solution, how does this maybe augment the go-to-market to go capture that public sector vertical? And maybe how does this kind of augment deal cycles or help improve things there? The added focus helps the sales force. The added focus helps the marketing team. The added focus also helps the implementation teams. Everything goes faster when you can pinpoint a solution, which is why we like the solution space, which is why Matt has said that we believe we'll become eventually a solutions company. That means the platform will continue to grow. Both our platform and solutions businesses will grow, but when we can identify a market that we can go deep in, then that helps kind of just every aspect of our business run more efficiently. The other nice thing about the solutions business is that it introduces us into accounts where we can then parlay that into additional platform sales, which is the real objective. So our salespeople like it. It makes sales cycles go much faster, the proof points are easier, and they can use it as a wedge into agencies to then grow in other ways. Because, again, the platform can solve many and diverse problems in the government and does. That's how we see it. And I should also point out that this is now, you know, it's something that's now being deployed not just in the federal space, but outside of federal and state and local space as well, where they have very similar challenges. It's a different kind of market that you need to sell into. Of course, it's much more fragmented than the federal market, which is all mostly in D.C., but it still is a market that appreciates these pre-built solutions. Okay. Maybe building off of that, you've talked about, you know, trying to become a, a solutions company and trying to find these, or, or kind of building off of the success of GAM and, and building on that, that solution area. I guess, how do you think about, you know, incremental use cases, or, or what kind of use cases would make sense to eventually support as a, as a standalone solution? With respect to GAMs or just in general? Yeah, just in general. It's tricky. It's tricky to figure out, you know, what exactly is gonna hit a market need at the right time. We're doing more right now in insurance because we think there's a real opportunity to modernize. We have a lot of insurance clients. They're probably some of our heaviest adopters of AI, to be honest, and 'cause they've got very similar problems in terms of just the complexity of the data they have to deal with and their need to extract meaning from that data. So we have an underwriting solution and a claims solution right now, which are our leading edge. And of course, the real benefit to building a solution on Appian for Appian, when we build a solution on Appian, we can invest broadly in capabilities. The capabilities that we can bring to bear on a solution are all of our platform capabilities, which we've invested in over twenty years. So when you compare that to a point solution, small company, an insurtech provider, they have very limited resources. They cannot provide the breadth of capabilities. So we have a very strong AI story simply by virtue of our platform, and now we bring that AI story into the insurance solution. That's a powerful lever for us. In terms of finding new solutions, the key is to figure out patterns in your customer base. We have a lot of customers. We see commonality in patterns, and that's a leading indicator for us that we should go look more carefully at making these investments. But there's no, no, no real kind of secret to it. You just have to do the work and figure out, you know, what the common needs are. Okay, that makes sense. Maybe sticking on the product side a little bit more. You know, I think we've been pretty innovative, come out with a lot of products. Can you maybe just talk about some of the newer product investment areas, maybe why are they relevant to your customers, and how does it maybe support your ability to expand within the customer base? Yeah. Well, and first, we made an acquisition a few years ago of a firm in Berlin that was doing process mining. And processes, I think that it's a good way to think about Appian, is think of us as a process company. That's what we're focused on, enterprise process. And we have the ability to design process and the ability to automate process. This acquisition was intended to add a capability, which we call optimization. So customers want to build process, and then they want to understand how those processes are improving. That's what the heart of process mining is. For Appian, the unique value proposition, we believe, is that you can now build on Appian, and then you can get feedback loop, which shows you how you're performing, and then that can be fed back into your process to make it better. The vast majority of companies out there who build processes don't actually understand how the process actually works. It doesn't work the way the software is set up. It works in other ways, and so you need these kinds of insights to understand what's actually happening. So Process HQ is a new feature that kind of, in our mind, really fills out our suite and reinforces this idea that we're a process company, and you buy Appian to build process and also to make process better. The second thing that we've been doing recently, which I think is very important, you know, there's a lot of talk about no-code and low-code. I consider the no-code market often to be very limited. It's too limiting for our customers to use, and yet Appian sees an opportunity to make some capabilities more available to business users to configure. We have a very interesting approach. We call it case management. A lot of business process problems look a lot like cases. You know, you have something you need to work on. There's a group of people you need to collaborate on, working on it until they get it done or it moves on from one state to another. So case management is a new capability in Appian, and it's extra charge to get it. And this, we believe, is going to help us expand in clients where they may not even want to do additional low-code development, but they know they want to continue to kind of augment their business processes with these cases. So we're excited about that. And then we're doing other stuff and some more technical stuff, but there's an exciting product roadmap we have ahead of us. Of course, the most notable item there probably is artificial intelligence, which we talked about earlier. Okay. All right. That makes sense. I do wanna talk a little bit about pricing and packaging with Appian. I think you have a new advanced tier that's come out. I think there's been some simplification of the packaging over the past couple of quarters. Maybe you can just talk about what's different today with Appian's pricing and packaging and the new tier structure, and maybe just, you know, how this has evolved over the past or over time. I mean, I think that the two things to note are that it's a far simpler model for customers. You know, if you're running a software company over many, many years, you have this kind of accretion problem, where you're adding complexity upon complexity, and you ultimately need to roll that back and get to something simpler that customers and the sales force can understand. So we went to a much simpler model. That's the first thing. It's just simpler to explain to folks. And then the second idea there is that it just has basic segmentation built in. So historically, you bought the Appian Platform, and you got the whole thing. Now we're segmenting it, depending on customer desire. So some of the new features I mentioned, like Process HQ, like case management, like the AI capabilities, all these are only available to certain tiers. So it's gonna, you know, we hope it drives customers to make a bigger investment in Appian. But that's the idea, primarily simplification and segmentation. Okay. We've got about five minutes left here, so I just wanna take a second to pause and see if there's any questions in the room. Maybe shifting gears a little bit, just as we think about. Well, maybe we'll talk a little bit about the AI side again, just on the product side. You know, it does feel like it is a bigger part of the roadmap and kind of the future of Appian and what that looks like. Maybe how do you think about what makes sense for Appian to own and for you to kind of include in the platform versus maybe leveraging some other, you know, other vendors or outside areas? Yeah. We first of all, I think it's important to realize that we have a really strong partnership with Amazon Web Services. We signed a strategic collaboration agreement with them early this year, we announced it, and it's essentially a kind of a joint investment agreement. We're working closely with them on a variety of AI-related initiatives, and we think that what we like about AWS for this, and they're the platform we built the cloud, our cloud offering on as well, just not uncommon, many people have, but I think what's unique is our close relationship to Amazon at this point. We're working with them in a variety of different markets. We like that, you know, the Bedrock product offers us a variety of different kinds of models. We don't wanna be dependent on just one foundational model. Obviously, Appian would never write a foundational model. That's gonna be left to the bigger firms. But we offer our customers that choice as well. So you have a variety of different models you can use within the Amazon Bedrock system, and we give you that ability to do so. So that's not something we'd invest in. We're relying on Amazon to provide a lot of the infrastructure, which, of course, there's capacity issues there as well. So it's good to be able to rely on a technology partner. We also use Amazon for some other AI capabilities, but there's also just a set of things that you have to, as table stakes, build out in terms of capabilities. So infrastructure and foundational models, we're not gonna do, but much, much that lies on top of that, I think we are going to have to, and we have been doing, which that's, that's what I'd say. Okay. All right. That makes sense. Maybe just shifting gears slightly, I do wanna talk about, you know, financials a little bit, and maybe we'll pull Jack in here on some of these questions as well. But, you know, I think I understand. I think we already kind of talked about the revenue guide and kind of what's being accounted for in there, but I think we did see a pretty good raise on the EBITDA side and on the margin trajectory here. So, maybe can we talk a bit about, you know, the path to reaching breakeven and maybe how you're kind of thinking about, you know, kind of the go-forward growth versus profitability debate? Yeah, so we were pleased that we're able to guide to, you know, an Adjusted EBITDA breakeven for this current year, 2024. You know, again, it gets back to we wanna be a growth company. That was sort of the opening message of our earnings call, but we wanna do that in a much more efficient manner. So we think that, you know, we can accomplish both goals, and our guidance is sort of reflecting that. You know, going forward, I think we're still we hope to be able to share some additional commentary on a go-forward how to think about the model moving forward, it hopefully at our next analyst day. So I would say, stay tuned on that front. Okay. And then I guess just in general, as we're thinking about, you know, free cash flow conversion and, you know, I think, you know, EBITDA to free cash flow conversion has maybe been kind of a typical way that it's been talked about or thought about in the past. Maybe how should we be thinking about what that looks like in getting over the that free cash flow generation? I'm not sure exactly what the, how you're thinking about it. I would say structurally, there aren't any sort of anomalies from the financial model that would not lead to sort of the typical free cash flow generation that you would think of over time. You know, structurally, there's nothing unique about our, the way we contract with customers or, you know, the margin structure or anything that's probably atypical for most other software companies that you analyze. Okay. I know we've talked a lot, a lot about the product side, talked a lot about, you know, what things look like here. I guess, as you think about the multiyear opportunity with Appian, I guess, what areas are you kind of most excited about? Where do you kind of see the most potential for the business and to drive, you know, more of that adoption within customers? Yeah, I mean, we're excited about. I mean, we've got a lot of new stuff coming out. We've been in the space a while. We've kind of figured out what it takes to succeed in the large-scale enterprise. We've taken that learning and then built it into our product. We're doing things like we have this new technology coming out called EPEX, which is our new process engine, which is a very much more scalable process engine that will allow customers to be able to do more of what they wanna do, which is effectively straight through automation at very high scale. This is what big financial services customers, insurance customers wanna do, is increasingly they wanna move to postures that are kind of largely automated. You need real high volume capability for that, so this focus we have on the large enterprise as a process company and providing kind of the complete suite of tools necessary to build processes and then optimize them is very exciting for us, and we're also excited about the solution space as well. We really feel like we've figured out what it takes to succeed in one or two solutions markets, and we think there's an ability for us to scale that out and to build more solutions. Those are areas that we're pretty excited about. The AI stuff, I think we're gonna continue to be an innovator. I mean, there's a lot of talk about AI out there, but I think the Appian is really right at the point where we're kind of figuring out how AI meets the enterprise, and meets enterprise process, and I think we're gonna be a leader there as well. Okay. Maybe on that, on that point, you know, I think, I think we've heard from a lot of companies this week about AI, and I think everyone has kind of an AI message. Yeah. How does Appian maybe kind of cut through the noise and really show- Mm-hmm ... the success there? Well- Enable that success for customers. I mean, I think the way we're pitching it to customers is that the first thing you have to get the customer to understand is that most of what they wanna do with AI happens within their business processes. That's an orientation that you have to make. Otherwise, they're thinking, well, you know, I'm just, you know, it might be a plug-in to Microsoft Word and give me a summary of a document. There's some value there, no doubt, but in general, what you have to show them is that the way they think about enterprise automation has to change based on AI. So that's number one, process framing. The second thing is that, AI needs data, and it turns out that one of the most difficult things to do with AI is to get data to an AI in a secure way, that's properly secured to the data set. So we think about LLMs, the entire corpus of an LLM is available to everyone. That's not how enterprise data works at all. Enterprise data works with very strict boundaries. This person can see that, this customer can see that, but not that. And so what you really need is a way to deliver AI in a scalable, or the data in a scalable, secure way to AI. Appian's Data Fabric for us is a key differentiator. So when we look at AI, the way we talk to our customers is we say that AI in the enterprise depends on two things. It depends on your process, it depends on your data, and that's where Appian regards ourselves as very, very strong. So that, plus, again, the foundational private AI message, I think is gonna show customers fundamentally that they can get started working with AI in productive ways, faster than any other alternative. And that's our goal, to be faster than any other alternative. Okay. To value. Okay. No, that sounds great. I think we're running up against time here, but Bob, Jack, I wanna thank you so much for being here and talking about the Appian story, and wanna thank everybody in the room for being here as well. Great. Thank you. Thank you, too. Yeah, thanks.