PegaWorld iNspire 2024: Pega 101: An Overview of Pega's Vision and Technologies

All right. What's up guys. How's it going? We're getting the thumbs up for the from the crew. So we're going to take it away. And welcome. Thanks for joining for Pega 101. Uh, although I see some folks in the crowd who have been working with Pega for longer than me. So I don't know what you're doing here.

You can be up here if you want. Come on up at any point. Um, but I feel like during the keynotes you almost got like a, a 401 course, you know, some really transformational stuff, some really cool capabilities. Um, and in this session, we're going to kind of, you know, take it down to how does all this fit in and come together? And where did Pega come from? Where are we now and where are we going? Um, so really excited to do that. And for those of you who I've yet to meet Matt Healy, I get to help out with product strategy for Pega's platform. So I get to think about all things development with low code, and then the capabilities that we have in terms of AI and automation to help solve business challenges.

And pleased to be joined by the one and only Hawke. I'm Stephanie Hawkins, otherwise known as Hawke. Um, I am on the Platform product marketing team, so working on all kinds of cool things with Process Mining, RPA Process Fabric. I've been at Pega for about three years now, and I'm super excited to be here today to just talk about everything that Pega can do. And um, I do want to say though, before we get started, you know, I started about three years ago and I really feel like I never got the full backstory on Pega's history. So, Matt, you've been here a lot longer than me. Um, can you kind of get us up to speed on the kinds of problems Pega was solving back at the very beginning? Yeah. I can.

But despite the wrinkles, I was not around for Peggy's founding. But I've learned a little bit about its history over the years, and sort of what sort of problems we were looking to, to solve at the beginning that have informed over 40 years of innovation. So Pega was was born in the 80s. Crazy long ago. I wasn't alive. I can't even confirm if my parents were alive. I'm not sure. You know, it was a time of peace, love, rock and roll and mainframe transitions. So moving work out of file cabinets and into these digital records, these databases and Pega actually started in the payment exception space, where the world's largest banks, Citibank and Bank of America in particular, had moved all of their payment exceptions from, you know, these manual processes into databases, which is great.

They had these digital records, but they didn't yet have the workflows and the processes digitized around those records to assist employees in getting their work done and to start to drive some automation. So Pega introduced workflow capabilities to help streamline and track the payment exception process, all integrating with the mainframe systems. So it allowed Bank of America, Citibank, and then building on from there to, to really bring management and digitization to the payment exception process, enabling them with things like SLA routing, reporting, tracking, auditing, all these critical capabilities for an important process like payment exceptions. And then soon after the founding in 1983, we sort of had a realization, not we, but maybe the people who were there at that time. That payment exception isn't the only type of process that is going to benefit from SLA routing. Management automation, workflow processing. It really is applicable across the business. And this is where the idea of a platform was born. So the ability for business and it to come together to take horizontal capabilities that can be applied to any process and to build it specific into the ways that their business is going to benefit from them.

So that's that's sort of the birth birth of Pega. Okay. All right. That's a good history lesson. Thank you very much. Thank you. And so, you know, as Matt mentioned, we've really evolved from those origins right to where we are today, which is being super focused on helping the world's largest organizations and government agencies leverage this sort of platform as a solution to solve their problems. So kind of where originally we were really hyper focused on operations, we really extended from there and partnered with these large organizations to deliver value in some of their more mission critical areas. So what are these mission critical areas?

Let's look at some of them. So first of all we have customer engagement. And that's really all about hyper personalizing every customer interaction and just delivering that single next best action regardless of channel customer self-service. So extending those operations and service workflows into all the channels where your customers are already engaging with you agent based service. So distilling the hundreds, maybe thousands of potential actions an agent can take right at any given time into this seamless, guided workflow that works across chat, email, voice, essentially any channel your customers are engaging with you in. And then there's sales operations. So just connecting that customer engagement experience with tracking and operations. And lastly, um, regular operations. So the back office and managing those complex workflows and providing efficiency and oversight across all.

And then underneath all of this, we have our single unified suite of technology. So at the core of all of this is the Pega Infinity Platform. And this kind of brings together everything. Right? So it brings together the ability to define user experience, workflow automation capabilities, AI integrations, everything you really need to build an enterprise ready solution. And then sort of on top of that, we have these sort of marquee solutions that can be enabled, which I'm sure many of you are familiar with. Also to accelerate time to value for these different use cases. So, Matt, can you talk a little bit about those? Yeah.

Yeah, absolutely. So as Stephanie mentioned, everything that we deliver is built and operated on the same low code platform. Um, but it's really flexible. So really open and to deliver accelerated time to value in some of these areas that Stephanie mentioned, uh, we've started to accumulate some pre-built applications. So you saw Kerim talk about Customer Decision Hub and how we've started to add generative AI in there to help marketers all this stuff. Well, the basics of Customer Decision Hub is it's a capability which allows marketers and customer engagement leads to define their engagement strategy. What are the offers, the actions, the KPIs that they want to be putting out into the market in B2C use cases. And then the platform and this capability will automatically create AI algorithms and decision rules which combine all of their eligibility criteria. The filtering, you know, the data that's coming in from all the different channels and start to proliferate these strategies out into into the market and really deliver based on these decisions and these algorithms, the single next best action at any given point for all of the customers across all channels, which is great.

So you're able to really maximize your business goals across all of your customers simultaneously. So you could be serving different actions for retention to one customer versus different actions that are that are focused on getting someone to sign up for a new service to another customer, all on the same, on the same platform. And then customer service. So we have a customer service desktop, which is really focused on simplifying and guiding the agent experience. So it sits right on top of the Pega platform. So you're able to plug in any of the dozens, hundreds, thousands of different workflows which you're looking to automate and drive across your business and present those to an agent in a way that's not going to be intimidating. So when a customer calls in or emails in, or chats in, an agent is able to quickly get them to the type of action that is going to resolve their issue. Leveraging AI, leveraging Automation and be able to deliver improved service in faster time with decreased training time, as we saw earlier. And then in operations, really what's core here is just the Pega Platform, which allows you to define your workflows and digitize those, automate those, and then push those out in either a headless format or, and or if you have employees who need to actually get some of that work done, there's also a pre-built back office portal on top of this, which allows them to engage with their assignments.

So how does this work? So I'm glad you asked. Um, so this is a really exciting topic. And this really gets into kind of a lot of the things Alan was talking about in his keynote today. But I think when you when you're starting to think about how Pega works, it's helpful to bring to mind a typical customer journey. So, for example, somebody filling out an insurance claim. And, you know, in a lot of organizations, it probably looks something like this, where a customer needs to call into a contact center. And that contact center has its own set of systems and workflows. That's going to kick off on behalf of that customer, and then that's going to go to a back office team.

And that back office team is going to have their own systems and workflows that they need to kick off. And then at some point, there's going to be a manager who is going to want to find out the status of that particular claim or maybe every claim right across this entire operation. Um, and then the customer is going to want to check back in, and maybe that'll be via the website or via chat. And so as you can see, this is a very messy path. And with all of these handoffs between systems and teams, things really just get lost, right? Um, things get slowed down, friction builds up. It's really, really suboptimal. There's nothing worse than calling into the contact center, getting transferred, and having to like re-explain all of your information to someone like that? Exactly.

I can't do it, I give up. Right. And you really notice, right? When the opposite happens, when you call in somewhere and you have that great experience where they just know exactly what is going on with you at any given time. So that kind of goes back to our approach. And at Pega we take a different approach to help sort of clean this up. And it's all about defining the customer journey first. So in Pega we call that a case. And a case looks something like this.

So much cleaner right than that other screen. Um, and what a case does is it allows the business to define the major milestones along the customer journey and all of the work that needs to get done underneath each of those milestones to drive the case through to completion. And then around that, you're going to benefit from things like tracking and routing and SLAs, all of these things that are fully embedded into the platform, which is then going to help that manager get the insights they need, and it's going to help drive that case straight through to completion. But another really cool thing about the case framework is that it serves as a foundation to kind of iteratively automate the customer journey. So where at first you may have this case and a lot of these steps are going to be fully manual, um, through insights like, uh Pega Process Mining our reporting tools, you can kind of start to figure out where you're going to get the most bang for your buck to move some of these steps from manual to straight through processing. And, um, the case is really kind of the connective tissue underneath all of that. And so in the platform you've got all of these automation capabilities, right. Like robotic process automation, integration capabilities, correspondence, machine learning, decisioning AI and all of these things are really helping drive that case to straight through processing. I love it.

Um, but the thing about this is do I have to code this into all of my different channels? And how does that all fit in? Oh, man. And you're going to have that question. Uh, no. So how this works is so as we saw before, right previously, where all of these systems are all disconnected and they're causing all this friction to build up in Pega, we have our secret sauce, which is kind of what Alan was focusing on his keynote. And that is our Center-out business architecture. And what our Center-out business architecture does is it essentially means that when you're defining these workflows and these customer journeys, you're not defining them in just one channel. You're not defining them.

For example, just in the contact center, you are defining them at a level beneath, so at the customer journey level, and then you're deploying them across all of these channels in a way that's going to be really consistent. Um, so for example, what this looks like is you might have a customer call into the contact center, um, with a request, and then they follow up through chat on the website, and you're going to have a back office team that is going to know exactly what is happening with that case at any given time. Going to be able to make updates to it, and everything is going to be reflected accurately just across the board. Yeah. This is I think really key. You know, for the quiz. This is going to be really important for you guys to know. Um, this is like my Rosetta Stone for Center-out. You see those Center-out slides and the keynotes and stuff like that to me, you know, great slides.

Cool. I kind of find it hard to see myself in that. I'm like, what am I looking at? And this is really it. It's being able to take a workflow, define it once, run it in one place and deploy it seamlessly and give access to all of the users who need to engage with it. Customer service agents, back office employees, and customers themselves through the self-service channels. And so it's very, very powerful, right? Yeah, it is awesome. But so the thing I'm looking around, I see all the brand names in here, right?

Like, these people don't just have one workflow. I'm assuming. I don't know. No, but they have scaled operations. So how does this all kind of come together at that level? That's very true. So we've got these scaled operations. We've got these scaled contact centers. And obviously there are dozens, hundreds, maybe even thousands of independent workflows.

And they're all different right. So you've got these these managed mission critical processes where you have to spin off a transaction or you need to kick off an exception when that situation arises. And in Pega, we're all about automating all of those different parts of the customer journey and then bringing them back together. So you get that 360 degree view of everything that's going on across your operation, and then taking all of that and serving it back up to the people who really need to get that information. So it's very cool. And I yeah, I'm really excited about kind of our approach and our vision. But you know, I have to say, when I first started, I did think this was kind of complex. And I'm curious. Matt, I don't know if there's a question in the crowd out here about how do you get to this point?

Yeah, no. So I you know, I used to be a release manager for the Pega Platform for a long, long time. So kind of responsible for managing and optimizing the end to end software delivery lifecycle. And part of that is planning and planning stinks. Nobody likes to plan right at the, at the level which all of us kind of deal in these like big applications, these big automations you need to bring together like 12, 15, 20 different people in order to plan an end to end project. It takes like weeks of workshops, meetings, you know, big, long requirements documents. Then you need to turn that into user stories. Then you need to turn that into a backlog. Then you need to size your bag.

It's a whole thing. It stinks and it's really ripe for automation. And this is where Pega GenAI Blueprint fits in which you saw this morning. So I'm going to pull it up here as well. Kerim kind of stole my thunder, as he's one to do. Um, but we're going to take another look at it. So Blueprint is really the best way to start any of your Pega projects. It's going to allow you to build on best practices. So as you can see here, you go through and you first say what it is you're looking to automate.

And I guess a good time to go to the crowd. Does anyone have a project going on or something you'd want to see come to life in Blueprint? Specialty Insurance I heard one over here to specialty insurance. All right. So, you know, you go through and you're able to look through the prebuilt templates that we have that are based on Pega industry expertise, types of workflows we've delivered over and over. And we feel like we have some, um, some good best practices for. Is that life? I don't know, I'm not an insurance guy. Commercial, okay?

I'm barely an insurance client, so I'm the wrong guy to ask. Um, let's let's let's look through claims and see if there's a pre-built template for specialty insurance. You know, maybe there's not, but we could just as easily if I could type specialty insurance claims management manage end to end claims processes. I'll keep it short. But you can keep this. You know, this is this is a place for all of us to flex our prompt engineering skills a little bit and, you know, really put in the requirements, the goals, the different types of people that you want to be serving through this application, the different regulations that you need to adhere to. And through all of that Generative AI takes it. And rather than starting your application design from scratch, blinking cursor in a word document, you're going to get a template in here that you can hit the ground running with. So you're able to walk through once this is created and, you know, adapt a template to fit the requirements that you have for your workflows, which are your case types, your lifecycles, your data model, all of the data objects you need to automate, and then the personas.

And the really cool part and this is our blueprint here, which is cool. Workers comp seems specialty I don't know. I like that at any point, as Kerim showed, and this is maybe a little a little more zoomed in than his version. So you can get a better sense of it here, but you're able to get a sense of that Center-out business architecture, that idea of having a workflow designed in one place and at any time. So you're able to see how are all of my workflows, which we have right up here on the right hand side. How how is any of these going to be manifested for a back office employee in the operations console here, a customer service agent through the agent desktop. If they were to need to spin off a task for a customer on the other line. A customer self-service scenario where we want to plug this workflow into our website, or a mobile application which is built for employees to have access to their work, their assignments on the go. And you can also see it, uh, in terms of the APIs that are built around it, all automatically generated that allow you to run this headless or plug it into a custom UI.

So really cool stuff. And then I won't do it now. But as you walk through, as you adapt this, it gets you that asset, which is a Blueprint file that you can take directly into the Pega Platform to generate a new application starting point in seconds. So that's how you plan a Pega project. I love it. And being able to see these live previews is just so cool. It completely just brings to life all of the power of that Center-out business architecture. Yeah, it is really awesome. But you know, we've kind of mentioned a couple of times complexity, complexity.

So, you know, once I've done this, I've built all these workflows. Uh, that's cool. But what am I going to be able to deliver to the employees in my ecosystem and really help them get their jobs done? So we have something for that. We do, we do. So, um, it's a great question, though, because, you know, once you've started to start blueprinting and building these apps and bringing them out to users, how do you kind of get these workers and contact center agents up to speed and working in some of these scaled operations more quickly. Um, you know, because what we know is your contact center agents are dealing with a situation that's probably something like this where they're hopping across 5 or 10 different systems throughout the course of their day, or even throughout the course of a single interaction to get their job done. And all of these different systems have a different set of work, different set of data that people need to be familiar with, different layouts. Just everything is completely different.

So, um, you know, and what that does is it, it it creates that kind of swivel chair effect that we're all familiar with, but it also really builds up training time because these, these contact center agents and employees really need to get familiar working in this super complex environment. Um, and it's just extremely suboptimal. So at Pega, we've introduced something to help with that. And it's called Pega Process Fabric. I think it's going to be on the next slide. Um, so what Process Fabric does is it lets you register in all of your applications, both Pega and non Pega, and it listens to all of the cases and assignments across all of your different systems, and then creates a unified real time index of all of the work across your organization. And then on top of that, it builds a unified employee experience. So you're giving your employees a single place to go to see all of the work that they need to do across their entire organization, and you're building them this single unified work list. So rather than me as an employee having to hop across ten different systems to understand what's on my plate, and then probably, in my case, picking what to work on based on what's easiest, I'm going to have this single prioritized place to go, and that's going to be proliferated across all my experiences.

Exactly right. So that's another really important part of this, is that in Process Fabric, managers are able to define some logic to really drive the prioritization. Prioritization of work from a holistic standpoint, sort of across all of your applications, across all of your systems. But there is one more part of the employee experience. I'd be remiss if I didn't point out, and that is our user experience. So we call that Constellation. And over the last few releases, it's really been redesigned from top to bottom, um, to sort of drive a more intuitive employee experience. So that's one where employees can go into any system, any case, and the data is going to be laid out the same way. They're not going to have to think too hard about where to look for their work, which is really cool.

But I think it's about time we get to the really interesting part of this. What do you think? Let's get to the fun stuff. All right. Let's talk about AI. AI. Um, so key to simplifying the employee experience is guiding people to what to do and giving them insights on what's going to happen. And this is where Process AI comes in, which you heard Alan mention. So Process AI kind of takes the stance that as your customer service agents are interacting with your customers, or as your back office employees are getting work done, completing assignments, they're creating a massive data set in the form of case history, which has a ton of untapped value.

Like you're probably using some case data for BI. You're probably storing it away in a data lake for compliance purposes. But are you really getting a ton of future value from it? And this is where Process AI can help. So it allows you to turn on machine learning algorithms in just a few clicks, which continually learn from that set of case history to drive future insight. So if a new piece of work comes in based on your past history, you're able to say things like is this likely to meet or miss its SLA? And both give an alert to an employee who maybe opens up an assignment that they may need to take some special action, or embed that prediction into your workflows to proactively escalate that piece of work. You can also do things like predict how is work likely to resolve. So let's say you have a loan that comes in.

Is this likely to be approved denied. Abandoned. And then you can take some different actions and different processing based on all of that. So that's really cool. And that's the statistical predictive AI. And we're also looking at generative AI. And the opportunity this has to simplify the employee experience. And there's some really good research going on. It's early days with all this stuff.

But the National Economic Bureau of Research did a study on thousands of contact center agents to understand how can Generative AI help and what's the actual potential value here for contact center agents. And what they found is, you know, for agents, employees who have been working in your organizations for a long time, they're skilled Generative AI not going to help much. But the massive value, the untapped potential is for new and unskilled employees. They found that the application of AI was able to get them near the productivity level of the top performers in the contact center, which is massive, right? Imagine if every employee could be your best employee. It's crazy. And I always think about, you know, if I were to be plopped in to one of your operation centers or one of your contact centers. First I'd be like, how the hell did I get here? Get me out of here.

But then I open up my first assignment, and I would have a trillion questions. Where did this come from? How do we typically process work like this? What are the policies I need to adhere to? What is what are people saying about this work? What is the collaboration look like? What is this attached document and what's important within that? All of these types of questions that it's going to take me an hour to process a single customer service interaction. So this is where where we've begun to apply generative AI all in service of aggregating, summarizing, and pulling out what's important from the massive amounts of data that employees need to interact with throughout the course of their work.

So we have Knowledge Buddy, which you heard about, which is all about, you know, taking your enterprise content, allowing you to plug that in, building a retrieval augmented generation system below it, and then enabling a concise, summarized question and answer based on those docs. Pega GenAI analyze takes the same approach, but it's prebuilt and it's for case data. So it allows you to get insights based on your work. Ask questions about what are the trends going on in our back office. You know, where are we seeing certain types of issues and get some instant insights and answers based on all of that. And then Pega GenAI automate. This is where we're starting to apply generative AI to do things on employees and agents. Behalfs like suggest email responses in customer service scenarios or automatically create, you know, interaction, wrap up summaries and post those to a customer service case. And all of this is available through Pega GenAI Coach, which is a new conversational interface embedded into the back office portal.

So it allows agents, employees to interact with all of this through a conversational experience. So that's it. Now I feel like I could be plopped in into one of your organizations, and it would only take me a week to get fired. So there you go. A week longer than before. Yeah. So that's great. I mean, I love everything that we're doing with AI right now, and I think it's really going to be game changing. Obviously, you know, we kind of flew through this and there's just so much more that Pega can do.

Um, we really encourage you to, if anything, piqued your interest out of what we talked about today. You know, go to one of the sessions about Knowledge Buddy or Coach or Constellation, um, go to the Innovation Hub booth, really kind of, you know, find out what you want to dig into and learn more about. And we hope this was. Use the Blueprint kiosks. Yes, definitely. It's a must. Do. I need to go do that. Actually I want to go check it out.

Um, but yeah, we hope this was a great one on one for you. And, um. Yeah, we can take questions. Yeah. I was expecting a standing ovation. I think there's mix up here. If you have a question, you can walk on up. Hi, I'm Sam. Great presentation.

First of all, thank you for this. Um, the question that I have is, uh, the GenAI part was cool, but does it have any personas? Like, for example, if I'm an admin, does it answer based on the persona that is being asked, or is the same answer for everybody? No. Yeah. Great question. Um, so there's a couple different levels where you can define system instructions and uh, sort of the prompt around these Generative AI capabilities. So you can do that in Knowledge Buddy. So Knowledge Buddy the way it works is you can connect in or plug in all of your content from multiple different sources.

So you build kind of a library of data sources. And then on top of that, you can build different prompts for different people that combine any number of those data sources. So you can say, like my customer service agents, they need access to these sets of data. And the responses should be like this. My customers only need access to this data and it should respond like that. And then APIs get generated automatically around that. And in Coach, there's kind of a similar idea where you can define system instructions, the personality, and then suggested questions for the employees. One more question. So you were sharing your templates right.

Like where we choose like Healthcare. And then I have a dropdown which talks about multiple options available. But is there a way that, you know, clients like us, you know, provide the template and we automatically just give it the simple configuration file so that it reflects. Is that possible? Yes, absolutely. Right now that has to go through Pega. Um, but we do have the capability to set up templates for any organization, and they'd be sets of workflows that would only be visible to the folks in your organization. So if there are certain types of workflows you want to scale out to anyone who might go into Blueprint, we can help you with that. Thank you.

Sure. Um, I had a quick question on the analyze AI on the reporting and how that works. So the way it's set right now is the configuration, the tools for configuring that is that at a state right now where if I have a supervisor in the call center, they can go in and say, hey, I just need to build a new report for this new team we're building out. And I need to look at their, um, survey performances X, Y, and Z. And do you guys, right out the gate, expose all data sets to this analyze AI? Or is it something where you have to go in and do a little bit extra work. Or is it all based on template? Sorry. No question.

Yeah good question. It is set up so you can give it like on a role by role basis. So you can only give it to managers or something like that. And it's the Generative AI portion of it runs on our insights framework, which is you know it's yeah. So all the data is there set up for like self-service slice and dice reporting. It's actually very slick. We should have shown that. We'll show that next year. Thank you.

Okay. So. I like you actually do release management the backlog and everything here traceability testing out to production. And I've just turned out kind of like, you know, visualize how it's going to help in this endeavor here. So, um, yeah. So that that's my question. Yeah, absolutely. Yeah. Um, an area where I was going to say, I'm still passionate.

I don't know if that's true, but. It's a lot of work and a lot of coordination. You know, as you know. Yeah, yeah. So we do have a suite of tools which are kind of built for release managers. Um, kind of at the core of it is a capability called Deployment Manager, which lets you actually use Pega's case setup to define your CI, CD pipelines so you can embed in the running of automated tests, different approvals, deployments to, you know, sandboxes and test suites and all this different stuff. So there's a booth out there around Deployment Manager, and I would assume next to it is we also have testing capabilities and performance evaluation capabilities. So a ton of stuff for you. Yeah okay.

Thank you. Yeah. Oh this guy. Just an easy question. I mean it's impressive what GenAI does with training agents and so on. But when you first time get in contact with Pega, It's quite a disruption to business to get it to understand. Do you also work on having a full experience of having day one users being trained in Pega, and only afterwards in the process they are using. From the end user standpoint, like an employee? Yeah.

So the Agent Trainer has sort of inspired us to potentially take that same idea and apply it to the back office portal, which I think would be really compelling. So that's one. And then I thought where you were going was for developers as well. And that's one thing we didn't really touch on, but we are also doing a ton in terms of generative AI for assisting developers to get up to speed in building applications in Pega, and we call that all Pega GenAI Autopilot. So now there's a new conversational interface where developers can ask questions on how to build, how to architect certain types of things, and they'll get summarized answers based on our technical knowledge, our Pega community and Academy, and then a ton of other cool assistance capabilities there. One question to that. In addition, um, is it also possible that we add our guidelines and architectural rulings on that one so that we make sure that they directly are in our guardrails? I'll have to check on if that's possible today, but it's a great idea. So let's work together on that.

Thanks. All right. Cool. Do I think we have one more here? Oh, we got one more. Yeah. Um. Really good presentation. Thank you.

Uh, yeah, it's really good. Generative AI stuff. When it works, it's awesome. But when it doesn't, it could be distracting. It could be a waste of time. So how do you measure the quality of any of these AI products out of the box and on an ongoing period of time? Because you start off with really good quality, in my experience, for a period of time, it degrades because user behavior has changed. Your models have not kept up with it. So how do you measure that?

Yeah. No, it's a good question. Um, we're doing some studies on the development side right now with Capgemini. They actually analyzed, you know, development in Pega with Generative AI versus building the same application in Java. And they did the same study without Generative AI, maybe like 2 or 3 years ago. And they found, you know, I forget the actual numbers, but, you know, three years ago they were able to build an application maybe five, five times faster in Pega versus Java. And now it's eight times with Generative AI. So we are doing some studies like that. And then um, the other thing I'd say is, you know, we're all kind of learning with generative AI, trying to figure out the best practices.

Where is it going to have value? What's the opportunity there? Um, so we'd love to hear from you guys as you are applying it, and we'd love to partner with you there. And then hopefully we can get some amazing stories next year at PegaWorld, which inform what we should all be doing. Hi. In a scenario in which we already have an application built on Pega, is it possible to use Blueprint to refactor it? Uh, it's only for the brand new application. I'll take this one. Sorry, I didn't quite hear it.

No. Um, yeah. So you actually saw Kerim mention BPMN import in his keynote. So in the next couple of weeks, you'll be able to take some legacy assets, starting with BPMN, import them into Blueprint. Generative AI will analyze them and give you a starting point based on what you're already doing. So that's cool. Um, and it's really the starting point for us. So that's going to be a broader strategy where we are going to be sort of opening up Blueprint to take in different types of legacy. I'll call them heritage assets from your IT landscape.

So BPMN is one, we've been looking at UI screens as another one user manuals, process documents and then of course process mining analysis. And there's actually a booth in the Innovation Hub that will show a process mining analysis being run on a Pega application and then filtering out the inefficiencies and then pushing that into Blueprint. So this sort of legacy modernization strategy is something you're going to see more and more of us, talk of us talking about as we head through the second half of the year. Thank you. Yeah. All right. We nailed it. Um, cool. Well, if there's no other questions, we appreciate you guys all joining.

And 201 is right out those doors. Yeah. Thanks, everyone.