Pegasystems Inc. (PEGA) Earnings Call Transcript & Summary

Don, thanks for joining us.

Of Course.

Looking forward to our conversation. There's so much going on in the industry at the moment that it's good to have like a more technical person here to kind of do this.

Maybe to start off, talk a little bit about your background? And then we can go into the presentation.

Excellent. So I've been with Pega for 26 years. And my background originally started with Pega in our support organization and working directly with clients like Citibank, Goldman Sachs on implementing workflow automations, integrating them [indiscernible]. For workflow to really work, it's got to integrate very nicely with the existing apps the clients have. And I took a lot of that implementation work moving into our engineering organization. Got, frankly, pretty bored at sitting and writing code all day. Worked myself back into the client-facing side of the organization, so much of the architect and eventually led our field goal with engineering and architecture function, which eventually just matured itself and [indiscernible] office, spending a lot of my time, probably about 50% of our time in products and 50% of...

Yes. Okay. That's interesting. And then the -- talk a little bit like -- did you say 26 years?

Yes.

Yes. Talk a little bit about the evolution of Pega from a product perspective over those years? 26 years, that was like -- was Windows [indiscernible] already?

Yes. So I joined the company when we were -- we transitioned into kind of thin-client Windows-based kind of development environment [ versus ] thin-client browser-based end-user apps. Pega always had kind of Metadata. And I would say very -- today, we would describe it as very JSON-ish with Pega, we have representing data which when we set, did very well with the HTML, like [indiscernible] we fit very well into HTML and now we fit very well into JSON-based base architectures, which is where we're going. But like over my years, a couple of things have evolved and a couple of things have stayed the same, right? Origin of Pega has always been how we automate workflows and [indiscernible] that have [indiscernible]. So they usually are global. They have multiple business lines which means they need to work in different regulatory environments. They have multiple products, and they want to be able to have not a bunch of siloed systems, but actually one common system way of doing work across all [indiscernible]. That's always been at the core of what we do. And that means automating business rules and decisions as well as the portfolio. Over the last many years, there have been big evolutions. One is in the architecture, so a big shift into the cloud, which was both a business shift, and I'm not going to try to talk about our recurring revenue sort of transformation. Ask [ Peter ] in our finance about that. But that's also been a big shift around moving our client workloads onto Pega Cloud, which is our SaaS offering, our hosted offering. And then the other one, especially over the last 10 to 12 years, has been the impact of AI. So we've always been in what I would call the AI space. We used to call "expert systems" back when it was rules-driven. We acquired a company called Chordiant I think about 14, 15 years ago. And they brought predictive and adaptive analytical AI. And we've been using that with clients to drive massive decisioning scale. So really getting into the front of the client conversation and drive decisions that actually drive revenue, cross-sell, upsell, retention-driven decisions. But now taking that same analytical capability, and putting it back into the process side. So process improvement, suggested with [indiscernible]. And I think that set us up really well right now for this GenAI moment because we've already got the architecture and the folks and the data sets in place to really leverage AI in a pretty effective way.

If you think about it, you guys put out the regional around the autonomous enterprise. Like what -- it sounds like -- can you just comment for us a little bit?

Yes. So I think one of the things that I would do is sometimes it's easier to take the concept of the autonomous and replace it with the idea of self-optimizing. Because I don't think anybody wants to turn a business into a completely [indiscernible]. Leaders, stakeholders, regulators, they still want to be able to make decisions. But what I think they want to be able to do is provide guidance into their technology. I want to optimize customer engagement to drive these products. I want to optimize this process to reduce cost of service. I'm going to optimize this process to maximize throughput and set those goals into [indiscernible], and then have the system through a combination of automation, analytical AI and generative AI, automatically adjust the process to deliver those business results. That's the vision that I think -- when I talk to folks like [indiscernible] and heads of customer experience, that they're really excited about. And I think with GenAI, it's a vision [indiscernible].

And then how does -- if you think about it, then that's almost like it sounds like the holy grail a little bit. That would be good, if you can do that -- like how has Pega kind of well-positioned -- like why do you think you're still well positioned to deliver that?

So I think it's important to understand that clients are [indiscernible] right? And I got to sit in Bill McDermott section. And one of the snaps that he dropped had to do with people's [indiscernible] right? And we actually did a study because we actually have a lot of data about how clients use our software. So we actually know when people [indiscernible]. We actually see it something like [indiscernible] times a day, still sharing across different applications to get it done, right? So there is sort of this first maturity level that organizations need to go through of just automating the simple stuff. And in some cases, it's just removing friction, it's removing points that are disconnected. And by doing that, you put in a very simple kind of case in process structure to catapult that and manage that. But now what you're also doing is you're actually building data history, right? So you're building history, and probably work is getting done. So if you look at an organization like Citi, who process credit cards and [indiscernible] investigations. But in their Pega environments, they have tens of millions [indiscernible] of how that work has been processed in the past. And that is the secret sauce then to drive the analytics and the AI to find, where are the optimization capabilities? How can I use the past to predict what happens the next time we get one of these? And how can I proactively prevent that thing from happening or proactively optimize this process versus before.

Yes. Okay. It makes sense. And then the other part of like extending that a little bit. Like if you look at your flagship software suite, it's Pega Infinity, like now in version '23. Like this Constellation [indiscernible] think about [indiscernible]?

Yes, Constellation is the user experience. So one of the things that I think we've all seen is the desire, even in our internal [indiscernible] to have consumer grade [indiscernible]. So that means I want things that are fast. I want them responsive. And I hope without offending any developers here, if you ask a software developer to build a user experience, you probably aren't going to get a very good one, right? It takes lots of investments from designers and usability thinkers. And what we wanted to build was something that anybody who's designing a process, be that a process developer or even a citizen developer, to use a low code term. If they laid out, hey, I need to collect these 5 pieces of information at this step of the process, they would get a great user experience to do it. And that user experience would be consistent on a web app, a mobile app. You're able to provide APIs to allow them to plug it into their websites. It could plug into their existing sales force system, their existing dynamic system and do all of that seamlessly. So Constellation is an architect that allows you to do a very simple set of controls, gives the information you need to collect at the process, automatically generate a world-class user interface, that both works across both of those channels, but also easily integrates to the other [indiscernible].

Yes. Yes. Okay. That's interesting. And then you mentioned AI quite a few times already. If you think about it, how do you -- like you have already like a good few kind of solution sets out there. How do you think about AI in the context of Pegasystems, and where you kind of benefit into the industry?

Yes. I think that there's -- I think it's important -- when I talk to our clients, like our CIOs and Chief Analytic Officers who are running kind of AI practice we put on the lines. It's important to think and understand sort of the nuance of AI, right? Because it's very easy to -- we love generative AI models, and they're very cool and very powerful, but large language models are actually a subset of the broader capability that we have inside machine learning, right? And if you ask the GPT -- it's not going to [indiscernible]. It actually gets it wrong a lot of the time, right? And even if you did get it right a lot of the time, it's a really expensive calculator, right? So what I want to be able to do is have different types of AI, some of which are large language models, and that's really good at creating things, right, generating information, generating a new workflow, automating the integration mapping between my workflow environment and my four legacy systems. So we've got about 20 or so different capabilities in the product that we've released to do that. But -- and you can think of that kind of as like right brain AI, just creative AI, right, makes stuff. There's also left brain AI. It's AI that is analytical. It makes decisions. It looks at numbers. It predicts what's the churn risk of a customer? It predicts what's the potential value of making this offer to a customer. It predicts how likely is this KYC process I'm running with my commercial banking customer, likely to run into a regulatory issue, right? And the important thing about that analytical AI is, one, it's predictable. It's explainable. I need to be able to tell my client and my regulator how I make decisions. And I don't need 40 billion records between [indiscernible] on a couple of hundred thousand records. So what we want to ensure our clients do is use analytical AI for those kinds of use cases, expect action, process optimization, and then use GenAI to accelerate the way in which we deploy their cases in Pega and connect across the different technology [indiscernible] for a business person to actually drive optimization into their business process without having to be a developer...

So then -- so if I understand you correctly, so then that would be analytical AI is the stuff that we had before already in a way. GenAI was this year, but the analytical AI, you should have worked on for a while already...

The analytical AI has been out there for a while. What I think is interesting is the generative AI boat is lifting all boats. Because now all of the sudden across the pieces, people are starting to be aware of what AI can do. And aware of the value that it can deliver, and the fact that it's real. And so as we talk to clients, they're happy as long as they can drive business value, whether it's analytical AI, generative AI. The most important thing to my client is, does it improve my customer experience? Can I attach it to drive revenue? And can I attach it to improving the efficiency of [indiscernible] get worked?

And then the generative AI like more to help people to interact with the system. Like if I understand you correctly a little bit better, is that like a -- do I think about that as like a copilot idea? Or like how should we do that?

I think you can think of it as a copilot. We've also been using the term autopilot. And the reason I like the term autopilot is, I actually want my GenAI to do stuff. Like I don't want it to just sit there next to me, I want it [indiscernible] tasks. And I think -- thinking that I've used -- if I got on a plane to fly here from Boston last night. And if I had gotten on that plane, and it was just an autopilot and not a human pilot flying that plane, I probably would have turned around and walked off the plane and said maybe I'll take the next flight. But if I got on that exact same plane, and there was a human pilot sitting there, with all the 500 switches and controls that he has inside or she has inside the airplane, and they told me the autopilot was broken, I probably also would've turned around and gotten off that plane and said, no, thank you. I'll take the next flight. So I think that combination of an autopilot that does things automatically to make the human more effective, more efficient and safer, that's what we're going to build into the system with GenAI.

Yes. And where are you on that journey?

So we put out, in 2023, a release that came out last quarter, the first set of capabilities in [indiscernible]. So that is things that like allow user to design a workflow, simply by typing the name of the workflow. We'll actually lay out the workflow, lay out the data model, lay out the various user interfaces that are needed automatically. In fact, you can go to pega.com and check it out as you can build [indiscernible]. We have use cases that have allowed people to talk with [indiscernible]. So instead of writing a report, find out how work is getting done. I can literally ask a question, how many disputes that I processed in Germany in September? It will actually show you that data in real time. That's using GenAI to allow people to interface with the system in a much more natural and easy to understand way.

Yes. And Don, like where are you -- where does this run -- like if you think about it, is that like -- is it in AWS? Like is it in Azure? Or is it -- do you run it yourself? Like how does it work?

So I think where organizations are going to end up is going to be [indiscernible], right? So we're -- we run a Pega Cloud, and a lot of our analytical AI, either on Azure or we've also added [indiscernible] as an [ infrastructure ] option to give our clients a little bit of flexibility in their [indiscernible] place. Our first release in generative AI is using OpenAI from Microsoft Azure, but we've architected it with the expectation that over time, Google just announced Gemini today, right? So there are new of those that are dropping on, that are going to have different levels of efficacy for different use cases. So we want the ability to put the right model in the right use case. We frankly also want the ability to apply the most cost-effective model for the right use case. So GPT-4 costs about 5x as much per token as GPT-3.5, when we need it, we want to be able to use it. And when we don't need it, we want to be able to use a less expensive model while we're prototyping work with open source models as well. So I think model flexibility is going to be a good piece of what the [indiscernible] that.

Yes. Yes. And then I think you [indiscernible] the New York [indiscernible] where you kind of offered a lot of clients. Like what -- are there any examples or any early use cases that you could see [indiscernible]?

Yes. I sat down with the Head of AI and Analytics for one of our really large [indiscernible]. And he said something to me that I felt was [indiscernible] which is, don't come to me with AI use cases. He's sick and tired of vendors coming to him with AI use cases. What he wants is value. Show me that you understand what the problem is in my business. I have a problem that I need to put the right products in front of my customers right at the moment that they're ready. I have the problem that I need to make it so that my customers can get self-service for what might be a really sophisticated banking process that has a lot of regulatory rules, right? Like disputing a transaction with a bank. I want to make it so they can self-service themselves that themselves on [indiscernible]. That's my problem. Now show me how you can solve that problem. And if AI informs that and makes that better and makes it easier for me to achieve that and helps me do it more effectively or minimizes my regulatory risk and it increases my efficiency, but don't show up at my door with an AI use case because I know enough to know that AI by itself isn't the solution to every problem I have.

Yes, yes, yes. And then how do you like -- this last question on that AI topic is as often in our circles, like how do you modify this? Like how does it fit in to your department?

So we've talked about sort of three ways in which we're monetizing this. So one is we're seeing AI as a big driver to the cloud. The Pega Cloud, which is our Saas offering. The technology right now is moving so fast, our clients are realizing they're not going to be able to actually do this in on-premise. They can't update them enough, they can't put in security controls, they can't upscale them and downscale them. They need the cloud. Because our clients -- we're using that as a driver to get the clients in our SaaS offering, that drives a pretty significant ACV increase for us. The second is, it actually drives more use cases. The way we license Pega is primarily not user driven because if we're doing automation right, you actually should need fewer users in order to get things done. We tend to license by what we call cases, which is really the number of [indiscernible] you put through the system. An address change is a case or onboarding a new commercial banking client is a case. Or when Verizon sells a new iPhone [indiscernible], that's a case, right? So we license by the number of cases. So if I can accelerate the way we deploy new workflows in your business, that then leads to more cases coming into Pega, which should be more value to the client in terms of efficiency and also [indiscernible] for us. And then the third piece, which you're going to see more of from us in 2024, is actually monetized [ SKUs ] or actual products tied to specific GenAI capabilities. So some of the optimization that I talked about, right, that would be an upcharge capability that a client would pay for, and that could use additional products that we would sell to our client.

Yes. You mentioned one aspect of AI that would help you nicely, which is like -- it needs to be more in a payer cloud rather than like the client cloud. Like can you talk a little bit about what you've seen over the last few years? And then like that extra incentive from AI to kind of move it [indiscernible]? Where are we on that journey?

Yes. I think the big thing that I've seen in cloud, and I think for a lot of clients, and frankly, I think we're going to see the same thing with AI. Those are maybe the next 2 to 3 years, is it is as much a comfort and risk issue as it is a technology issue. In other words, we've seen the federal government go from no cloud, no cloud, no cloud, no, it's got to be on the cloud. I have seen major global banks go from no cloud, no cloud, no cloud to, oh wait, now it's got to be on the cloud. And I think what happens is, over time, organizations go like cloud itself hasn't fundamentally changed. What's changed is internal perceptions in the organization that actually, these cloud vendors can actually mitigate my risk and my security and my scalability, a lot frankly, better than our own IT department. And the cost-benefit just isn't there for me to try to run it myself. Over the last couple of years, we've seen an acceleration of clients moving to cloud and clients at the scale that 5, 6 years ago, I would have said would never move to cloud, are now moving to cloud. And that, combined with our own advances and leveraging some of the latest cloud technology capability [indiscernible] have allowed us to dramatically improve our margins associated with cloud.

And are you already at the point -- there's the carrot and a stick approach?

Yes.

It doesn't sound like we're at stick yet?

We're not at stick, and we like to tend -- we generally try to stay away from stick because at the end of the day, I don't want my clients associate me with the stick. I want my clients associate me with the carrot, and [indiscernible] value in doing it.

From a tech perspective, is there a different -- like should be -- how much extra tech debt or a extra tech headache do you get from a customer on their cloud versus being clean on your own cloud?

The primary issue that we get is, on our cloud we get to upgrade. We get to make sure that you're on the latest and greatest and taking advantage of the [indiscernible]. When the clients deploy it themselves, it's up to them, and it's sort of get in line with their IT department. So it's harder for our clients to adopt this [indiscernible] that we're giving which means it's harder for us to go in and convey to them the value they're getting from it.

Yes, yes, yes. Okay, yes. And then if you think your aftermarket and the movement in the market it's like it like more and more on a spectrum, there's like the RPA, process mining, et cetera. Like you guys are doing more process mining now as well, like how do you see that all coming together for you guys? And the broader market as well.

I think we viewed these things as coming together for quite some time. I've been in this long enough to remember when people thought that rules engines were a different technology than workflow automation engine, and we got into a lot of trouble with Gartner because we were saying, like you actually can automate workflow without a really good rules engine. And there's no point in making a bunch of decisions that you can't actually do things about. So we've long believed that a lot of these things [indiscernible]. I think we're the first major workflow automation company to buy RPA, through RPA endorsement, because again, we saw that like this is just another form of automation that's valuable if we can bring it together and work [indiscernible]. Same thing with process mining. So we really welcome the emergence of these because at the end of the day, the value to the client is not in the technology itself, it's in, did we improve this process to drive more efficiency? Did we make this user experience better? Could I create -- automate a customer service process and connect it to a self-service channel where you're deflecting calls out of the contact center. And if I have a richer set of technologies that actually allows me to address that problem, I think I've got a better chance of solving the [indiscernible].

Like how do you see -- when you talk to customers and see them, like how close is this market already coming together? Because like we had earlier some of the guys that come from the other subsegments, and there, everyone is still doing fine, and nobody is really talking about like the crossover yet in terms of competition. It feels like, how do you [indiscernible] how close are we that it becomes like very much like one space whereas everyone is still in this [indiscernible]?

I think there's still, frankly, a little bit of confusion inside of the customer base. And there's some degree to which both vendors and the analyst market exploit that confusion, right? I've always found the best conversations happen with the customer, when you're not actually aligned into technology categories. Because technology categories have a tendency to commoditize that reason. And I'd much rather drive a conversation around, what are you trying to achieve? How [indiscernible] right technology gets [indiscernible]. And let me prove that I can do that at the scale of the enterprise. So that you get that economy of scale and you don't end up with a bunch of silos and disconnected environments. And when clients have problems like that, that's where they really turned to us.

Yes. Okay. Last couple of questions more, like, again, on technology, like the pay up process fabric, like can you speak like what it does? And why it's important?

Yes. This is -- this came out of this desire to minimize silos in our clients. So there's a lot of things in a big organization that create silos, right? I sell multiple products? Great, I need an app for each product. That's a silo. I operate in the U.K. and in the EMEA and I operate in Singapore. Well, every single one of those countries has data residency requirements. Silo, right? So there's just natural things in a big organization that [ creates silos ]. However, if I'm a relationship [indiscernible] for Citibank, and I have a global client where work for that client ends up in my Singapore system and in my U.K. system and in my Europe system, it has to physically stay there because data residency says it has to stay there. I don't want to have to go to three different systems to find out what's [indiscernible] with my client, I want to have a holistic view that pulls back together. And we built Process Fabric as a way of accepting the fact that there just are going to be data silos in a big business. But if I have a way to pull those together into a common view, that's when I can drive efficiency and better experiences.

Okay. Perfect. And I think my time is up because -- but Don, that was really helpful to have this conversation, and thanks for joining us. It's really nice to [indiscernible] take technology lens as well from Pega. Thank you.

Thank you.