PegaWorld 2026: From Legacy to Launch: Scaling AI‑Driven Legacy Transformation with Capgemini CAALM and Pega Agents

But before we get into that, just a bit of introductions, I believe. So I'm joined with my good friend Nicholas. Yeah. Nice to be here with you, Alex. So this is my second time presenting with you in a month. And I'm really looking forward to this session today. I'm the senior director at Capgemini and the CTO for Customer Process Management, where Pega is our strategic partner. So I get to see all the cool stuff. Pega is launching and work with all our brilliant clients implementing Pega. Yes, we've been hanging out a lot on WebEx the last couple of months, which I love, but I'm Alex Exum. I'm on our Platform product marketing team, so I've been focused the last couple of years on how we can use AI and Blueprint and some of our platform capabilities to accelerate legacy transformation projects. Um, and yeah, so before we get into all of that, um, Actually, that's.

Yeah. I just want to give a quick overview of what we will be covering. So a bit of a reality check and level set around the landscape, um, of around Legacy transformation in general and AI adoption and how you can use that to accelerate those projects. Um, Nicholas will get into some of the methodology and tooling and technology around calm and how you can set yourself up for success. Um, we'll really focus on, um, prioritizing the value and focusing in on that so that you can scale these projects because a lot of times, you know, it really depends on why you're doing what you're doing and making sure everyone is on the same page about that. Um, and not just the how and the technology and whatnot, but we will cover that and cover some demos. Um, so from there, we'll get into how you scale for success from your first MLP into a repeatable model and develop a bit of a transformation factory.

And then we'll hear from some client stories and how we kind of make it real. Hopefully we'll have some time for Q&A, but we may use the time. So we are in the Innovation Hub after. If we don't get. Get time for that. But let's see. Start with a bit of a reality check because I know a lot of, you know, a lot of folks have done transformation or modernization projects in the past. Oftentimes there's a lot of baggage around these projects or past experiences where they've increased in scope or they've maybe gone sideways or, you know, you set out to, to decommission an application and then you never actually get to the point where you can, you can do that. And so now we have visuals. So what's the reality check? Alex. Yes. So, you know, a lot of times you're weighed, weighed down by the baggage of these, these past experiences, but you're pressured by some of the expectations and the mounting influence of needing to implement, implement AI and automations in ways that actually bring ROI.

And, you know, from the customer side, provide experiences that are consolidated and consistent across channels. Um, And. Let's see. Um, all of that is important to think about, but you kind of have to avoid this analysis paralysis because the cost of doing nothing kind of increases every day. Um, that you're maintaining these systems on life support, the cost of, you know, estimate around the, IT spend on technical debt is about 40%. Um, and sometimes that's a huge issue, just the dollar amount. Sometimes it's less important for a legacy application around cost, but it's more so the underlying opportunity costs around the inability to innovate, the inability to maintain agility or set yourself up for the future and, um, to, for your ability to kind of spend those dollars on modernizing and setting yourself up for success. So, um, it's really an opportunity cost problem because you're, you're not setting yourself up for the future.

Um, and the, um, you know, we know that AI and digital leaders can, and those folks that are able to transform their legacy systems are able to grow faster than others. And over the last couple of years, this may be manifested more of like a FOMO, fear of missing out. You're adopting and trying to implement AI at any cost. But lately, that's obviously shifted into a much sharper and more important question around how do you prove the value and bring ROI and prove out that you know, that the use case makes sense and that you can scale effectively across your organization? So I know, Nicholas, you work with these folks around AI and transformation projects all the time. So when customers come to you, do these sorts of things ring true? Yeah. I mean, the cost is one thing, but as you, as you said, like everyone wants to do AI.

And as we heard in the keynote this morning, like, what do we want AI? But yes, but now we have tried the pilots for a while and they are cool, but like, how do we actually scale it? And that's the problem we really need to solve. Some of you might recognize this MIT study from last year. It was widely debated when it came out like, okay, 95% of all AI pilots actually don't deliver meaningful ROI. Does that mean AI is bad? No. Not necessarily. It's actually, again, about picking the right use case and understanding the why of of your transformation. Because the 5% that got it right, they knew why they were doing it. And implementing the AI technology on the right use cases define the business case for it before they started and can actually show the ROI at scale. So that's really the problem we want to solve, and that's why we're in this room today.

And also, when we talk about scaling and kind of going beyond the POC or the pilot, like what happens to all of those pilots, that doesn't make it. Yeah, they get scrapped. And that's a huge waste of money, right? So that's why this problem is so important for us to solve together. On how having AI at scale. Um, so we come into now the playbook and the, how you do things. Uh, some of you might have been in, in the session I had last year to get with Matt Healy and my colleague Dinesh, we talked about how to do Legacy transformation and kind of do code analysis and kind of drive that onto the Pega Platform. So you might have recognized this slide if you're here last year, it's our proven methodology to drive Legacy transformation at scale, powered by our technology and strong assets in driving code analysis and business analysis with AI powered tooling.

So it's a six step process, um, that we go through. Typically, we might get a question about one app from a client like, okay, I want to retire this old app. I want to have something more modern, but quickly it starts to become a bigger problem to solve. Like, okay, I don't have only one app. I have actually 300 apps. So okay, what do we do now? So that's when we come into the first step of application landscape assessment, we can scan your application landscape and start to build a roadmap. And also in this step is where we start to build a business case for your transformation. Because if you're not clear on why you're doing things, you're likely to have problems down the line. Once we know where to start and why to start, we do the deep dive on the application analysis. This is where our code analysis tooling can come into place to understand what your legacy is actually doing, and extract a lot of insights so we can generate application summaries, business processes, data mapping and all that, and then use that as a basis for the transformation.

If you want to see more of this, you can come to our booth. Booth 23 in the Innovation Hub, we, as I said, generate a lot of documentation, but we also use this documentation to understand what are the gaps in our transformation story. And this is where we can then start to kind of map out what to beef state. And this is where we will show some assets today on how to drive a genetic user research. So then combining the Legacy understanding with the to-be state and kind of the desired state of our processes, we import that into a beautiful platform called Pega Infinity. And this has proven to be a very good platform to kind of transform from old app into an agentic future at speed. We actually had a study two years ago, I think, where we saw that implementing it on a Pega Platform was eight times faster than custom code on Java, so that's quite significant in speed, having a platform, helping you with governance and and deliberate speed.

In the end, we verify and make sure that also we have change management, user training and that the business is ready for the change. So yeah, that's kind of end to end approach. But yeah, as I said, we will now kind of go deep dive or kind of take you through a case of Lagos transformation step by step following this methodology. So show me the value. How do you build a business case for transformation? Um, so as I said, typically we get an ask. Okay, I want to replace this technology with something else. And okay, it's a starting point. But again, you need to shift that perspective to what's the business value I'm getting out of this transformation. And this is where we rely on our business value framework. So we make sure that we align the transformation to your business strategy. And then we have the four pillars of different types of value you might want to deliver.

So it might be business value. So improving your your margin or having revenue growth. It might be customer values improving the Csat or improving customer loyalty. It might be operational values or automating processes or standardizing them. So everyone works in the same way. So you have consistency for all the customers you're working with. Or it might be something more long term value. So unlocking a genetic AI capabilities, for example, or having data driven decision making. So this can also be a value in itself. So when you define your business case, these are some buckets to pick from. You don't have to pick from all of them, but you have to at least pick one. Because if you don't know what you're doing across these pillars, then then you have problems down the line. As we will see in the bottom there you see kind of a life cycle.

And it's also important that you follow this in those values you have defined. So identify your value. You want to create and define the business case and measure the baseline. This is a very common mistake. People forget to measure the baseline. And then when they want to follow up on the business case, they have nothing to compare it against. So don't forget to measure the baseline. And then in delivery, focus on what you set out to deliver from the first from the outset. So if it's improving margin or automating processes, don't get distracted along the way. Keep course and steer towards the business case you actually define. And then of course it's around value realization. So follow up on your business case and make sure everyone knows how great you were doing. So let's take an example of how you drive this for a Legacy transformation project.

So frame your first project as a proof point of the value you want to deliver, and start with a high value, high visibility use case. Because yeah, we can do it with Pega. So don't be worried and quantify the business impact before kicking off. So for a transformation project, it might be that you want to reduce maintenance costs or increase process efficiency and standardization. Break this down into more tangible targets. So it might be to reduce technical debt or it might be around standardizing processes or increasing efficiency. Okay. And then you define the KPIs you want to measure, measure the baseline of these KPIs and make sure that you know what they are when you start out, focus on it and when you deliver. And then once you have delivered it, follow up on the business case and compare the actuals. Capture any learnings that deviate from your initial business case and move it into the product backlog, because this is not a one off, as we will see today.

This is just a foundation, as your first project will now unlock funding for your future state, because if you're able to save money or improve efficiency in your processes with the new platform, this unlocks a lot of potential. And also you set up the foundation for your continued journey. Um, so moving on into the kind of application analysis and discovery phase. So we have in our toolbox, we have code analysis tooling. Again, come to the booth and we can show it to you. It outputs a lot of different documentation. We also have new capabilities where we're able to run it offline, so no AI provider needed. If you're a bit skeptical around using LMS to run on your code, we will also show you a little bit later around our race for intelligent experience how you can drive a genetic user research. All of these assets screen recordings, BPMN diagrams and whatnot.

You of course move them into Blueprint, which we have already seen this morning, is a really powerful tool to sort of reimagine your application. Blueprint, as you know, is powered by a lot of industry expertise. Pega expertise. And also if you work with Capgemini, you get access to our industry expertise as well. And then as you reimagine, it's important to reimagine your, your processes. So you redesign what's possible with the platform. You move into a future proof, a genetic architecture. So I wanted to show you now a cool tool we call a race for intelligent experience. Or in short. So this is an application that helps you do user research with the power of AI. So in this instance I took the example of making a travel to PegaWorld much smoother because as we all know, it was a lot of preparation to get here, approvals and whatnot.

So I put in the kind of task for my user research tool to, to make it easier for travel planning and approvals, event planning and all that. I'm now able to have the agent actually scan a lot of resources, scan a lot of knowledge about the company I put in, and summarize the user research and the context I'm working in. So this can then serve as the foundation for actually re-imagination step as well. I'm able to generate personas. So in this case it suggests maybe you want to create a persona for travel savvy employee. Maybe it's me myself, but in this instance it's actually a persona called Sofia, and it's now generates this persona. It tells all about the challenges, pains, and wishes that this persona has. And also you can actually chat with the persona itself. So you can actually ask follow up questions and understand the pain points and, and the desires of this virtual persona.

So this is very powerful. You don't have to kind of then engage with and find real users that reflect these personas, but you can actually simulate it before you even start your transformation. You can also generate user journeys for the different personas you define in this tool. So here we get a complete user journey map for Sofia as the travel savvy employee who is excited to go to PegaWorld. And also as a final step, you can also generate samples of user stories that you can use as input for your transformation as well. And what do you do with all of this? So we're moving on to Blueprint. This is a Capgemini version of Blueprint. So that's why we have the Capgemini logo. And what we can do here is actually then upload our assets. So we upload the assets we got from Rick's or Razor intelligent experience.

We upload output from a code analysis tool that run on our existing travel application, and it summarizes everything it found in this. So now we're actually build upon our Legacy, but we also make sure that we reimagine and make sure we, we think about all the new cool stuff we can do. Um, in our new solution. As you see, um, Blueprint is able to understand and start to generate, um, sample processes, which we will soon see also in detail where you can now start to reimagine things and add automations or define details. Blueprint looks a little bit different in this screen recording because I did it two weeks ago, and then one day later there was more capabilities added. So that's how fast Blueprint actually moves. But it's, uh, it's still as powerful in this recording, you define all your data requirements and the business rules.

Um, and also you were able to, to kind of capture any integration requirements. And this is the screen that's the most changed in the latest release, and I complained to Alex, why did you have to change it before PegaWorld? I just finished my screen recording. You get a summary of your application and this is really powerful to actually get the stakeholder agreement of what you did. So what we have seen so far is now that we have run code analysis, we did user research. We also have run through the whole blueprint to actually reimagine what you want to build and set your future for the future, for your success. And of course, you can preview your app at this stage as you saw this morning, but this is just a blueprint. How do we make it real? Alex. Yeah. So obviously there's a lot of great capabilities in Blueprint where you can design and iterate and build on, bring your IT folks and your business folks together to kind of collaborate and align on, on shared goals.

But the next step is really to bring that blueprint into Infinity. And so we're going to look a little bit at some of the Infinity 26 Capabilities that will help you accelerate development of your Pega application. So just to orient a little bit, at first, we, as you saw probably this morning in the keynote, um, our kind of thinking about implementing and incorporating a genetic AI in these three key pillars. So we saw reimagining in Blueprint. And that's sort of our transformation engine at design time. We're going to be building an Infinity studio and incorporating, um, a genetic AI that's kind of purpose built for enterprise application development and contextually aware of your Pega application. So not just a general purpose code generator, um, but something that's a little bit more specific to what you're doing. We know that generating code is not usually the bottleneck to some of these projects.

So it's usually the hardest parts are around understanding legacy systems, um, and aligning stakeholders, designing for compliance and being able to operate reliably at scale. And so those are the sorts of things that we're thinking about and building towards rather than sort of just general purpose code generation. And so that's all in Infinity studio, but really all it's all in support of, of running predictable AI agents and the Agentic Process Fabric that you saw this morning. Um, because, you know, within enterprise operations, it's really about orchestrating humans agent systems in a predictable and reliable way. So I'm going to just jump into, um, Infinity studio here. So, um, so I imported the blueprint that Nicholas gave me right into Infinity studio and just give you a bit of a tour, a walkthrough. So you'll be familiar with this experience if you built any blueprints. We're bringing a lot of that user experience and, um, you know, navigability into Infinity studio.

But importantly, just want to call out that the AI assistant allows you to connect to your enterprise LLM provider of your choice and jump in and start to get an, uh, look at what your application is doing. I think I just asked for an overview of the, of that specific workflow and what contextually relevant information it has. So it gives you an overview. It gives you an overview of your case types and your data structures, etc.. And then at the end, I think it identifies some gaps in your application that you still need to work towards. Because as I think you saw this morning, we demoed the, the endpoint of, of Infinity studio. It gives you this auto generated implementation plan and a bit of a Kanban board for you to work through the final steps of implementing your application, really to accelerate developer productivity and make it as easy as possible to start building as fast as you can.

Um, and again, yeah, you'll be, if you're familiar with Blueprint, you're familiar with a lot of these pages other than the implementation plan. And that's all connected to the AI assistant as well. So you can start to ask like, what next step makes sense, makes sense to get started on and start developing right away. Um, and again, this is sort of just want to reemphasize that the AI assistant here is incorporating Pega best practices. It's also contextually aware of your application such that you're able to plan and design and also implement changes right away. Um, so I think I just scroll through a little bit of this. That's, that's kind of the end of this demo. So I'll move forward, but I wanted to kind of jump into the next slide where, um, just to call out some of the capabilities that make all this possible.

So the vibe coding experience is really great. And it's nice to bring kind of the best of both worlds where you have the value and the acceleration of, of like vibe coding, but also being able to do this safely and effectively, um, is it's important because we're able to do that because we're built on this platform that already out of the box has things like enterprise grade security and compliance, like RBac, audit trails, policy enforcement, all kind of built in. It's not, it's not an afterthought. Performance and scalability that's proven to meet the needs of enterprise scale workloads without custom infrastructure, and then real time monitoring and alerting. That is really critical for maintaining uptime and availability that that's required for, for enterprise scale. So I think, um, yeah, Nicholas, I don't know if you have any input on, uh, some of the examples you've seen in.

I mean, this is really the power of building on a platform like Pega because like, it's easy to underestimate the, the value you get of a platform if you just vibe code your CRM over the weekend. You also have to consider this stuff in the long run if you want to scale it successfully. So having proper security and compliance and governance around it and having someone actually accountable for driving it. So the goal in the end is actually to be able to scale confidently, right? Absolutely. Because we know everyone here knows the enterprise operations are complex. It's it's involving many different people, different teams, disparate systems, different handoffs in between different applications. So it can be really messy and embedding AI into that. You can't really just shoehorn it in and hope for the best. And our answer to that is this predictable agent orchestration layer.

And that's really built around our 40 years of experience and learnings around case management, because case management is kind of, um, the great foundation for AI orchestration because it gives agents structure, context, and governance to be able to, to operate within because agents in a vacuum, if they don't have the right access to certain data, they, you know, you're not getting much out of them. They're not very useful. Um, though on the other hand, you know, if your agent has too much access or too much data access or the wrong type of data access, or their ability to execute is too wide and ungoverned, that can be a real, a real issue. So, um, I just want to call out kind of why we're, how we're able to support this. Um, but I guess before we jump into the demos, I want to just talk a little bit of how we see how we can maintain agility and see that in action when we go from, you know, iterating on an application that might already be built out.

I know in the past we've used recently, up until now, we've been using Blueprint sometimes to build out those workflows and then bring them into to the platform. But what does that look like in the past and that, that process? Yeah. Interesting that you ask. I actually spoke to the Blueprint delivery team and the innovation hub just before. And kind of when you get an ask before to change something, of course there was like, okay, put it into Jira, go drive collaboration sessions, write it down in the document and have Sprint, plannings and whatnot. And then of course you get it wrong. So you have to do it again. Then we got Blueprint, which is of course really powerful for kind of getting agreement on what you want to do together. Um, so, so this was a game changer in kind of getting agreement among the stakeholders.

But then it was always this OK Blueprint was awesome for the first release, but then you got into the development environment and you kind of lost that flexibility and kind of fast iterations that you got with Blueprint. But I think I think you have some cool new stuff to show on this. With, with incorporating the Blueprint AI into Infinity studio, we're able to solve for some of those problems where you might be having to, you know, you're designing a Blueprint externally, and then you're having to rejig the whole thing into allow for existing data structures or data models or existing design patterns and that sort of thing. So being able to incorporate that right into Infinity studio is great because, you know, again, the sort of contextual awareness, um, and, uh, I guess just to outline, we'll get into a demo in just a second, but I wanted to kind of build on the example that you gave, um, and expanded a little bit to an events planning situation where you might be hosting an event and you may have pre-approved folks that are staffing that event like myself or yourself.

Um, and you know, they may not need to go through the standard approval workflow. So I'm going to jump in and add a. On the next demo, I think I go into the AI assistant and just start to add a field in the event planning workflow to take a look at, um, being able to get a list of confirmed event staff for that, that process. And then we are able to do that. And then I jump over to what I want to be able to do is in the travel approval workflow, create some pre pre-approval decision logic to look at that list so that if, if someone is staffing the event, they don't need to go through the standard workflow and you can, you can speed that up. And so I jump over and I just am chatting with the AI assistant because I'm not a Pega developer.

So I'm coding over here. Um, but we're able to add, I think in just a minute that pre-approval decision logic. And that way it speeds us up because they already know I'm coming. They know that I have to be here, and I don't need to go through the standard travel approval workflow. So this is why I'm coding with control. Sorry. Coding with. Coding with control. Even Alex can do it. Even I can do it. Um, and then just to, to add on to that, we've gone through some, some of these agentic steps. Maybe you want to streamline your operations a little bit more, or maybe take a manual process or a step and, you know, create an agent step that, that speeds that up a little bit. So I go through some of the workflows here, and you can see some of those agent steps come through from the, from your Blueprint.

But I just jump in to the expense management, um, stage. And because maybe at the end of this expense process, you want to audit why, you know, certain expenses are not meeting compliance reasons or if there's similarity across different, different issues that pop up, you can create an audit report and have that generate automatically. You don't have to have someone going through and collating those. So just a couple. You know, this is a little bit basic in terms of how to implement it. But when you think about implementing automations at scale or, you know, you're doing manual processes thousands of times a day, it can really make an impact in terms of speed and acceleration. So all right. Yes, the factory, the transformation factory. So a lot of these capabilities, the end result, very useful for being able to go from your first MLP and start to build out a repeatable process for that.

Yeah. So as we saw now we could take a legacy app, go to Blueprint, go to Infinity studio and finish the first MLP in itself. Very cool. But then how do you scale this? And this is where you have the trap, like you have either like you have, okay, I can build it once, but then you don't know how to scale it. Or you have this fear that if I don't get it into the first release, I have to kind of wait forever. So then you kind of have this scope creep problem of scaling it. And also you have the whole governance around it. Like if what if I just let the platform be and let's see what happens. And I tell you, it's going to be interesting for you. So that's why we, when we work with our clients on scaling Legacy transformation, we often come in that you have to have an approach to scale it successfully.

So as we kind of alluded to in up until now, the first MLP is where you prove the value, improve the feasibility of what you do. You make sure you establish a way to measure KPIs and prove your business case. Also, as we saw, we saw an end to end process or multiple end to end processes for event planning in this example. And this is really a value in itself, I think to have an orchestrated process, get something up and running, get a platform established. And also if you at this stage design for reuse from day one, you get a lot of kind of momentum in your transformation because the next process you're going to build, you don't have to set the foundation once again, you actually get a lot of reusable assets. So once you have completed your first MLP, as you do that, you actually start to industrialize and builds your transformation factory foundation.

You in this stage should actually consider having a CEO or design authority established as well, because that enables you to scale out to multiple streams across the enterprise, but still having control and someone who is accountable for making sure you reuse your components and build in the right way and build in the same way that you're supposed to work with, like Blueprint delivered, of course. And as we also saw in the examples, now we built one process and then we quickly wanted to iterate and optimize and improve it. And that's something you also can do confidently if you have the foundations, right, and you have a COE, and also if you have the enablers for the factory. So of course, having an AI powered delivery and a common way of working and the COE established, if you have a way to measure your business value and make sure you follow up on it on each release and hold people accountable for that, for the assumption they made in the beginning, you can leverage Pega insights.

Of course, when you now have a process that is measurable and sort of orchestrate work, you can see where you have your bottlenecks and also improvement potential. Um, you get the reusable components and assets. So if you have a CEO and senior Pega developers, they can make sure that they extract the best of what you already have and make sure it's available for less tech savvy employees. When you sort of do vibe coding like we saw Alex did. So you can drag and drop into and reuse, um, things in your process without creating a lot of maintenance. And of course, we have these accelerators. So we showed some of ours today. If you want to see the code analysis tooling, come to our booth. We showed you Rick's, we showed you Pega Blueprint. But I think Alex, there might be a few things more that helps you scale confidently, right?

Yeah, absolutely. So, um, like you saw earlier this morning, there's really something for everyone. We're kind of building based around maybe on the business user side or people like myself, we're really who were less technical, were able to build a blueprint and iterate on and take yourself from ideas and intent to an application without ever having to like manage a translation layer to actual technology under the hood. Um, and it can accelerate that process and it can bring, bring us together. Um, and then for people that are a bit more experienced, obviously I was vibe coding in Infinity studio, but this is something more for experienced Pega developers who are able to implement those changes and build out your application, but in a way that or operating or implementing a genetic, uh, AI and some of these capabilities to help accelerate that process. Um, and then lastly, um, if you are another developer or you're not working, you're not working within the Pega Platform.

The Infinity and CP toolkit allows you to work from anywhere, be that another IDE or a vibe code coding tool or the CLI or wherever you're working. We're kind of trying to build on this foundation of interoperability and scale. So kind of trying to think about who's using our applications, who's interacting with them and implement these, these capabilities in a way that makes sense for those folks. Um, so I guess, you know, along with that, as you know, once you have teams that are building at this pace, the question then becomes, how do you maintain, um, how do you make sure that what ships is actually safe and it's production ready, it stays performant. Um, and that's where some of the DevSecOps agents come into play and some of the updates we've made there around being able to continuously monitor and optimize and run CI, CD pipelines, ensuring that your hundredth releases is performant and stable and valuable as your first.

And, uh, some of the key capabilities there is auto generating, generating CI, CD pipelines and providing some of that scaffolding work so that before someone has to be interacting with it, um, AI driven risk assessment that flags things that might be risky before you deploy. Intelligent failure diagnostics and diagnostic center that give you kind of move you from. Here's a list of logs to look at when something goes wrong to a an AI assistant interaction where it's like, here's what likely happened. Here's why this matters. Here's a good implementation plan of how to address the problem and address the root cause. So trying to help help in that regard as well. Um, and then lastly too, I think we talked through this morning the AI generated user testing as well. So all of those things kind of trying to help automate some of the toil associated with, with DevSecOps.

These are kind of the best kept secrets of the Platform. Like it's easy to kind of forget about these, but these enable you to actually move at speed. If you're not having test automation, there's going to be some hoards of testers having a very sad day. If you're having scaling multiple teams and not having test automation. So leveraging these agents to have automated tests at scale, and also the deployment pipelines and everything else. You have quality and control of what you deploy across all those streams. So these are my favorite features, but they're also best kept secrets. I guess. Yeah. Just to, to close us out quickly, I know you mentioned being able to end to end traceability, being important, being able to identify bottlenecks and processes and those sorts of things. So just want to quick plug again for for insights, because being able to track and optimize across your operation, across every case, every, every handoff, every AI interaction, being able to audit those things always important and kind of help you, uh, bring more efficiency to your process in general.

Even if you don't automate in the first release, having a process established helps you kind of set the foundation for automation as well and agents that we all want as well. So establish the process in a common way of working is a, a real game changer, actually. Regardless of AI or not. Yeah, absolutely. Yeah. So now we've taken you through the whole journey of how you could go from legacy code base, starting to reimagine what you build. Um, and actually now see also how you can iterate on it, scale it, and also leverage the agents to scale confidently without having all the boring work of writing test scripts. Um, but this is not just theory for us. Actually, we have a lot of clients who are already doing this together. Um, so, um, we have of course, Navy Federal Credit Union, um, they are transforming a lot of Lotus Notes apps.

I think it's in the hundreds to the Pega Platform where they leverage the analysis tooling and Blueprint and the Pega Platform to transform this into a modern Agentic platform. And luckily they are also here at PegaWorld and will speak tomorrow at 215. So make sure you check it out. I think they have some real, real life lessons when it comes to Legacy transformation. So you're not hearing it just from me and Alex. They will actually tell you how the real thing looks like. And then we have, of course, experience of how we do Typekit transformation at scales. Again, we, we started with like, can we do typical transformation and quickly realize they have like 300 typical processes and Tibco is, is not the most modern platform anymore. But we of course could solve this as well and how to kind of move it into Blueprint and transform it.

Same thing with very complex Java app for mission critical app for European insurance company. So so far we haven't come across a single language or a technology we haven't been able to solve how we do Legacy transformation at scale and speed, together with our analysis, tooling and Blueprint and how to move fast. Because in the end, you want to be able to shut down that old system. And of course, make sure that you enable your agentic future. So I'm happy to talk more about this if you want to come up to the stage afterwards. But we do this for real, and it's really powerful to have a platform like Pega to help you move fast and at scale. So to close out, I think I hammered in this message quite a lot, but still lead with the business value, not just a technology approach, because moving from one technology to another with the same process and not understanding why you're doing it, you're going to end up with the same app, but on a new technology.

So make sure you understand why you're doing things and reimagine the way you're working. And similarly, you know, AI is obviously a great accelerator. It's amazing. We love it. But also you still need to be in the driver's seat. So you can't really offload the strategic decisions or scope calls or stakeholder alignment, like all those things obviously still very important. So you're still in the driver's seat in that in that instance. Exactly. And then thinking starting small, of course, but then starting to think big already from the outset, you enable this factory scale approach. And also now you can leverage all the genetic AI features and agents that helps you scale this faster. So think big and how you can scale it with control, leveraging technology to support you doing the, the, the needed but boring work of control. Right. So I think hope you enjoyed the presentation today.

And if you're ready to kind of go now from your Legacy application to launch, visit us in the innovation hub. We have the booth number 23, very near the entrance to the right. Come talk Legacy transformation with us. And I'm happy to kind of have a conversation of how we can take this forward. It was a pleasure doing this with you as well, Alex. Likewise. Always. And there will be a happy hour later at the Innovation Hub. So we'll be there. You can find us at the bar. We'll be around. Yeah. Thank you.