Appian Corporation (APPN) Earnings Call Transcript & Summary

Welcome, everybody, to day three of the Citi Technology Conference. I'm Steve Enders, part of the software research team here, and with us this morning, we're pleased to have the good folks from Appian here. So Bob Kramer, GM and Co-Founder of Appian, and also Jack Andrews, recently added on the IR side. So very pleased to have both of you here today.

Thanks for having us, Steve.

Yes. Maybe just to start, Bob, I don't know how many people know you from Appian. I think 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. Yes. So I founded the company in '99 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 been -- I've run our consulting group, so I'm very familiar with our CS practice, and I've also -- I also ran our engineering team for a long time, but no longer do that.

Okay. And then, Jack, are you going to intro yourself?

Yes. To your point, I joined Appian about 3 months ago. I'm -- my background is 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 is an enterprise software firm. We are focused on process automation. We help customers, typically large enterprises with 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 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 examples of recent customer wins, or maybe we can dig into some of the key use cases that the customers are using Appian for?

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 100 countries, they are a major life sciences firm, pharmaceutical firm, nearly 100,000 employees. And they decided they need to build a kind of a global enterprise risk and compliance solution on top of Appian. 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? They will 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, 100 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 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 asked a little bit differently, like if a customer or like 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 replaced and build out?

Appian at times has been known, and we certainly came to public markets as a local vendor. But when companies need to solve business problems 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. 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, is 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 bootstrapped firm from the very beginning, we started the company 25 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, that kind of merger of those two things, complex enterprise process and very quick time to value, that's what drives us.

All right. No, that's great to hear. I do want to 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 get into that, I wanted to touch on other topics here. As you mentioned earlier, you were the head of engineering for the 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 going to get into all of the details, although I'll give you a couple of 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 -- if you ever used Vizio 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 because 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. And our Data Fabric essentially makes that effort almost point and click, and we do it in a very resilient way as well. So 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 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 with Appian's AI strategy, 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 is -- 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 -- we all keep all data within Appian's security parameter. 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. And 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 case of all development. It's just to help make our low-code developers go faster and anyone who's in development tools business will understand that and that's all they're talking about. The second, I think a 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 chat bot I'm speaking to 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 going to get materials that you need to, to analyze and you're going to need to extract data from those documents, you're going to have to apply business tools to them and make meaning of them, so you can decide what to do about the claim and we 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 aspects of -- some of the tedious aspects of that work are going to be done using an AI. An 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 roles and processes too. So our theory is that most AI -- the greatest utility for AI in 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...

Yes. 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? I guess, are there like specific capabilities that you embed into Appian? Or is it...

If you think about it, the way that Appian thinks about process, and we think -- 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. So 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 and 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 LLM, you're charging based on a token basis, if you're using other kinds of AI, 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. So 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.

Absolutely.

Okay. All right. That makes sense. Maybe taking the other side of the question, where do you kind of see the risks of what AI could do to Appian. Maybe it's something in the competitive landscape or just yes, how would you kind of view the impact that AI could potentially have?

Yes. 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 make 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 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 nonfunctional characteristics like scale and security. You're not going to get those from an AI right now. 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 you mentioned that the usage of Appian AI doubled in the past quarter or so. Maybe can you just walk through some of like the use cases that customers are leveraging Appian AI for? Like how does it actually translate into customer outcomes of how they're using AI?

Yes. So -- I mean, we have a lot of customers who are doing kind of roughly the following. The kind of the door into which data and information comes inside companies is almost always unstructured. It's often e-mails, 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 -- the documents aren't always clear. They have to figure out how to route things for approval or they need to use -- 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 of the business to understand how inefficient they are. A lot of that work is now going to be taken over by AI. I mean, 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 e-mail processing, 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, and they maintain cases on their students. And they had case workers who just want to understand quickly a whole history of that student's cases, that student's issues. Well, rather than reading through reams and reams of paper now or documents or e-mail 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, I think in this case, a student case management system with some of them in the course of the process.

Okay. All right. Makes sense. I'm going to pause for a second to see if there's anything in the room here before we continue. I'm going to pivot a little bit towards some of the recent restructuring efforts that you've gone through and called out on the 2Q call. Maybe just to start, maybe just run through what the changes were, what's the significance of that? And I guess, what's new kind of moving forward here?

Yes. Thanks, Steve. I think the changes of it, what Appian is trying to do is continue to be a growth company but execute that growth in a more efficient manner. And so some of the themes that we talked about on the most recent earnings call, we're just refocusing on larger enterprises, the types of use cases, what Bob has been discussing here as well as refocusing on our key verticals, which would be financial services, insurance, life sciences and government as well. So really, 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 restructuring initiative? Like what were you may be seeing in the market that ultimately led to you making this decision now?

Well, I think it's been -- it's a case -- again, this is something we've also talked about on earnings calls that Matt, our CEO, has really immersed himself in the sales and marketing function for the last few quarters now. And basically, he's uncovered 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, the best time to stop is and make changes is today, right? It's not winning for 6 months from now to quit a bad health habit, for example. And so that's really -- we identified some ways that we think Appian can operate better, and we're moving on that path.

Okay. I guess from -- I guess, maybe provide a little bit more, I guess, color on the decision to then reduce the revenue guide for the year. I guess, are you anticipating any disruptions coming in, in the second half?

Yes. 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 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 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.

You want to handle that, Bob?

I think there's two things we 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 greater successes tend to come from companies and financial services and insurance and life sciences is 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 tend to be Appian's value proposition. Of course, we do a lot as well in the public sector. I should mention that. And then 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 want to touch a little bit more on public sector. I mean I think you have 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.

Yes. So government procurement is complicated, many, many different rules. And this again cries out for a kind of a complex, where there's solution to a complex enterprise problem. In many cases, 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 and vendor management, which is how they manage vendors. So there's a variety of these kinds of things, variety of these different kind of point solutions, which all connect to each other. And it's been a big success for us. This was an area of the 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 success at the U.S. Air Force, but many other large branches have also purchased our procurement solutions. And we've also begun to actually move into the data side of this. We have something called ProcureSite, which is new website that is available to federal agencies and have seen really good growth. We're giving 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 have 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 solutions 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 to and 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 indiverse problems in the government and does, which is how we see it. And I should also point out that this is now -- 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 mostly in D.C., but it still is a market that appreciates these prebuilt solutions.

Okay. Maybe building off of that, you've talked about trying to become a solutions company and trying to find these -- or kind of building off of the success of GAM and building on that solution area. I guess how do you think about incremental use cases or what kind of use cases would make sense to eventually support as a stand-alone solution?

With respect to GAMs or just in general?

Yes, just in general.

Well, tricky. It's tricky to figure out what exactly is going to 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 -- because 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 20 years. So when you compare that to a point solution, a point solution, a small company, an insurtech provider, they have very limited resources. They cannot provide the breadth of capability. 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 of 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 real kind of secret to it. You just have to do the work and figure out what the common needs are.

Okay. That makes sense. Maybe sticking on the product side a little bit more. I think you'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?

Yes. Well, first, we made an acquisition a few years ago of a firm in Berlin that was doing process mining and processes, I think 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 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, 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 have something you need to work on. There's a group of people we need to collaborate on, working on it until they get it done or 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 we're doing other stuff and there's some more technical stuff, but there's an exciting product road map 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 want to talk a little bit about pricing and packaging with Appian. I think you have a new advanced UI 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 how this has evolved over time.

I mean I think the two things to note are that it's a far simpler model for customers. 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 moving 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 desires. So some of the new features, I mentioned like Process HQ, like case management, like the AI capabilities. All of these are only available to certain tiers. So it's going to -- we hope it drives customers to make a bigger investment in Appian, but that's the idea. Primarily simplification is segmentation.

Okay. We've got about 5 minutes left here, so I just want to 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. It does feel like it is a bigger part of the road map 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 vendors or outside areas?

Yes. 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 kind of a joint investment agreement. We're working closely with them on a variety of AI-related initiatives. And we think that -- we like about AWS for this, and they're the platform we built 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 using -- we're working with them in a variety of different markets. We like that the Bedrock product offers us a variety of different kinds of models. We don't want to be depending on just one foundational model. Obviously, Appian would never write a foundational model, that's going to 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 AWS Bedrock system, and we gave you that ability to do so. So that's not something we 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 going to do, but much that lies on top of that, I think we are going to have to and we have been doing, which -- that's what I'd say.

Okay. That makes sense. Maybe just shifting gears slightly. Do you want to talk about financials a little bit and maybe we'll pull Jack in here on some of these questions as well. But 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 the path to reaching breakeven and maybe how you're kind of thinking about kind of the go-forward growth versus profitability debate?

Yes. So we were pleased that we're able to guide to an adjusted EBITDA breakeven for this current year, 2024. Again, it gets back to -- we want to be a growth company, that was sort of the opening message of our earnings call. We want to do that in a much more efficient manner. So we think that we can accomplish both goals and our guidance is sort of reflecting that. Going forward, I think we're still -- we hope to be able to share some additional commentary on how to think about the model going forward, 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 free cash flow conversion and I think EBITDA to free cash flow conversion has maybe been kind of a typical way that has been talked about or thought about in the past. Maybe how should we be thinking about what that looks like in getting over that free cash flow generation?

I'm not sure exactly what the -- how you're thinking about it. I would say structurally, there isn't -- 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. Structurally, there's nothing unique about our -- the way we contract with customers or 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 about the product side, talked a lot about 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 more of that adoption within customers?

Yes. 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, and we've taken that learning and build 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 do more of what they want to do, which is effectively straight through automation at very high scale. This is what big financial services customers, insurance customers want to do is increasingly they want to move to postures that are kind of largely automated. And you need real high volume capabilities 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. We're also just -- we're 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, so those are areas that we're pretty excited about. The AI stuff, I think we're going to continue to be an innovator. I mean there's a lot of talk about AI out there, but I think 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 going to be a leader there as well.

Okay. Maybe on that point, I think we've heard from a lot of companies this week about AI, and I think everyone has kind of an AI message. How does Appian maybe kind of cut through the noise and really show the success there or 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 understand is that most of what they want to do with AI happens within their business processes. That's an orientation that you have to make. Otherwise, they're thinking, well, I'm just -- 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. It'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 and a scalable -- data in a scalable secure way to AI. Appian's Data Fabric for us is a key differentiator. So when we look at AI and 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 the foundational private AI message, I think, is going to 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, and still value.

Okay. That sounds great. I think we're running up against time here, but Bob, Jack, I want to thank you so much for being here and talking about the Appian story and want to thank everybody in the room for being here as well.

Thank you.

Thank you.