Pega at Rosenblatt’s Summit: AI Strategy and Future Prospects

On Monday, 18 August 2025, Pegasystems Inc (NASDAQ:PEGA) participated in Rosenblatt’s 5th Annual Technology Summit Part II. The focus was on Pega’s AI strategy, highlighting its potential to transform enterprise systems and boost revenue. Despite macroeconomic challenges, Pega’s performance remains strong, driven by its innovative AI applications.

Key Takeaways

• Pega’s AI strategy emphasizes enterprise transformation and legacy system modernization.
• The Pega Blueprint platform accelerates application development and system updates.
• Pega’s pricing model aligns revenue with automation-driven usage, benefiting from efficiency gains.
• AI is expected to enhance Pega’s intellectual property value and drive higher margin revenue.
• The company supports various AI models, maintaining a model-agnostic approach.

Pega’s Core Business and Market Strategy

• Enterprise Transformation: Pega focuses on transforming legacy systems, workflows, and customer engagement processes.
• Target Markets: Financial services, healthcare, insurance, government, and telecommunications sectors.
• Workflow Processing: Pega’s systems handle millions of workflows annually, with AI decisioning supporting billions of interactions.

AI’s Role and Data Utilization

• Data Strategy: Pega enables clients to use their data for continuous improvement rather than collecting it.
• AI Integration: AI is used to streamline processes and improve customer service engagement.
• Value Proposition: AI reduces human interaction, enhancing efficiency and customer experience.

Technical Debt and Legacy Systems

• Challenges: Technical debt and legacy systems hinder agility and AI implementation.
• Pega’s Solution: Focus on real transformation, moving beyond temporary fixes.
• Pega Blueprint: Aids in accelerating legacy system transformation and modern application development.

Pega Blueprint Capabilities and Adoption

• Rapid Adoption: Blueprint is Pega’s fastest-adopted product.
• Innovative Features: Extracts workflows from legacy systems and creates new applications.
• Customer Success: Vodafone reduced development time significantly using Blueprint.

Revenue and Cost Efficiency

• Monetization Model: Revenue is based on automation rather than user count.
• Cost Management: Competitive AI models help maintain reasonable costs.
• Pricing Strategy: Shift to automation-based pricing aligns with delivered value.

AI Disruption and Differentiation

• Complex Applications: Pega focuses on process-driven applications, differentiating from simpler AI tools.
• Generative AI: Seen as complementary, enhancing Pega’s platform capabilities.

Future Outlook

• Transformation Acceleration: AI is expected to speed up legacy system updates.
• Revenue Growth: Increased automation and cloud adoption will boost transactions and margins.
• Operational Efficiency: Reduced reliance on professional services and lower operating costs anticipated.

For a deeper dive into Pega’s strategies and insights from the conference, refer to the full transcript below.

Full transcript - Rosenblatt’s 5th Annual Technology Summit Part II:

Blair Abernethy, Software Analyst, Rosenblatt Securities: Good morning, everyone, and welcome. It’s Blair Abernethy here, software analyst with Rosenblatt Securities. I’m thrilled to have Pega back with us again to our fifth annual AI conference. Joining us is Don Sherman, CTO of Pega, and we’ve also got Ken Stillwell, is CFO and COO. Been with the company for a number of years.

Welcome, gentlemen.

Don Sherman, CTO, Pega: Nice to see you.

Ken Stillwell, CFO and COO, Pega: Hey, Blair. Thanks for having us again.

Blair Abernethy, Software Analyst, Rosenblatt Securities: Listen. This is a, you know, a product AI focused conversation, so I’ve got a number of questions for Don. I’m gonna pull Ken in a couple of times, more for, understanding, you know, value propositions or or cost structures or the AI implications to the to the revenue model, if you will. We’re not gonna go through the quarter. We just did the quarter.

You guys had a great quarter. You know, years have been ticking along quite nicely, despite all the macro problems. So so, you know, with that, Don, why don’t I just have you, kick it off here and just give us a bit of a high level overview of, of Pega systems right now. So what are your core end markets, and and sort of the problems or challenges that you address for your customers?

Don Sherman, CTO, Pega: So so we really focus on providing a platform for our customers that drives high levels of transformation in their business. And that focuses on transforming some of their legacy. I think we’re going to talk a little bit about technical debt and the impact there. Transforming the workflows that manage their operations and open up, I think, a lot of opportunities for increased efficiency. Transforming how they drive service for their customers, whether that’s traditional contact centers, but increasingly through agentic and self-service kind of channels.

And then transforming the way they engage and market to their customers to move from more traditional kind of, spray and pray marketing solutions to being able to use AI to be highly personalized inside of every conversation they have.

Blair Abernethy, Software Analyst, Rosenblatt Securities: Your platform, is also, it’s it’s fairly generic. I mean, it can be used across a wide range of applications. Right? So maybe just get at at a high level, you know, what are your what are your core end markets?

Don Sherman, CTO, Pega: Yeah. So so our platform is really an AI decisioning and workflow automation platform. Platform. So it or as I like to describe it, it helps organizations make decisions and then get the work done. And we really focus on the enterprise where they have to make these kind of pretty sophisticated decisions and manage work that often crosses multiple organizational silos, works across multiple systems often in the back end.

So we tend to target places like financial services, banking, health care organizations, insurance. We do a lot of work with it with the federal and also state and local government, telecommunications. So those organizations and a lot of the workflows that we do tend to focus around the end customer. So how do you onboard an end customer? How do you service an end customer?

How do you resolve exceptions when things go wrong for an end customer?

Blair Abernethy, Software Analyst, Rosenblatt Securities: And you have some pretty, pretty large, deployments. Right? Like, we’re talking, you know, tens of thousands of, of transactions going through your system.

Don Sherman, CTO, Pega: Yeah. It’s it’s it’s not it’s not uncommon for us to be in systems that are processing tens of millions of what we would call cases or workflows over the course of the year. Or when we’re doing AI decisioning for some of our large customers that use this to figure out how they have the right conversation in every interaction, we’re talking about billions or tens of billions of interactions happening in real time.

Blair Abernethy, Software Analyst, Rosenblatt Securities: Yep. Yep. So, obviously, collecting a lot of useful data can be used for repurposed for for other technologies such as AI. Right?

Don Sherman, CTO, Pega: Yeah. So, I mean, we we we wanna be really careful. We’re not in the business of harvesting our customers’ data. We we are we are in the business of giving them a platform. Right?

But what we want is our clients to be able to then take that data from how they’ve interacted with their customers or how they’ve managed their workflow and use that to drive the kind of continuous improvement loop so that they are continuously getting better and more targeted, for example, at the the marketing conversations that they have or more efficient in how they predict the way that their workflows are gonna end up so that they can actually drive better efficiency and effectiveness into the workflow engine.

Ken Stillwell, CFO and COO, Pega: Yeah. I think I think, Blair, to the kind of the theme you think about the two sides of this one is if you have billions of transactions or, you know, even millions, the level of automation and the level of value that AI can provide to be able to streamline, reduce human interaction to only when necessary is very critical. And then on the other side of that, the amount of information you can gather from the patterns that exist. The actual data is not that important, meaning the name, the transaction, etcetera. That doesn’t really matter.

It’s the type of thing that happened. How often do people ask for address changes? When do they ask for them? Why do they ask them? What are they typically what’s the lag?

What other things, know, happen that might be associated with events with clients? And how can you predict those and then in then reinforce and improve the customer service engagement that you have using AI both in the analysis, but also on the front end to drive a better customer experience. That’s where it’s so powerful, what what AI has given us. Yeah. That’s interesting because it’s not really necessarily, as you said, it’s not a

Blair Abernethy, Software Analyst, Rosenblatt Securities: name, address, telephone number in a database. It it’s actually the processes that have occurred around that to result in this this case happening.

Ken Stillwell, CFO and COO, Pega: Right. How often do people change their address? How often do people add other people to a credit card? How often do you know, these are evasers. It’s important to understand the, you know, the the frequency of the different types of things that you see.

Blair Abernethy, Software Analyst, Rosenblatt Securities: Yeah. Yeah. Interesting. And, you know, Don, just just back to your your last user conference a month and a half, two months ago, you guys were talking more about IT technical debt. And Yeah.

The reliance how the reliance on legacy systems is makes it a challenge to adopt AI. So maybe maybe tell us your perspective there.

Don Sherman, CTO, Pega: Well yeah. I mean and we we have some real data there. We went and surveyed over 500 executives at at enterprise organizations. And what we saw was that 88% of them felt that the technical debt they have prevents them from having the kind of agility and responsiveness they need in their systems. Right?

And I think if we know anything in today’s market in this economy, being able to move fast and respond to changes is pretty darn important. 68% of those executives said point blank that legacy debt and legacy systems was preventing them from implementing and getting the full value out of AI. So as enterprises really think about how they’re gonna integrate the power of whether it’s large language models, whether it’s more traditional classical machine learning into their business so that they can drive more efficiency and they can deliver better customer experience, A prerequisite to that is getting their business logic and their data out of some of these legacy systems. So we think there’s a real huge urgency inside of our client base to do that. And now powered by some of the generative AI tools that we’ve brought to market like Pega Blueprint, we think we have a really unique opportunity to help accelerate our clients right at this moment of need.

Ken Stillwell, CFO and COO, Pega: Blair, one of the things that Pega’s done for decades is this concept that, is is you know, we we use but it’s used in the industry as kind of a wrap and renew, which is we would go in and we would let a legacy system sit as is, and we would create a different UI, manage some of the workflow even across maybe a few legacy systems to try to improve what was otherwise a terrible experience for our clients. What Don’s touching on is that isn’t enough now. Right? These systems cannot be leveraged in AI. They’re they’re many times unsupportable or in very dire need of support.

They’re sitting on, you know, legacy environments whether that be, you know, whether that be custom development, maybe that maybe that’s COBOL systems, maybe that’s mainframe system, like so people are no longer patient. Our clients, at the survey that Don just mentioned, they can’t tolerate this rapid renew. They need to do real transformation. And so that’s that’s what Don’s touching on.

Don Sherman, CTO, Pega: And they can’t be spending money on just keeping the lights on on these systems anymore. They need to be able to free that budget to drive true transformation. And that means, in many cases, completely rethinking how they run their workflows, how they engage with their clients. So moving off of that legacy system both allows them to move faster, but it also opens up the IT budget for them to be able to put it to the things that drive the real transformational value, which is where they need to be.

Blair Abernethy, Software Analyst, Rosenblatt Securities: And so this you’re one of the ways that PEG is doing helping them to to transform is with PEG at Blueprint. Right? So maybe talk a little bit, Don, about, you know, where is Blueprint at today in terms of its capabilities? And and, you know, as you’ve seen it, you know, it has had some pretty good adoption over the last two years. You know, where where where does this thing go?

Like, how how sophisticated does does Blueprint become?

Don Sherman, CTO, Pega: Well, so see, Blueprint has been the fastest adopted product that we’ve brought to market just in terms of the rate at which our clients have been able to come on board and use it. And it’s pretty amazing that for a a product that’s essentially about eighteen months old, how far we’ve been able to push, I think, the capability. So we just introduced, for example, some features in Blueprint that allow a client to literally take a a movie. So imagine you sat down and, you know, you can do screen recording of a laptop. Say you have a mainframe system.

You can sit down and do a screen recording of somebody using that mainframe system, narrating and explaining what they’re doing. Upload that into Blueprint, and Blueprint will extract from that recording the workflows that are in the system. It’ll look at the screens and figure out the data model by looking at just the fields that are on the screen. It’ll figure out what the user outcomes are, what somebody’s trying to drive. And then it’ll use all of that information and combine it with industry best practices that we’ve developed, industry best practices that it can find on the Internet.

We’ve given our partners and our some of the GSI partners the ability to inject some of their best practices into Blueprint as well. And it’ll use those best practices and what it understood of of that mainframe system that you uploaded in the video to design and lay out a whole new application, new workflows, new data models, where the interface points need to be. And I can literally in a couple of minutes be clicking through what my new application experience will be.

Blair Abernethy, Software Analyst, Rosenblatt Securities: A mock up of it. Yeah.

Don Sherman, CTO, Pega: Yeah. Well well but but but but a a fully running mock up with synthetic data, with dashboards. The I mean, the the running mock up even has an agentic chatbot built into it that you can pick up the phone and call and talk to in any language. Right? So, I mean, the scale of what we’ve been able to do when you combine what Pega already had in terms of a really powerful architecture that worked across systems and front ends, as Ken talked about, a time tested and proven structure for managing workflows and decisioning at scale.

And then you use the ability of generative AI to synthesize information about existing legacy systems and best practices and inject it into that structure, what we’re able to deliver and show to our clients in an initial meeting, in an initial conversation, I I personally find pretty mind blowing.

Blair Abernethy, Software Analyst, Rosenblatt Securities: Yeah. Yeah. Interesting. Interesting. And and so you’re, how are the existing customers been adopting this?

So, are they really using this to are you seeing any I don’t As you said, it’s been eighteen months. Are you seeing any impact in terms of, I don’t know, a long standing bank customer or insurance company customer? Are they starting to create more new workflows?

Don Sherman, CTO, Pega: Absolute. I mean, the the the the the story that I keep coming back to is we had one of our long standing telecommunication customers, Vodafone, on stage at our user conference. And Vodafone has adopted a corporate wide mantra where they say no sprint without a print. And a sprint, right, a sprint is just a rush at doing software development. So it’s just a a a time frame, two or three weeks of software development work that you’re gonna do.

And a print is blueprint. So, basically, what they they’ve said is they don’t do anything without doing a blueprint of it first.

Blair Abernethy, Software Analyst, Rosenblatt Securities: Interesting.

Don Sherman, CTO, Pega: And that that that mindset has been driven because they’ve seen real results. You know, they were able to take, an application, an actual enterprise set of workflows that they needed to use from concept to live in forty hours. Right? And that kind of responsiveness, that ability to respond to change and and not just move fast, but actually do something that is really good and impactful and meaningful for the business, that’s what’s driving, you know, other enterprises across our client base to really take this on and inject it into how they think about the new workflows that they’re building and increasingly how they remove some of that legacy debt that is acting a little bit on of an anchor on their ability to drive AI innovation.

Ken Stillwell, CFO and COO, Pega: So, you know, what’s interesting, Blair, is what is what is both amazing and is a challenge to us at Pega at the same time is the way that Blueprint engages with you to actually design your application. It is so advanced and mind blowing in terms of what it can do. It really is. But the challenge is that it is so different than the way organizations are used to building with post it notes and Visio diagrams, etcetera. So there’s a change management process that needs to exist in the industry around leveraging AI tools.

And I and I I think we will get there, but, naturally, people are stubborn and they go back to patterns and they get used to doing things a certain way. And so our that’s why we are putting Blueprint in the hands of our partners, in the hands of the hyperscalers, in the hands of our clients, in the hands of anybody that wants to come to pega.com and see the experience because it really is I mean, it’s very analogous to how ChatGPT is made publicly available to really encourage adoption and and and, you know, enabling people to use new technology. So I think that’s that’s a it’s a challenge for us. Right? Because we wanna try to help people rethink how they’re supporting enterprise applications even though in many ways they don’t they don’t fully want to because they they go back to their old habits.

Right? And so that’s that’s the that’s this great opportunity for us and also the mission that we have at Pega.

Don Sherman, CTO, Pega: Well and and I think the opportunity, as Ken kinda hinted at, right, is it is a big change, but it is it’s much easier to drive a big change when you people can literally put their hands on it and do it themselves. Right? And the fact that anybody can go to pega.com and try out Blueprint. In fact, you know, this might be a weird thing to say during an investor call, but I would encourage anybody on this call who’s really interested in what this thing does to go to pega.com/blueprint and try it out. Because I think not only will it help you better understand some of the things that Pega is doing, I think it I think it provides a really good vision.

Forrester has been talking a lot about what they call AI app generation platforms. And when they go around and they talk about it, they actually include a screenshot of Blueprint as an example of sort of what this future of AI powered app development for the enterprise looks like. And I think it’s a really good way to experience where I believe the future of application development for enterprise software at our clients is going.

Blair Abernethy, Software Analyst, Rosenblatt Securities: You know, I I I wanna ask you, Don, because we were talking about this just before the we got on the call. But, you know, if you a lot of concern. It’s a very rapidly evolving technology. We all know that. And it’s a horizontal technology.

We all know that. So question for is, so how does the Pega platform fit into the new AgenTeq world if this is what we’re where we’re gonna be in the next few years? Does Pega just get, you know, obsolesced and sideswiped by somebody else’s building agentic applications, or do you become a a core that’s used even more, than the past? And so how how do you fit in with the bigger AI world?

Don Sherman, CTO, Pega: So our architecture, I think, has set us up pretty uniquely for this moment. Right? So we’ve demonstrated and, you know, without without anticipating necessary large language models and the rapid rate at which they’ve developed, we’ve been working in the AI space for well over a decade now. So we’ve seen and known what has been coming in terms of machine learning and the ability to take data and drive better predictions, whether it’s into customer next best action and decisioning, whether it’s into process optimization. And as we’ve built out the structure of Pega, we’ve really designed the architecture and the underlying structure that captures the elements of an enterprise application, the workflow steps you have to complete, the decisions you have to make, the places in which it needs to interface with data, much of which will not actually live inside of Pega, but will live in some other system, either a traditional system or increasingly a a cloud native data fabric like a Snowflake or or something from AWS or or Google.

We’ve also built Pega from the ground up to assume that we’re not always gonna be the front end. Right? So Ken mentioned earlier that many of our clients, the front end into a Pega workflow is a Salesforce lightning screen, or it’s a a customer self-service screen that’s sitting on their website. So what that’s allowed us to do is a couple of things. One, it’s allowed us because that structure is so complete and powerful.

It’s allowed us to build a tool like Blueprint that actually uses AI to inject business logic directly into that structure and get you to a running app that that isn’t pretty, but is actually enterprise grade and enterprise ready in minutes. Right? So that’s that’s a unique advantage for us, and that’s why, you know, you’re not seeing other companies and other vendors with tools like Blueprint. But the other

Blair Abernethy, Software Analyst, Rosenblatt Securities: the sorry.

Don Sherman, CTO, Pega: Other thing that’s set up is this is that it has allowed us to plug into this agentic world, both using the agents at design time. Because Blueprint is an agent. Like, under the covers, when I mentioned, you know, that Blueprint is reading that movie and figuring out what Blueprint is doing is it’s actually running a bunch of agents to figure out what’s inside that movie. It’s sending agents off to look out for best practices. Blueprint is an agent.

But the great thing is because those agents run at design time, some of the downsides of large language models, fears about hallucination, the fact that they don’t give you the same answer consistently. When you actually apply it at design time, that kind of creativity and a little bit of unpredictability and out of the box thinking is actually a good thing.

Blair Abernethy, Software Analyst, Rosenblatt Securities: It’s valuable. Yeah.

Don Sherman, CTO, Pega: Yeah. It’s valuable. Right? So we’ve harnessed it for a good thing. And then at runtime, Pega’s got the workflow structure where we can plug in either our agents or somebody else’s agents to ensure that at runtime when you need predictability, when you’re a bank and saying, hey.

We’re investigating fraud. We have to follow these steps. We can’t make it up as we go. We actually have to follow the steps. We’ve got the perfect architecture to help either our agents or somebody else’s agents follow those steps in a predictable and repeatable way.

And that’s gonna be absolutely essential as enterprises try to deploy this stuff at scale.

Ken Stillwell, CFO and COO, Pega: You know, Blair, it’s an interesting an interesting kind of analogous point to or excuse me, a parallel point to what Don’s talking about is if you think about the way the way a model works, a model can become more precise and more powerful if you actually give it proprietary content around the thing that you’re trying to solve. Right? If you just went to a public model, asked it a question about something that it didn’t know because at, you know, at Pega or whatever company you worked at, you actually had information around your process flow, information about your risks and how you’re trying to manage them, the model will be that much more powerful. Parallel to that or analogous to that is imagine if it had the workflow in its in its hands. Imagine if the agent at runtime actually knew how to execute the work, knew what the steps were, knew all the all the pitfalls and the things that that it’s it’s so it’s interesting because if if I said to if I said to an investor, do you think it would be valuable to give the model relevant content to make it more smarter?

I think everyone would say, of course. Why wouldn’t you give it a workflow to actually tell it how to do the work? I mean, so I just think it’s interesting on this concept of, like, disruption or obsolescence or replacement or competition. It’s really it’s very similar to giving it more information so that at runtime, the agent can be that much more efficient, can get the work done exactly the way it needs to get done, and reduce the risk. And it is a big risk of unpredictable results, unpredictable process steps, not being able to know how the work is going to get done is a very, very big issue.

The way to do that is to manage it using the workflow, the Pega workflow.

Don Sherman, CTO, Pega: Yeah.

Blair Abernethy, Software Analyst, Rosenblatt Securities: It’s it’s interesting. I think the the fact that you applying Blueprint at the at the design time is is key. As we’ve seen in other areas in the design software space, that that that unpredictability probabilities actually add value because you end up with a with a a solution that might be outside of your initial you know, what the initial designer was looking for, but then it sparks that that that higher value.

Ken Stillwell, CFO and COO, Pega: And encouraging innovation. It’s it’s it’s actually another level of innovation. Right?

Blair Abernethy, Software Analyst, Rosenblatt Securities: It’s another level of Yeah. And, Ken, I would just well, we would have you, can we talk a little bit about, revenues and costs with respect to AI? How does, Pega monetize, you know, AI, whether it’s it’s it’s it’s, you know, large language models or agentic technologies? And then what how do you you know, what are the costs? How do you absorb them?

What what’s it sort of look like from from your with your CFO hat on?

Ken Stillwell, CFO and COO, Pega: So on the I’ll hit the cost side first, and then I’ll talk about the monetization model for us. So what’s what’s really amazing about all of the models, and quite frankly, the proliferation of models has created a significant amount of efficiency. Even at the scale we’re at now, which is probably nowhere near the scale we’re going to be in three years, there’s a significant amount of efficiency in the cost to deliver and execute the AI models because there’s a lot of them, and they all need to be competitive, and they all need to manage the cost of their models. So that’s actually it’d be it I I don’t know if I’d be able to say that if there were only one model. Right?

So I do think the the economic competitive pressures of multiple models is a huge, leverage point. The second point is the infrastructure build out of the capacity to run all of the AI models. So these large language models, is is helping to keep up with the volume. So I I think from a cost standpoint, you know, we’re not that’s not a concern of ours at all. I think the models actually are running very reasonable in terms of the cost to run them.

The security is actually where a lot of our clients spend a lot more time managing the security because they are not only focused on what the model uses, but the steps and the processes it takes. So Pega helps our clients to manage that risk of, like, how will the model execute. On the on the on the the up on the monetization side, Pega believes that the more that our system performs automation, the more that we should share in that in that cost savings or that revenue share. We do that by calculating a certain amount of usage, so to speak. A case is a unit of measure.

That’s a very common way we connect to usage. So as the models run, the models will drive more automation. The more automation turns into cases, Pega monetizes based on that increased volume of automation. So the cost side, I don’t I believe there’s lots of market forcing pressures to keep it reasonable. We get paid based on activity that the system is operating for our clients to automate and streamline activities and events.

Don Sherman, CTO, Pega: And and just to add to that, I think this was a place where, again, we were well set up for what AI was doing because we had moved away from user based pricing years ago. Right? Because we always felt like if we were driving more automation, the way to capture and monetize that was the amount of automation we were driving, not the amount of users on the system. Because if we were doing our job, the amount of users on the system should be driving more automation. So so for a long time, we’ve built around this amount of automate automation, and that sets us up really well with our clients because now as we drive more automation through it, we have the contractual models in place to support that.

Ken Stillwell, CFO and COO, Pega: And and and I would just say one point, and I I I think that, you know, this happened this this happened before I joined Pega. But Pega was driving so much value with our clients that there were points in time where clients were coming back saying, I don’t need to renew for the same number of users because you’ve helped me reduce the amount of people that are needed to actually execute this workflow, which is what drove us to the point that Don you know, Don kind of alluded to that in his comment saying, we we actually said, well, that’s not a fair relationship. Right? A fair relationship is if we cut your cost by half, we shouldn’t receive half of that. We should actually receive more because the per the system is doing the work.

So that’s a that was something that we really got onto, you know, ten, fifteen years ago in a big way. Now we look smart to have done that, the but reality is we were trying to solve a different problem, which is a problem was we’re solving your problem of efficiency. We need to have a commercial model that makes sense for that. And now it just turns out that, you know, and not having a user based model was really advantageous to the world of AI.

Blair Abernethy, Software Analyst, Rosenblatt Securities: Yeah. Yeah. For sure. You know, I I wanna ask I got actually a question just popped in here from the audience. It’s really around, you know, are are you the third party models that you’re using, what which which ones are you using?

Are you are you building any of your own? Or or or, you know, what sort of the what what sort of the the the needs for you to be able to deliver things like Blueprint?

Ken Stillwell, CFO and COO, Pega: I’ll I’ll start real quick, then Don can give specifics. We are not building our own model. We wanna be very open, Pega. We believe there’s a there’s there’s a lot more value in helping manage the work than it is to try to create a commodity type based model or a specific model for our for our workflows or for our business. Don could talk specifically about all the different models we work with.

Don Sherman, CTO, Pega: Yeah. And and I’m gonna I’m gonna put a little CTO specificity on what what Ken just said, which is we are not building our own large language models.

Blair Abernethy, Software Analyst, Rosenblatt Securities: Yeah.

Don Sherman, CTO, Pega: We actually, for a long time, even prior to ChatGPT, were working with our clients to allow them to build their own machine learning models, their own NLP models, their own predictive models. And those models continue to be very, very useful because some of the things that that large language models are actually not particularly good at are things that are very mathy, like predicting the likelihood of a client to respond to a a particular offer. So we’re continuing to work with our clients to build those models that stay proprietary to them and their unique datasets and their unique client needs. On the large language model side, when this first showed up, we realized very quickly that there was going to be a sort of multi model world. So we designed the architecture of what we call PegaGen AI to begin with to allow us to plug and swap different models in.

Because we saw that as the models were developing, certain models are faster. Certain models are run a little bit slower but give better results. Certain models are better at ingesting docu documents than other models. So behind the scenes with Blueprint, we’re using a combination of some open AI models, GPT four. We’ve been starting to experiment with GPT five.

We’ve also been using, Quad from Anthropic. We’re running that on top of, AWS Bedrock. You know, AWS has been a huge partner for us in a lot of this journey, so we’re using a lot more of of some of their capabilities. And the important thing we found is the ability to swap models in and out as we add new use cases and capability to Blueprint. And as Blueprint has become truly agentic, it’s actually arbitrating a quant a bun across a bunch of different models to find the right model to get the job done.

Ken Stillwell, CFO and COO, Pega: We’re not we’re not gonna bet on which model is gonna win. We know there are gonna be multiple models. We don’t think there are gonna be 50. Maybe there might be less than 10. We just don’t wanna be in the game of having to bet which one is gonna be better for which use case.

So we’ve just been as open as we can in terms of leveraging those models. Like, for example, in our Pega Cloud for government, which we run on AWS, we use Bedrock for that, for example, in the in the model there. But so it’s really just it depends on the situation. But clients can also bring their own too. Right?

I mean, they can actually they can decide that they wanna use a specific model. And to the extent that we can support them on that, we will. But I think to Don’s point, like, we just don’t wanna be betting on the large language model. I think that’s where the question was was pointing to when they said model. I’m assuming they make meant the large language models, which was thank you for the clarification, Don, on my on my answer.

Blair Abernethy, Software Analyst, Rosenblatt Securities: Don, we maybe I don’t wanna revisit something we spoke a little bit earlier on. But just just to to to understand, you know, we we’re all seeing a lot of, scary things for the software industry with AgenTeq AI kinda moving in and taking over. You know, besides Blueprint, so if, you know, if there’s other players out there that have built agents, how do they interact with, you know, your core installations of of Pega Cloud or or or Pega on prem? You know, what what’s what’s the opportunity and the threat for for Pega?

Don Sherman, CTO, Pega: So we announced at, PegWorld at our user conference a capability we call AgenTic Process Fabric. And what that is really about is about using again, Ken talked about the fact that our knowledge is we know the workflows. We know the steps that have to get done in order to deliver meaningful business outcomes in what are often highly regulated business situations for our clients. But we want those workflows to both be accessible to a wide variety of agents. Right?

So we have we we had already had an API that we call the DX or the digital experience API. And that’s what allowed, for example, us to have a Salesforce lightning screen running Pega workflow seamlessly. We quickly extended that to become what we call the agent experience API, the agent x API, so that any agent, whether it’s our agent or somebody else’s, can call into Pega to initiate a workflow. And the workflow can then dynamically, in real time, tell the agent what it needs to do next. So the workflow can literally give instructions to the agent in real time.

And, we’re continuing now to advance that to use things like MCP and a two a, which are emerging sort of standards at both the agent and tool interoperability space. The other thing we realized was gonna be really important is our workflows are really good at assigning work to to people. They’re really good at automating work that maybe used to be assigned to people. Well, now they can assign work to an agent. And the power of that is a lot of the structures that you use when you’re assigning work to people are still really useful when you assign work to an agent.

You wanna make sure you assign it to the right person or the right agent that has the right set of skills. You wanna be able to run quality checks to make sure the agent actually did the work the right way. You wanna be able to have a feedback loop. So if the agent gets something wrong, you can push it back into it. You wanna be able to have an escalation point.

So if the agent can’t figure something out, it has a way of pushing it forward to the next step. We happen to have all of that already in place in the system. Right? So now we’ve just turned it so that it can actually orchestrate agents as well. So to answer your question, we can have peg other agents, Pega and otherwise, calling into Pega workflows, and we can also have the Pega workflow calling out to either Pega agents or third party agents to do individual tasks within the workflow.

But we’re still maintaining that workflow governance to ensure that the necessary steps, the mandatory steps, the things that the best practices that companies have spent oftentimes decades developing and in many ways represent their competitive advantage when compared to competitors, that those get followed in a predictable and consistent and repeatable way.

Blair Abernethy, Software Analyst, Rosenblatt Securities: So it’s not a a threat of replacement necessarily, but a new way to leverage your platform.

Don Sherman, CTO, Pega: We we see what’s happening with AI and AgenTik AI as a really exciting accelerant. Like, both blueprint, the fact that we’re able now to build and design and deliver workflows, in some cases, 50% faster or more than we ever could before, the fact that we’re able to accelerate a lot of our selling process because I I can literally, in the first meeting, be showing a client what is what is essentially a bespoke personalized demo of what Pega would look like in their environment, and I can show it to them in minutes without any engineering effort. And then the fact that we’re able to plug into both our agents and other agents to orchestrate work across the business, we just think it creates a huge new set of opportunities for us and especially the unlocking of the legacy transformation, which I think is gonna be a huge area of investment for enterprises as they look to modernize to keep up with all of the changes that are happening.

Ken Stillwell, CFO and COO, Pega: I I think there I think that there what what maybe freaks people out a little bit, Blair, that there are real disruptive areas that AI is gonna disrupt. Yeah. For example, if I’m using a tool to produce a dashboard that’s simply organizing data that I can go to AI and say, tell me what the insights are, That’s a real disruptive event. Like, that is gonna be very challenging to argue why AI could not actually go take the data and do the exact same view that two financial analysts that, you know, on our team could do. So that what what happens is you see that use case and then you wanna extrapolate that to everything.

But the reality is there is just work that needs to follow a specific process, that needs to be able execute a certain way, whether that’s for internal controls, whether that’s for a compliance issue, the, you know, the EU AI act, that be regulatory standards like like the payment card industry standard for credit card transactions. Like, there’s a lot of work that to protect consumers from certain information following a consistent path. So you can say to a consumer, this is how your loan was origin here’s how the decision was made. Here’s how your transaction was processed. When you have situations like that, it’s very different than just producing a a dashboard versus a a text field to tell you what the answer on the you know, the analysis was in the dashboard.

Dashboard. But I think what’s happening is investors in in many and, you know, in in some cases, the industry gets confused on the differences between these use cases. We don’t do any of the simple use case of, like, let’s just throw some data in rows and columns. Most of what we do, I would have venture to say, materially, all of what we do is done with Pega, not because Pega’s just a simple tool to be able to do open close tickets. It’s because they use us because of the power of the platform.

And that’s exactly the reason why generative AI is complementary and not competitive to that differentiation.

Don Sherman, CTO, Pega: Right. Right.

Blair Abernethy, Software Analyst, Rosenblatt Securities: Don, I wanted to ask you on a couple other areas in the business that the, which you know, it’s fairly small part of the business, but I wanna understand. So what what’s the impact from AI on things like, traditional robotic process automation or screen scraping, if you will? You know, is there any chain is it does that go away eventually, do you think? Or or what what happens there?

Don Sherman, CTO, Pega: So so we’ve never thought hugely amount about sort of RPA as a standalone business. Right? When we when we acquired OpenSpan, which I think what was, like, eight, nine years ago now, the the initial driver of that was because we thought it was the complement to the workflow orchestration we were already doing. And that the workflow orchestration, the getting the work done to the outcome was the real value. RPA gave us the ability to plug in and get data faster or or maybe go to systems that didn’t have nice APIs.

And we’ve continued to really use it in that way. I think over time, more and more of that will more and more of that will begin to erode because of two things. One, in some cases, AI might be able to drive some of it. In other cases, if we’re successful in driving some of this legacy transformation, we’ll be moving our clients off of these old systems where they don’t have APIs and onto modern new cloud architecture where the data is API accessible. And if you have good APIs, you don’t need any of this RPA stuff to begin with.

Right? Yeah. But I think as a stop gap, as I look out over the next three to five years, we’re gonna continue to use the RPA technology as a way of getting at some of those systems and getting at some of that data that we need. But ultimately, our goal is not to sell a bunch of RPA. Our goal is to drive workflow orchestration and decision management at scale for our clients.

And we’ve always thought that RPA was just an accelerated and useful tool in helping us do that.

Ken Stillwell, CFO and COO, Pega: We yeah. We’ve said in and and and, Blair, you you know from conversations you’ve had with me, I was criticized heavily over the last seven or eight years because we didn’t go deeper into screen scraping and desktop automation. And I have said that I we did not believe we thought we thought it was a Band Aid. It’s duct taping your window and shutting the home. It’s not actually fixing the issue.

And I think what’s proving that to be a Band Aid is if you look at all of the RPA companies, what are they all doing now? They’re trying to term what they did using agents. They’re trying to have agents do the RPA. So I think to Don’s point, we never thought it was something that was going to be a long term trend. We thought about it as a break fix short term kind of Band Aid, and I think it was.

And it helped clients advance in places where they couldn’t redo the application at the speed that was needed. But now what you’re seeing is even the vendors themselves are recognizing they’ve gotta make it agentic. Right? The RPA and that there’s just too much breaking. The robots break.

They get confused. Too much manual interaction. So what Don was talking about is we value the robotics that we have inside in the operability of the of the Pega platform. Right? Actually helping and with AI robotics in the platform, it’s all very complementary of what you use when.

Blair Abernethy, Software Analyst, Rosenblatt Securities: Yeah. Yeah. Okay. That’s great. That makes sense.

You know, Don, I want I want to just, talk to you a little bit more about, some of the other innovations you guys have put out there and fielded in the last year or so that are, besides Blueprint, which we’ve talked about, just some of the other, AI driven enhancements that you’ve made to the platform, you know, what what would you call out to say, hey. These are these are the ones that are really resonating with customers?

Don Sherman, CTO, Pega: Well, I think the I think the agentic process fabric that we talked about, the ability to think about how they stitch these agents together and really, again, orchestrating agents against what’s the outcome we’re trying to drive. Right? Because I think the interesting thing and McKinsey just, you know, did this study where they were talking about how I I think they said something about, like, eight in 10 CIOs have said that they’ve started implementing AI, and roughly the same number are still trying to figure out where the value is. Yeah. Right?

And and I think the value comes by looking at the outcomes you’re trying to drive. How do I drive better efficiency for the business? How do I help my customers get their service request driven faster? Agentic Process Fabric gives you the ability to orchestrate your agents against the outcome you wanna get done. And I think clients really appreciate that as a pragmatic way to use this stuff.

You know, there’s also a lot of stuff that we’ve been doing to support, in addition to Blueprint, the acceleration, for example, of bringing apps to live. Like, a big challenge enterprises have is testing. If I’m gonna take an app live, I’ve gotta be able to test it. And then every time I wanna upgrade it or change it, I wanna be able to automate that regression testing so I can make my change quickly. Well, that used to require you to have a whole bunch of developers write a bunch of test cases, which is but one slow, and two developers hate it.

They hate doing that.

Blair Abernethy, Software Analyst, Rosenblatt Securities: Yeah. Yes.

Don Sherman, CTO, Pega: We’ve now embedded tools with AI that will actually generate all the test cases for you so that you get an app that you can deploy faster and you free your developers to work on the things that are really meaningful and the stuff that they actually like doing.

Blair Abernethy, Software Analyst, Rosenblatt Securities: Yeah. Yeah. You know, it’s interesting. It’s interesting. We’re coming up on our time here.

Maybe one more for you, Ken, if I can. Just, you know, with your crystal ball, as you kinda and I’m not asking for guidance. I’m just looking at saying, okay. It looks like the AI that you’re doing is going to, you know, enhance the value of your platform. Does it accelerate your revenue in the next five years?

And number two, does it accelerate at higher margin revenue or lower margin revenue? Because it’s just way more sophisticated, you know, what has to be done to to deliver these systems.

Ken Stillwell, CFO and COO, Pega: So I I if I had to predict, I would predict that, and I think these go hand in hand, that the ability to, get started on a legacy transformation project is going to be faster. I would also predict that the getting it done is going to be faster, which I think that is going to drive faster systems being legacy transform. If those things are all true, we will have more value put into the intellectual property versus the professional services that are needed to implement it. We’ll have less operating cost. We’ll have more people on the cloud.

We’ll have more automation and value, more transactions going through our systems, I think that would all lead in the direction of us having a great opportunity in front of us.

Don Sherman, CTO, Pega: Yeah. Okay.

Blair Abernethy, Software Analyst, Rosenblatt Securities: Great. Great. Great way to summarize and and bring this to a close, Don. Ken, really appreciate it. Great to see you guys, and we’ll we’ll let you drive on.

Thanks, Blair.

Don Sherman, CTO, Pega: Thanks a lot. Bye.

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