Great. Hello everyone. My name is Alexei Gogolev, and welcome to JP Morgan Boston TMC Conference. Today we're delighted to be hosting Pegasystems management team. First of all, Alan Trefler, welcome, Founder and CEO of the company, as well as Ken Stillwell, company CFO. Alan, happy to have you here. First of all, if we could maybe begin with a short overview of the business. Would investors who are new to Pega story, maybe talk about what Pega does and provide maybe a few use cases to better understand the business.
Well, I'll let Ken kick that off. He talks to the investors all the time.
Okay. All right. Pega has been around for quite a while helping, you know, traditionally or historically, enterprises with scale transactions. That isn't exclusively companies that work in the B2C industry, but there's a lot of connection there between B2C business models and a significant amount of volume of work that needs to be automated. That volume of work also tends to be very structured. You know, deterministic workflows is the term that's, you know, more commonly used these days.
We've always, you know, thought about all the power of Pega being to be able to build something once and run it millions of times the same and predictably, and then allow the change to be manageable in terms of the evolution and how we innovate, and how we, how we change the, either the, you know, the work process or the workflow. Because, you know, things do evolve. We've been on a journey over the last 10 years or so where we had a historically a user-based perpetual license model in earlier part of our business model. That has dramatically changed over the last, you know, 5-10 years, where now we have more SaaS or Pega Cloud business. We're all recurring.
We have a volume-based or a usage-based metric as our primary licensing metric. Sometimes we have users and cases. Case is a unit of measure that we think of at Pega. The business has went through a pretty significant transformation over the last few years. We went from a business that was $500 million and largely perpetual to a business that's approaching $2 million and generating a significant amount.
$2 billion.
$2 billion, excuse me. $2 billion, generating a significant amount of free cash flow, as we went through that transition. More recently, over the last couple years, we've launched something called Pega Blueprint, which, if you haven't seen it, you should go to pega.com and take a look at it. You can test it for yourself. It's really our way of leveraging AI and how we can help our clients reimagine or envision the future of what they want to transform in terms of their technology platforms.
Thank you.
Maybe just to touch on AI a little, because it's such a wild and crazy part of the lexicon these days at all moments. We've been heavily involved with AI since 2010. In 2010, we went out and acquired a company that were specialists in statistical AI. That's machine learning. It's the part of AI which I think people are overlooking now some, but is still incredibly valuable. The reason we did that is we had, since our inception, been experts in rules engines and process automation. How do you have processes that make sense according to the various either legislative or business policy or other types of rules?
You could really see that being able to do machine learning off of that and pull that sort of knowledge and learning into the workflows would make a lot of sense. Of course, since 2022, we've done massive changes, candidly to our business model as well as our products, which are reflected in this Blueprint AI technology, which we're really excited about.
Well, that's great segue. maybe we could discuss, Alan. Customers, they either buy, build, or they configure solutions. Where does your low-code Pega solution fit into that landscape? You talked about GenAI. How does that change customers' decisions?
Well, I think GenAI has, you know, massive implications, and I hope not to contribute to the crazy hype that's going on around agentics and AI these days. We're pretty neck-deep in it, and it's having a pretty substantial shift in our business. If you think about one of our customers, they typically have hundreds, thousands of workflows that run and describe their business. They're, think of that as being the way they want to have standard operating procedures. You know, if they have to pass an ISO regulation or they have to pass something, you actually have to even document those and show those to people sometimes. And that's a, you know, that's candidly a good thing.
What we've been able to do with the Blueprint AI technology is use the full power of these frontier models to be able to apply AI in anger, as it were, at the time that a customer is re-envisioning or reimagining how they want to do part of their business. This might be moving an older system to a newer environment. This might be taking six or seven systems that come together as a result of a merger or acquisition and blending them together. What our Blueprint AI does is it lets you do that and lets you rethink it in ways that leverage our literally four decades of best practices. We know a lot about workflows.
We've been able to incorporate those in a language model technology that as part of our Blueprint AI that really can bring that to our customers when they want to think about a new way to either engage with their customers or engage in their back office or do those sorts of operations. What it really is instead of the traditional model where people would either, you know, buy an off-the-shelf software product, figure out how to wire it up to, you know, various backends, figure out how to hook it into how their users or their customers might use it, this lets you instead literally create something that's yours, that is specific to your backend systems as a customer and specific to how you want to engage it.
That's fully agentic in architecture, which means if there are things that can be automated, they just naturally automated. By using all this tremendous AI power at design time, and then at runtime when people are actually using it, being very selective about the use of AI, we can do a couple of really interesting and unique things. One, the systems this creates are remarkably better and really allow customers to do you can take an old system and convert it, and you get something that doesn't look like the old system. It looks like something that is the way you would want it to work. More interestingly, by applying AI at runtime selectively, we don't actually charge our customers for tokens.
They're able All this stuff that's finally caught up, you know, in the last four weeks, where people are suddenly worried about token expense. Of course, they should be worried about token expense. $1.5 trillion is being spent on data centers, and somebody's got to pay for it. We are so, I would say, smart in the way that this uses the AI and uses tokens that we really get the best of all the worlds, and I think this is gonna be pretty exciting is as the confusion abates in this market, there's a lot of confusion, but as it abates, and it will, I think the companies that have done the right things structurally are gonna be the ones who are gonna be able to take the lead for themselves and for their customers.
Excellent, Alan. Could we maybe talk more about Pega's competitors and how is it changing? Obviously, all these hyperscalers and SaaS platforms, they're expanding their agent strategies. How has this changed recently?
Well, we're very involved with both AWS and with the Google GCP lines because we run, we don't maintain our own data centers anywhere. When somebody uses Pega Cloud, which the majority of our business now is, and the you know, significant majority of any new business runs on Pega Cloud, it's actually something the hyperscalers are really quite happy about. We have, for example, AWS is gonna be with us at PegaWorld, and I think talking about how they're working with us and doing things. They have agents that actually know how to translate legacy systems, and we integrate with those so that we're in a position where, for example, AWS Transform, which is a agentic capability they have to read like old COBOL code.
We will take that as one of our inputs when we are reimagining how one of their systems could work. Of course, we take lots of other things as inputs too. We'll take user manuals. We'll take what's on the customer's website. We'll take all sorts of pretty much anything you throw at it. Blueprint will digest as part of making a new solution. I think we're pretty well aligned with the hyperscalers there. The noise that's in the market where people worry that all software is dead, I think rumors of the death of software are greatly exaggerated, particularly if you pay attention to the different types of software that are out there. There's just a lot of, certainly, there are companies that are dead, there's a lot of different types of software.
I think we're awesomely positioned to give the customers the ability to manage the workflows they need to manage to run their business, but to do it in a way that is both innovative and deterministic, and I think that's a big advantage over a lot of the other things I see out there.
I just add one thing. I think, leaving, say, agentic engineering or code writing out, and you think about the competitive landscape. I think there's a pretty deep misunderstanding around companies that don't really do workflow. Essentially custom build process connections to be able to try to replicate that activity, t hey don't really have the structures, v ery hard to manage change, i t's very hard to get repeatability, i t's really just another version of custom code. Then there are workflow providers that are purpose-built for really straightforward, simple use cases like a ticket management system. I think many times all of these companies get thrown into the same bucket in terms of workflow.
What we pride ourselves is that our clients get value in building enterprise scale. When I say enterprise, I mean repeatability at scale. Millions, hundreds of millions of repeatable things that happen that need to be done in a very deterministic way. I think lots of companies say that they will help that workflow, but they really are just another version of custom code or more simple use cases.
Deterministic does not mean not varying. I mean, it's really important, and this is where the AI helps a lot in making it so you can have decisions and subtlety and other capabilities that will do the right thing for every customer. They'll do it in a way that, you know, at first you don't reimagine it literally every time a customer shows up. They'll do it in a way that ensures that you're treating two customers the same way, which in lots of businesses is considered a good thing as opposed to reinventing something for each one of them.
That's a very important point, Alan. With generative AI disruption point solutions and obviously low-end workflow companies, how does Pega's architecture and product suite position you against very big competitors like Salesforce?
You know, it's interesting. I think Salesforce, Microsoft, ServiceNow, they've all done something I think is wonderful. They've all created something called a prompt studio , they want people to go in and, quote, create agents, which are pretty easy to create, though whether to do one that really does exactly what you want to do is so easy, not so much. They create agents by putting English language prompts in. You know, you listen to ServiceNow is a great company. You listen to Bill McDermott talk about he's gonna have a AI control tower to let the thousands or tens of thousands of prompt-driven agents that you create magically be able to interoperate, find each other, call each other, and do something wonderful.
I personally think that approach, which is the general approach for some of those other companies I mentioned, and candidly is the preferred approach for people like Claude and OpenAI because it generates staggering numbers of tokens to have this happen. That preferred approach, I think, is madness. In Pega, if you want to have an agentic process, you create a workflow, and that workflow, if doable by a person only, will go to that person, or if a step is doable by a person, will go to that person. Our super-agent is able to read any workflow in any Pega system and execute it. What do you get? You get the power of the AI creativity, but at design time.
At runtime, you're using the AI very narrowly for language translation and for selecting the correct workflow, but you're executing a workflow where you could actually tell somebody what it was gonna do before it did it. We happen to think, you know, it's not candidly whether we're, you know, 10% ahead or 20% ahead or whatever. We're structurally doing this the right way, and I think that will over time come out.
It's interesting. There are examples or there are analogies to this workflow discussion that Alan's having that we would never pause to think that doing it using agents or AI. For example, imagine if you just scrapped your ERP system and what you said was, "I'm just gonna ask an agent what the GL transaction should be every single time." I mean, it's. We would look at that and think, like, there's no possible way. You wouldn't even. How would you even audit that? What would you get as a result?
There are situations that are very parallel to this discussion that Alan's having around, I need to make sure that when I do a dispute on a banking transaction, that follows a very deterministic process, largely because that process may be regulated. There may be some variation, as Alan said, because something unique comes up or there's an exception, or there would be variation with a human interacting with that workflow. That's where AI is really powerful to augment the workflow. I do think the concept is very well understood in other kind of analogous use cases. The workflow is done because you need to repeat it at scale.
You know, the thing that I think people miss because sometimes they think of workflow systems as being just kind of ticket tracking systems. When you build a workflow right, and particularly when you use like Blueprint AI to design your workflows, it can put a lot of discrimination, a lot of selectivity into the branches of those workflows. It can know different types of customers, know different types of risk profiles, know different types of steps, but it can show them to you. It's not that it's figuring it out every single time you go through. You know, I find that, and I think customers will find that increasingly comforting as they hear, you know, people, you know, Julie this morning was talking about an AI control plane.
You know, I think we're gonna go through a phase where this stuff has to go out of control before people realize. Well, it just should never have been done that way. I mean, there were other ways to do it. We represent the alternative way to do it.
Makes a lot of sense, Alan. As enterprises move from experimentation to ROI-driven implementations, can you talk about some of the themes you just highlighted, the AI governance and explainability for your customers, this approach that you have towards this strategy?
I think it's a difficult time for customers because, you know, the hype cycle's been in full, you know, gear for a while. There are lots of things being said. The whole, you know, declaration of war with the entire software industry, where the AI models have basically said the entire, you know, software target addressable market really belongs to them. You know, I think customers are trying to figure out some of this stuff and, you know, we're moving into operationalizing more, but the company is still trying to figure out the key elements of their architecture, I think. I don't know how much longer that will go on for, but it's gonna go on for a bit. The reality is the world is moving very, very fast.
These things are coming at them very, very fast, and they are contradictory. You know, this is an opportunity, I think, for people to make decisions that will be very consequential, either good or bad. I think some of them are being a little cautious because of that. Having said that, a month ago, we were token maxing, right? You guys know what token maxing? Burn as many tokens as you can. You know, Jensen Huang gets up and says, "If you're not spending a quarter of a million a year per engineer on tokens, you're just wasting your life." I think people have reconsidered that, and I think that reconsideration is gonna come fast.
It's perfect because candidly, we figured out in 2023 when we started that this free lunch was not gonna exist at some point in time. I'm really pleased that I think between now and the end of the year, we'll be out of free lunch territory. People will realize that somebody was expecting to pay for all these data centers.
I think You know, to add on to what Alan was saying, Inside of a month, I would say in the last two to four weeks, almost every client conversation that I'm in has some part of the conversation that says, hold on, how many tokens are you going to charge me for? Right? I think the clients are becoming much more sensitive because of their own experience. Naturally, there are the very highly publicized, like, I ran out of my token budget by February. You will see more and more of those use cases. Clients themselves are starting to understand there's variable pricing models. I think that everyone kind of knew. I think we all knew in our, you know, in our stomach that, like, it was going to come. It just came really fast, right?
The fact that the realization of, this is a very costly way to do repeat scalable transactions. It's not efficient at all, and it's not the right way. It's the reason why we went away from writing custom code 40 years ago, because it's incredibly hard to manage. Writing, by the way, writing the code is, 20% of the problem, right? The 80% of the problem is operating it, scaling it, changing it, man. That problem becomes much, much harder with the proliferation. That's why, that's why we existed. That's why Pega, we existed to try to give our clients an alternative to that. I do think the token thing is not something that's gonna go away. I just think the conversations are gonna continue to escalate around how do we use AI in the right way?
That's not a point to say don't use AI. It's a point to say use it like anything else when it is appropriate, not use it just for everything indiscriminately.
Thank you, Ken, and fascinating conversation. I'm sure people in the room have a few questions and, you know, if you have a question, please raise your hand. I wanted to just to clarify, how do you see enterprise technology changing around these ideas that you just mentioned, the Center-Out thinking, and where should Pega fit into that future?
I think the future will involve, in any enterprise of any complexity, any $1 billion+ company, there will be no single solution. Part of what it will take to make an enterprise successful is to be able to have a collection of technologies, able to interoperate in sort of a sensible way. Being able to use AI to create a fabric, as it were, that enables you to run processes across these technologies is exactly what we do and what we've had to do. I think that it's incumbent on us to first get customers to realize that there will be applications in the future because it's very convenient to build your, like, customer onboarding system as a system or your lending system as a system or your customer service system as a system.
They will be applications, the way you will access them will no longer be by coming in the morning, logging on to a computer, logging on to one, two, three, 20 applications and figuring it out. The applications need to be able to reveal themselves so that you might access any application in your environment through a chat. By the way, that chat is also gonna come from multiple companies. It's not I know everyone's aspiring to be like the, you know, the portal that you use. I don't think customers are gonna go from that. The different applications and chats will have to interoperate in our world at the workflow level. At what is the piece of work that they're trying to do or what is the piece of data that they're willing to reveal and bring back.
This fabric idea and this idea of the multiplicity of applications is absolutely centered to use the term Center-Out. The idea is build your applications not around the GUI at all, because GUIs are changing, if not always, optional, and not around the specific back ends. You really want to define the processes that make your business a business, and that center needs to be accessible by people, by customers directly where it makes sense, by agents to do those different pieces of work, and that is exactly what the Pega Center-Out architecture is and what we've been promoting and working on for a lot of years. The experience that we have doing that gives us a big advantage.
I think I'll add one. I would say this maybe would fall into the category of a prediction more than a know. I do think there's a level, there's a parallel to a utility that is a parallel to the model providers. I think they're just like you manage, it's an energy source, right? The AI models are an energy source. I think there is an analogous example of we need to figure out how to manage those in the right way. We don't wanna leave lights on when no one's in a room. We don't wanna unnecessary use an energy source.
We wanna optimize it and use it as a in exactly when needed, and almost build in that efficiency into how we use it. That's where we think we are very aligned with leverage AI exactly when it should be leveraged. Don't leverage AI. It's a very inefficient use of energy or of power when not used in the right way. I draw the parallel to, like, a utility or a source of energy because I think it is very, and m y prediction is in 10 years we will view AI models as an energy source. They're literally physical centers building processing capacity.
Amazing. Anyone has a question? I think I see you, `hand over there.
Thank you. You guys have laid out a very strong case for how your position in heritage sets you up well. I wonder if you can kind of address the success of Palantir's approach, especially in light of the comments they've been making recently about the death of software.
Yeah, no self-interest there. What's ironic is, I think what Palantir does is as subject to attack by the LLMs as anything. I guarantee you Claude can build a brilliant, you know, ontology, you know, which is how they talk about what they do. Look, Palantir is a custom development shop, and they have a lot of, you know, they have capable people, and they build extremely intricate and sophisticated custom software. I mean, that's how I see them. They say that, they, you know, have, quote, products, but the end of the day, I think they gotta send a whole slew of those forward deployed engineers in to actually get anything to work. There is a role for custom software.
Whether everybody is going to customize their software on top of a, you know, Palantir ontology, you know, I don't think in my lifetime.
Thank you
Thank you. Thanks for being here, and I hope to see you guys at PegaWorld in June. Quick question.
Thanks for the plug.
I've got two questions, actually. One was, you guys were discussing the competitive landscape earlier, especially against larger competitors. I'm more curious to hear about how you guys are fending off smaller, say, AI, younger competitors, who seem to be gaining traction in the space. That's question number one. Question number two is, you mentioned that customers, your customers are having a difficult time trying to figure out AI. By this time next year, how far along do you think customers will be along their AI workflow and orchestration paths?
That's I'll answer the second question first. I think they'll be pretty far along. A year is a very long time at the pace that things have been going. You know, new truths are being revealed every month. You know, tokens is the truth for this month. You know, there'll be a new truth coming out. You know, customers are smart. They'll figure it out, and they'll get to a good place. In terms of the smaller competitors, you know, the amount of capital and the amount of noise flooding in this space is mind-blowing. I had a traumatic experience a month ago. I was speaking at an AI conference, and I drove down 101 in San Francisco, and there's billboard after billboard advertising AI.
I had this enormous dotcom flashback that hit. Look, there's a lot of craziness and insanity going on. I do think at the end of the day, making some of the critical architectural decisions will differentiate the companies that are successful from the companies that aren't. That's true for vendors like us, and I actually think that's very much true for companies like our customers. Eventually we'll see some of this froth subside a little bit, but we may have to get through the next round of IPOs, you know, before that sort of calms down. That's why next year is probably the right time.
I think you're hearing a lot. The token maxing token cost is just one element of it. I think clients, you know, we use, listen, we are big users of AI across every part of all of our functions in the organization. You know, ROI is elusive, right? Like, when you use an agent to get an extra hour out of your time, what do you do with that time? Like, what is , and it's not obvious in every case where there is a real ROI. There are some where it is obvious, right? If you can do self-service deflection and get away from people on a call to talk to somebody, it doesn't negate the fact that you need the system to actually manage that work.
There are use cases that, I think, are more visible in terms of the ROI. A lot of the experimentation that's going on right now, I think will channel into the very targeted use cases where there's ROI, and I just don't think companies will be able to experiment forever because there's a lot of spending being done where companies, you know, ask companies that use it. It's very hard to demonstrate the actual value that they're getting for the organization. I think that will fix. I think one thing to add on new entrants, there's a, there's a tremendous moat, as you might call it, that we have knowing workflow at the level that we understand it. The use cases, the vertical use cases, the scale, the variability, the knowledge we have is not publicly available, right?
This is things that we know by working with our clients for decades. I think that is a pretty significant barrier to entry when you're trying to do things where you want to be best in class and you want to connect the relevancy to the business problem. I think that's not the only thing we need to do, but that is a helpful, you know, moat in terms of new entrants.
Got it. Thank you.
Great. Well, if we don't have any more questions in the room, I wanted to ask about the Blueprint. We've talked a lot about it already, but considering that the SIs and hyperscalers, they're using a lot of the partner-branded Blueprint and embedding their own IP on it, how's this motion scaling, and what role will partners play in the go-to-market strategy over time?
This is all pretty new, so just to explain what it is. If you go to pega.com Blueprint and you create a Blueprint, which is a pretty interesting thing to do. We're glad to help you with a demo, you know, you can even do it yourself. The system really will walk you through the reimagination of a business process. Whether you decide it's a business process of onboarding a new customer or if you want to go into the llama rental business or something else that, you know, you get to see. It's pretty amazing what Blueprint can do when it does something that we have no idea about, but it just understands concepts like what it takes to run a business and workflows and other things. It will actually do some pretty amazing things.
We decided that we wanted to recruit our partners to be a part of this go-to-market for their own good, for their own benefit, not for Pega's benefit. We added a capability, which really has only gone into the system as of the beginning of this year, that enables a partner to, when a staff member of that partner, like for example, when somebody from Cognizant logs on to Blueprint, they sign on with their Cognizant credentials, and the top of the Blueprint screen talks about Cognizant. Cognizant has a vector database that we've given them that we can't see into, where they can put their best practices into that vector database.
When Blueprint runs in that context, it is going to create a Cognizant-influenced, Cognizant-empowered blueprint that they can go talk to the customer about, hey, because, you know, Cognizant's IP is in here, this will do a better job of reimagining your legacy system or bringing these two different things together, et cetera, than if you had, you know, done it just with the, with the Pega elements. By the way, the other player in this party is the customer themselves. They get to upload, in the context of Blueprint, their IP, their business objectives, their business plan, things from their website.
All of that is part of this distillation into a Blueprint that can be specific to that customer's backend systems, to the actual interfaces of the backend, or can be specific to the way they talk or some of the language that they use in them. This partner-powered Blueprint, we don't, you know, yet know how it's gonna turn out because we're really hitting the pedal hard coming into PegaWorld next month. I think it's a pretty exciting opportunity, and the fact that we had so many partners, big companies, want to sign up to create and put their own IP in, we think is a good early signal for what can happen.
We're going outside the Pega practices in the system integrators to the actual end sellers that are selling legacy transformation broadly across the industry. These are not people that necessarily know Pega, which is a market that we've never touched before in terms of the visibility of it.
We need to work it. We need to market to them, and we need to get them excited about it because they're not, as Ken said, the, you know, the couple of 1,000 people. They're the tens of thousands of people or hundreds of thousands of people in some cases that are outside of ever having known Pega.
Alan, Ken, thank you very much for this very insightful conversation. Appreciate it.
Thanks, Alexei.
Thank you.