PegaWorld 2025: Pega 101: AI-Driven Innovation for Transformative Business Operations

PegaWorld 2025: Pega 101: AI-Driven Innovation for Transformative Business Operations

Good morning everyone, and welcome to Pega 101. This session is all about AI driven innovation for transformative business operations. And my name is Anu Shah. And I'm joined here with my colleague by my colleague Jeff Aiken. And together we run a global organization of technical sales experts focused on the most cutting edge technologies that we offer. Say, say hi, Jeff. Hey, Anu. So Anu and I have known each other and been working together for 20 years, which just seems amazing. We were doing the math last week and that just seemed crazy that it's been 20 years.

But today we're really thrilled to be with all of you here at PegaWorld 2025. And whether you're new to Pega or a seasoned veteran, this session is going to provide valuable insights into how AI powered decisioning and workflow automation are revolutionizing your business operations. In the next 40 minutes, we're going to explore how our cutting edge technology is helping organizations in three ways enhance their employees productivity, deliver personalized service, and drive unparalleled value globally. And let's start with a fundamental truth. Digital transformation is no longer just an opportunity for organizations. It's really become an imperative with the fast moving developments in artificial intelligence. Any organizations that are failing to embrace AI are really risking falling behind their competition, who are using AI to create more efficient operations and better customer experiences. And that sounds really, really important. But I don't even know what does that transformation look like?

I knew. Well, what I'm seeing is that the organizations we work with aren't just implementing AI into their existing processes, but they're using AI to change every aspect of how they create software, including the design, as you saw earlier, the development, the testing and the delivery. What they're doing and our clients are doing is building software around the decisions, the work and the outcomes they want to achieve. And what this is allowing them to do is deliver consistent experiences across channels for their customers. And the leaders that work with Pega that are moving in this direction are really on the path to realizing the autonomous enterprise, where organizations become more self operating, adaptive and data driven. Yeah, and what we're seeing are incredible results. And this is $150 million in incremental value, add 50% faster development time and 20 to 40% increases on your NPS scores. So anew, how is Pega participating in this transformation? Well, at the heart of Pega, at the heart of this transformation is Pega, the enterprise AI decisioning and workflow automation platform.

Our Agentic orchestration is allowing organizations to design, automate, and optimize in four critical areas. The first is customer engagement, and this is the engine that's creating that AI driven, personalized, contextual experience for customers in a variety of channels. Then we also have a customer service platform, and this is a platform to deliver efficient, empathetic support to our customers when they need it most in whatever channel they desire. We also have workflow automation, and this is all about streamlining your operations to increase efficiencies and reduce your costs, leveraging our automation platform. And then of course, Pega's approach to leverage to legacy transformation is all about replacing outdated systems and moving organizations into the future. And again, we're seeing incredible results. And by using CDH. Vodafone saw 100 million pounds profit in a single market from a higher offer acceptance rate. And what I was thinking about today, because that's that customer experience Blueprint we just saw run on the main stage in there.

I was thinking this was these results are really before that even happened, right? Think about the speed and the uplift they're going to get from starting to use that with their CDH implementation. And then by using legacy legacy transformation and our Platform T-Mobile transformed and optimized over 500 workflows with like first to market speed, get out there, be first, right. But this is a Pega 101 session. I thought that meant how many people would be here. There's a lot more than 101, but I'm assuming some of you are new to Pega. Or maybe you only know Pega in 1 or 2 of the areas that we kind of just went over. So let's get into a little bit of details around these, these four primary ways. We really are trying to help our customers transform.

So in customer engagement, the Pega Customer Decision Hub acts as one centralized decisioning authority or brain to power all client engagements and our artificial intelligence power, adaptive powers, adaptive models. These words are hard for me, which means that as a customer, as a customer puts out data, signals, all the things they're doing with you and in the market, the technology senses what the client needs in that moment to provide the right thing. With a centralized brain in place, you're going to get rid of your traditional silos that go across your organization and deliver the relevant messages at the right time. That action could be a personalized sales offer, a retention plan and nurture effort. But whatever it is, and this is the really power of it. It's using real time AI and adaptive models and machine learning, and then it's delivered out back across any channel, inbound, outbound, paid media, and even agent assisted, as we saw in the presentation this morning. And all this is happening in less than 200 milliseconds. So for our customers that do use CDH, the number one rated real time interaction management system, they translate to about $10 per customer, per year of additional revenue through more relevant offers and more uptake on those offers. And then the one that's really near and dear to my heart.

I've been at. I've been with Pega since 2007, and my main focus has been customer service throughout most of that journey. And Pega Customer Service delivers a comprehensive solution now powered by a lot of advanced AI capabilities. So we are combining the personalized agent desktop that's been a part of the customer service application for a long time. With the dynamic case management of our platform and AI powered tools, including chatbots, virtual assistants, and voice AI to streamline all the operations and enhance your customer satisfaction. I think we saw some of that in the presentations this morning. And building on top of that, world class case management system allows us to build processes that work in any channel and operate at the same level as your best agents that are answering the phones today and complementing these core capabilities. Customer service also integrates easily with some of our other solutions, such as Pega Sales Automation, and then it also serves as the code base or the base functionality for a lot of our other applications, such as collections, Smart Dispute, Smart investigate, and then really critical for the future of customer service is the idea that we're moving away from the agent desktop. They're not going anywhere, but we're going to hope all of us are hoping we're not going to be adding thousands and thousands of agents, but hopefully removing some of those.

And so the platform which has the agent desktop in the middle is really sorry I lost my place. The platform really extends beyond the desktop and into contextual digital self-service capabilities for web, mobile chat and the really cool stuff that we saw Kerim doing on stage, the voice interface stuff, allowing customers to perform independently and reducing your volume and your customer service centers and organizations using Pega Customer Service. They usually save about $10 million on call handling and call deflection costs. So it's pretty cool and I love it. Now Anu is going to talk about what's near and dear to her heart, which is the platform and legacy transformation. Thanks, Jeff. And at the heart of the customer service platform Jeff just spoke about is workflow automation, the engine that's driving those consistent customer outcomes. What Pega does is it automates your complex business processes by seamlessly orchestrating the structured workflows with those agent driven tasks. And it's this blend of the discipline of process with the power and the flexibility in AI that's allowing true end to end automation for our customers with full governance.

And what I love most about Pega and you've all seen it, if you've seen the platform, is the fact that we have a visual design interface. And what I'm seeing is our business users, the business users within your organization can strengthen their collaboration with it in a visual paradigm. And the way we start, as you've seen on the main stage, is we design and ideate with Pega GenAI Blueprint, which is a free SaaS product on Pega.com. And this is where we collaboratively design the processes we want to automate. We can define things like the personas or the people involved in the process, the channels we'd like to expose the process on, and the data we want to push and pull from systems to instantiate the process. We can even preview all of the user interface, as you saw earlier today. We then take that blueprint and you didn't see a lot of this, but we import it into a studio we call App Studio. And this is where we can further configure the workflows to do things like intelligently route the work to the right person or the right team, connect siloed systems and orchestrate those full journeys. It's this combination of generative, AI powered blueprints, process orchestration, and AI driven automation that's driving an average of 45% efficiency increases for our customers across their operations.

And given the power of this workflow automation, Pega is uniquely suited to transform legacy systems. We jumpstart the discovery of those legacy systems by automating the extraction of the processes, the data and the requirements. And we do this in a couple of ways. We use third party source code analysis tools as well as our process mining product to extract all of that knowledge. Then we take that extraction and we import it into Blueprint, leveraging generative AI in our decades of automation knowledge to optimize and redesign quickly. And then we build faster using that AI decisioning and workflow automation platform. And what this is allowing organizations we work with to do is rapidly deploy new workflows in weeks instead of years, seamlessly integrate with their existing systems, and leverage that AI driven innovation to drastically reduce and accelerate the design of their systems. And what we're seeing is this approach is dramatically accelerating the time to value. Yeah.

And so these people have had to listen to us talk quite a bit. You probably need a drink of water. So why don't we go ahead and see a little bit of a demonstration. It's pretty. It's a little similar to the one you saw, but I like the detail in this one in particular. Let's start with creating a blueprint that will model how your application would work in Pega, because we want to help you leverage your existing work and assets. You can import information at various points of the process. Firstly, any textual description, a process definition or a walkthrough readout can be interpreted by generative AI and used to feed the prompting process. In this case, for an Insurance first notification of loss process will select our Subindustry and department before naming our organization.

We can then see the pre-prepared prompt that gives an outline of the application to be built. You can add as much or as little information as you like. After building it. It returns the results in which we call Pega case types, which contain the steps and stages of a workflow. In this case, an initial contact with the customer looking to report an incident. As part of this blueprint, we also want to add an additional case type for managing the onboarding of customers with high value vehicles or high risk activities. Rather than prompting, in this instance, we can take a preexisting BPMN file to help us build directly in Blueprint, which we can then see listed. Blueprint then starts building the workflows, which allow us to visualize the steps and stages required to run each case. Here we see the progression from initial contact to triage, investigation and then resolution.

We can also preview what data is required within the workflow, containing everything from the customer information, incident details, to third party involvement. Once more we can make use of your existing assets. And this is where we saw what we were talking about earlier with legacy transformation. Here we've taken system requirements. We've taken a BPM file which many of the software systems that you use can extract or you may design in BPMN. And we've also taken DDL to kickstart that redesign and the design of your Blueprint. This time importing a SQL Data Definition language file to give you full control over the data model used. Finally, Blueprint will present a list of personas required to interact with your workflow. This could be everyone from the policyholder making the claim to the claims handler, processing the request down to the repair technician.

Dealing with fixing the vehicle. Finally, we are presented with the overview, which in a single screen provides details of each case type and the data and personas required for operation. Here you can see the onboarding case that we've created from the BPMN file import. Now the eagle eyed amongst you might have spotted the big blue preview button down here. It does exactly what you think it should do, allowing us to see a real time version of your application that you can interact with. Let's try creating a new case. Initial contact. Oh, you know what? I don't like the fact that it's just got one field for driver information.

Let me go and change that. I can go back to the workflow detail screen. Then add a couple of fields for first and last name. Then remove driver information and reconfigure the order. This will drive the capture information that appears on screen. Now let's go back to the summary and preview the application again. Quickly you can see how this behaves like a real screen in Pega showing you reporting dashboards and case view. If I go to create a new case now you can see the changes I've made in real time. And here you can see how we've designed in Pega GenAI Blueprint.

We've previewed the screens, we made a change. That change was reflected across a variety of channels. So what you're doing is you're making a change centrally based around the work and the outcomes, and it's immediately reflected in the variety of your channels. And that's actually the heart of what we say when we're saying our business architecture is center out. Not only that, you can see how your app will look on mobile embedded within another enterprise application, on the desktop of your contact center colleagues, and even how it would look exposed to your customers. We can also chat with the conversational agent, allowing your customers to communicate naturally with a model that understands nuance and unstructured information. And here what we've seen is we've created a new workflow using all of that legacy data. We've designed the workflow and it's immediately conversational. And this is due to the agent X API, and I'll talk more about it later.

But what we're doing is two things. We're using reasoning and natural language processing to understand that when you hit a car, what is the most optimal workflow to execute, right? So we're picking the right workflow based on the API. Then we've done no work, but that workflow is immediately conversational again, due to the APIs that are telling us what information we need at each step of the workflow. And those are the two concepts we mean when we're talking about Pega being naturally agentic. The days of chatbots falling over at the first slightly problematic challenge are thankfully coming to an end. Finally, you can emulate service calls in and out of the application. This will allow you to easily extend Pega functionality into other systems and application, all whilst keeping the rules and intelligence in one place. And all of this really speaks to what we call our Center-out methodology.

Now if you're thinking this is just a standalone sandbox for playing around with. You'd be wrong. Blueprint allows you to download a configuration file that can be used to build from fresh, or extend an application within a live Pega environment. After previewing the application on import, we can choose what gets either inherited, reused, or built directly from the blueprint. This applies to case types, data objects, and personas. And here we see the results in App Studio. You'll note that we can visualize the construction of our app, built in layers that allow you to reuse and adapt across regions and business units. We call this the situational layer cake. We can open the contact case life cycle and see a similar view to Blueprint.

And here once again we can preview the application to see how it looks and make changes in real time. Back in the case Life cycle view, we can dig in further and configure. We can select generic service level agreements, or create custom ones that reflect the importance of each stage, and step goals and deadlines can be established that raise the urgency, notifications can be triggered, and automated escalations can occur on a controlled basis. This will ensure that your teams and your customers always know where the progress of a case stands. You can create and maintain data within Pega, but it's also very easy to connect Pega to live data sources. This allows you to bring your critical information to one place for your colleagues. Cutting down the amount of time needed to switch between desktop apps. Here, we're simply connecting to a data source in real time and making it available to be utilized within your Pega applications. Our final piece is making use of Pega Autopilot, its conversational AI that gives you real time guidance and feedback on anything that relates to building within Pega.

And of course, it's telling me to start with Blueprint. Of course, everybody. Starts creating a blueprint that will. Say, hey, I really love that demo. It's it's it's a little more detailed than what we saw with Kerim you saw that step to go from blueprint into the App Dev Studio. And, and that's, that's a really cool thing. And you guys notice that I use my natural accent when I was recording that. Everybody get that. So it was really I got to switch to this for the stage presence.

But um, Anu what's really driving this mandate for change around transformation? All right. Well, I think we're all aware. That the transformation is happening against the backdrop of three major shifts in the world of work. First, we're seeing the rise of AI agents, and these agents can perform increasingly complex tasks. And I do believe that we're just starting to see the realm of what's possible with Agentic AI and what can be accomplished in this AI race. Then we're seeing. Of course, we don't want those rogue agents that you saw that visual on with Alan's keynote. We don't want to see agents that are ungoverned and unpredictable.

We want them to be responsible, and we want to trust them, and we want to trust what they're going to do. We're also seeing a huge demand, even in our work, in the work we do to reduce that repetitive labor, that low value labor, that low value activity. Right. And we're seeing this. And that's a huge drive to change the way your employees work. Yeah. And I think it's I felt like I was being made fun of this morning as a former COBOL programmer. But we have spent decades of bolting on new technologies and capabilities onto the code stacks we already have. Right?

And with the rise of Agentic AI and with the rise of transformation. We have to take a different approach, don't we? We do. And enterprises are really at a crossroads right now. And I can take you in one of two ways. I think there's a difference between paving the cow path and doing what we already do, just doing it faster. And you hear this with GitHub copilot and code generation, and we're doing it faster. But what we're doing is adding on a lot more code and a lot more complexity. Or you can take Pega's approach that we've seen, and that creates meaningful outcomes by building around the work.

But the truth is, you can't just use AI and transform your enterprise with AI alone. What's needed to underpin that? AI is a boat platform. Gartner's defining the concept of boat or business orchestration and automation technology platform. It's a big mouthful as a class of technology that enables the orchestration and the automation of a wide range of business processes. And what it does is it connects disparate enterprise applications, also augmenting the operations of those applications with embedded AI. So you're going to hear a lot about AI agents and go to the innovation hub. But what AI agents need are tools, and boat provides the tools for your AI agents. Yeah.

And if I think I understand it. AI alone is not going to be enough. And I love a good acronym and a good theme. So what is a boat? What exactly are the parts of a boat? Okay, so. A boat platforms unified technological capabilities into five key components. First, you need automation. And this is the heart of a boat platform.

And Pega has this with business process management as well as low code capabilities and robotics RPA. And then you also need orchestration or the brain driving the boat, and Pega, which consists of an orchestration engine with integrated statistical AI allowing for the system to continuously learn right from your past work and your prior work in your system. And Pega has this with what we call case management as well as Process AI. Then you need an optimization engine and this is the boat improvement engine. And Pega has this with a Process Mining product. You also need integration because you need data right. So this is the pipeline fueling the boat. And Pega has this with our data virtualization layer called live data, as well as complex event processing where we can process complex events at high volume using process AI. And of course, you need those AI agents themselves.

And this is the Experience Builder, which Pega has with integrated expert AI driven coaches and AI agents. Built low code, right. And what's important to recognize in all of this is that Pega has all of these capabilities in a single unified platform. Yeah. And so if I understand this correctly, and we're going to stick with the nautical theme for one more slide. So in order to unlock the transformative value, organizations have to do have three essential steps. First, they have to raise the legacy anchor that's holding them back. Right. Make sure you're really transforming your legacy code.

Make sure you're getting rid of those stacks of stuff that's just causing you more problems than it's worth. You need to put AI agents on the right boat. Business orchestration and automation technology platform still a mouthful. And then we have to set a course for the autonomous enterprise. Right. And it sounds easy enough, but let's talk a little bit about what that journey is to the autonomous enterprise and what that kind of looks like. I really like this slide because you can see this and understand any organization, even a part of an organization, can be operating at a particular part of automation, for lack of a better word. Right. And I think of the first one, they're manual.

I went and saw my two year old grandson in his in his daycare room, and that is clearly a manual space. These teachers are just trying to keep people alive, just fed and alive. There's no processes. You just are dealing with every issue after every issue. And I think, sadly, probably some of us know parts of our organizations or have worked in organizations in our past that have a similar vibe. But as computers and systems matured, we started to get into the other types and you get into things like managed, and then you get into automated, where you're thinking about batch processing and payroll structure. But over the last 24 months, we've seen the rise of AI, right? The real AI that's actually accessible and available to people in their systems. Right.

And intelligent AI is a great way to think about it. I was really struggling with the difference in a new set. It really great to me, right? She said, think about the buddies and coaches. Think about this is AI that is trying to use to make predictions based on the data that it has and past actions. It's trying to inform you and help you make a better decision, help you do something better. But it's not doing the work or making the decisions right. The autonomous enterprise where a lot of us would love to get to, especially with some of this, this repeatable tasks that are simple is is actually where the work is optimized in real time, with AI agents making decisions and actually completing the work without much supervision at all. And just a short pause because no show is complete without a commercial in the middle.

So commercial is Pega can provide really good in-depth application reviews to help you understand where you are on this journey in different departments and systems. And it just so happens that Anu and I, our teams, are two of the many teams that do that for the organizations globally. So if you are in a situation you think, man, I want to get there, but how do I even make that happen? Reach out to us. We'd love to help you. Commercial over. So the progression really delivers four key benefits if you get all the way to autonomous. But I think the one that I like to talk about is governed agents there. And why governed agents?

Because predictability with AI is going to be the number one thing we're all thinking about over the next year or two, right? We're in highly regulated industries. You guys are in healthcare, financial services, insurance. Everyone knows how important the governance is. No one is not talking about it, right? But not everyone has over 40 years of workflow and process automation that provides those guardrails and the roadmap for their new AI agents to actually make sure they're doing the right steps at the right time. Except for Pega. So a new why is Pega the right partner for this journey? All right.

This is where we get we get to talk about our architecture called Center-out. So in Infinity 25, what we've done is we've taken all of our capabilities to the next level. What we've allowed to happen is every workflow you build in 25 is immediately agentic, meaning it can leverage AI to make intelligent decisions and take autonomous actions and core to Pega's ability to combine the power of agents with the predictability of your workflows. Is this architecture we call Center-out? And I do believe this is the right approach for an AI driven and agent driven world. And what Center-out does is it does not start by embedding a lot of logic into your front end channels, which we all know created a lot of duplication of logic across those channels. And it's even worse in a world where we're moving towards less screens and more agents to do the work. And we also don't want to embed your logic into the back end trapped in your legacy systems. We don't want to design around your data structures, right?

Instead, we want to build from the center. First. We want to define those business rules. And I and the decisions we want to make describing how to run your process. Then we want to describe the workflows, as you saw earlier, that you'd like to automate. And then we want to capture all of that in a common definition or a case. And the key value of a case is that it allows you to track all of that work really easily. Right. And what we're doing now is we're surrounding all of this, all that work we define on the front end with two APIs.

One is the Digital Experience API, which allows you to connect that work to any front end channel. We've also introduced the agent X API, which allows you to take that Pega workflow and connect it to a Pega agent or a third party agent. And then on the back end, we have what we call live data and Agentic live data. And that's where agents we can use to find the right data at the right time, dynamically from a variety of data sources. And we can rationalize it into a format, a business format that your workflows can use. And this architecture, because it's API driven, is exactly the right architecture for an agentic future. Yeah, and that's a lot of differentiators. It's also a lot of words. I we tossed a coin for that slide and she got that one.

So let's take a few minutes and talk about some of these most critical differentiators that you mentioned. Right. And I think it's important I just want to spend a few minutes with Jeff and I to just double click into how you define workflows in Pega, and we really allow you to orchestrate your work across the enterprise. And what that really means is the ability to low code, define where the work goes and what work is the most important. Using rules in AI, we can route it to the right team or the right person using what we call routing rules. And we can also ensure that those service level agreements are met and importantly, take action. We can move the work if that service level will not be met. Now I got this I got this slide. So I get to talk about the Pega situational Layer Cake.

And it has always made me smile for 20 years to say that, because it's kind of a funny name for an amazingly powerful part of the system that you heard them talk about on the main stage today, right? It's used to manage complexity and variations across the massive size of organizations that we work with. And the easiest way is an example, and my example seems so pale compared to that beautiful slide he had up there today around his layers that were ice cream and cake. But, um, so a global bank might use our enterprise layer to use standard login processes. But then at the division layer, they're putting in their general loan processes. And then as they dive deeper down at the next layer. In the unit layer, they might create region specific mortgage rules for different countries, different geographies, different regulatory areas. Right. And this layered approach ensures and ensures consistency accelerates development.

It simplifies maintenance by allowing those changes to be made really just in the one place they need to do, but then be accessible to any of the applications or any parts of the processes that need those. And then once you do that, you can begin to use these processes across every channel. And I see every in here. But I always say any channel because there are channels Pega can be on today that when we first built this architecture didn't exist, but we just can absorb them because of that Center-out architecture. So if a customer starts a process in their mobile app, they can pick it up in another channel, whether that's in a web portal or a call center, or even an AI agent that's assisting them over chat or voice. And everyone has visibility into all this work, so it never gets lost. It never slips between the file cabinets. And then what's most powerful about that is your agents and your employees are guided through every workflow step, ensuring consistency, efficiency, and accuracy. And as you can transform faster with a lot less disruption in that case.

And as you guide those employees through their work, you can provide them with the assistance they need and the AI autonomy that best fits their needs. And to get to this intelligent enterprise, you need agents to help you. And two of my favorite are Knowledge Buddy and Coach. The Knowledge Buddy agent really provides instant access to your enterprise knowledge and your policies and procedures, and it uses NLP and semantic search to go against your own organization's data. Then we also have coaches, and I think of these as expert assistants providing structured guidance, leveraging your data and generative AI to bring together that data with your organization's best practices. I think of the coaches as expert assistants, and you can have multiple coaches on a process to provide expertise in different areas. It's like an expert assistance with predefined prompts. And then you can also on your journey to the autonomous enterprise, you can build your own agents using our AI agent framework, and that will provide instant access for an instant help for every workflow. And these agents have knowledge, they have data and they have tools.

There's two types of agents. One can act autonomously when appropriate, without any human interaction, and optimize the execution and select the right workflows to execute. Or there's agents that can provide conversational support for your employees or your customers powering those employees and channels, such as channels such as chatbots. And then let's just unpack this agent experience a little bit, because I know everyone's curious about it, right? So due to the agent X API in the front end, we can turn any workflow in minutes into that self-service experience. And it's because we're natively API driven that it's so easy for Pega to do. And unlike other Agentic architectures, we orient around that work and the outcomes. So instead of creating new ways to process work because the agent reasoned differently for every single request, the agents are instantly powered by workflows you can trust and meaning any agentic experience, whether it's Pega or a third party, can tap into our workflow automation and get work done in a very reliable way. And then alternatively, within that workflow, you can also call another agent Pega or otherwise, or a third party agent, right?

Adding those agentic connections within a workflow. And what this API does is it ensures that every single agent interaction is tracked, it's governed, and it's auditable, leading to AI agent performance that enterprises we work with really need. Yeah. And we've talked a lot and this sounds great. What I want to show you now is we're going to take that journey of the earlier video where we built out a first notice of loss process and actually show you what the user experience is. And as a side quiz, why don't you try and count how many times I is engaged during this? Because once I started noticing, I started counting every time. Meets John Smith, a U+ Insurance customer who just had an unfortunate accident whilst driving on a country road. He collided with a cow that suddenly crossed his path.

His car sustained significant damage and needed to be towed. Thankfully, no one was injured, not even the cow. But John needs to file a claim quickly. Like most modern consumers, John prefers self-service options. With the U+ mobile app powered by Pega. He can file his claim immediately without calling an agent. John selects his policy, initiates a new claim and enters the accident, details date, time, and that his vehicle was towed. He also notes that a police report was filed due to the involvement with livestock. John uploads photos of the damage and the police report directly through the app Before finalizing his claim.

John has a question are accidents involving animals covered by his policy? Rather than searching through policy documents or calling customer service, he simply opens the in-app chat assistant. This chat assistant is powered by Pega Knowledge Buddy, which has been trained on specific policy documents and frequently asked questions. John asks if accidents involving animals are typically covered. The assistant responds that such accidents are generally covered, but asks for more information. When John explains he hit a cow that crossed a road. The assistant provides specific information about coverage based on German insurance regulations, which distinguish between different types of animals. The assistant even provides a link to the relevant policy section for complete transparency. Reassured, John submits his claim and receives a confirmation with his claim number.

At Uplus insurance headquarters, claims adjuster Michael sees John's new claim appear in his work list. In the past, Michael would have needed to read through all the details. Police report the uploaded images to understand what happened today. Michael uses Pega GenAI Coach, which instantly provides a comprehensive summary of the claim. The Coach highlights the fact that this is an animal related accident involving a cow, which requires specific policy verification instead of manually searching through policy documents. Michael asks the internal Knowledge Buddy are accidents involving cows covered under John Smith's policy? Knowledge Buddy immediately provides the answer yes. Collision with cattle is specifically covered under section 3.2 of the policy. This policy covers collisions with game animals as defined in paragraph two of the Federal Hunting Act, as well as horses, cattle, sheep, and goats.

The system also displays the source documents, allowing Michael to verify the information directly. This saves valuable time and ensures accurate information. Michael notices a police report is attached and he asks GenAI Coach if the police report is complete. The Coach identifies that the farmers contact information is missing, a detail needed for full claim processing. Michael then asks for recommended next steps. The Coach suggests sending an external adjuster to assess the damage. Providing guidance to John regarding the farmers potential damage claim and requesting the missing farmer information. Now Michael needs to communicate these steps to John rather than drafting an email from scratch. He asks GenAI Coach to generate a communication.

The system quickly produces a professional email explaining the claim status, the need for an external adjuster, and requesting the missing farmer information. Michael reviews the draft and asks Coach to make a small adjustment to clarify the animal coverage specifics, and sends the approved email to John. Michael now needs to arrange for an external adjuster. While insurers like U+ normally have standard processes for such a scenario, we want to show a different approach using Pega's Agentic AI capabilities to launch an ad hoc case using this ad hoc type capability. Michael simply instructs the system to organize an assignment of an external adjuster for vehicle damage assessment. The AI agent automatically creates a structured workflow with the necessary steps, ensuring nothing is missed whilst maintaining human oversight throughout the process. This approach also ensures that Agentic actions are audited and will only act within well-defined boundaries. As the external adjuster is assigned. Pega Process AI alerts Michael that there is a 98.63 probability of missing the service level agreement for this claim with Pega GenAI capabilities.

The prediction has also explained in an easy to understand way to Michael. This is based on historical data showing that claims requiring external adjusters typically take longer to process. Other factors influencing this prediction include the age of the vehicle and the complexity of the damage. This early warning allows Michael to set appropriate expectations with John and potentially take proactive steps to expedite the process. Later that day, Michael needs to prioritize his caseload instead of running predefined reports. He uses Pega GenAI analyze to chat with his data. After opening a predefined report of all open claims, Michael asks for cases in status open and filtered by motor Insurance then refines only show claims older than five days. And finally filter to show only claims with breached SLAs. In seconds, the system narrows hundreds of cases down to the six most critical ones, requiring immediate attention, allowing Michael to focus his efforts where they'll have the greatest impact.

Meanwhile, John has received Michael's email and called us with additional information about the farmer. Customer service representative Sara answers the call after verifying John's identity. Pega next best action immediately suggests that this call is likely related to John's recent claim, and provides a direct link to open the case, ensuring Sara has the right context from the start. Sara listens as Michael provides the farmer's details, name, address and contact information as John speaks. Pega Voice AI automatically captures the farmer's information and populates the appropriate fields in the claim record. Sara can focus entirely on the conversation rather than having to type notes. John also has questions about the external adjuster process, which Sara answers. She promises to follow up with the adjuster to ensure a smooth appointment scheduling process. After the call ends, Sarah would typically need to summarize the conversation and create follow up tasks manually.

Instead, Pega GenAI automate instantly generates a comprehensive call summary and automatically creates a task for Sarah to follow up with the external adjuster. Sarah quickly reviews the AI generated summary and approves it, ready to take the next call without delay. Throughout this claim journey, we've seen how pega's gen AI capabilities transform the insurance claims process. Together, these capabilities help make every employee your best, enhancing both customer and employee experiences whilst improving operational efficiency. All right. So I came up with 9 or 10. If you count the AI picture of the cow as using AI, I think it's 9 or 10 in there, and it just really blew me away because I thought the ad hoc case was really the only thing that we kind of added to show you something special that you wouldn't need in just a normal fnol. So it's amazing how much I can be assisting people today. This isn't the future.

This is now, right? So you saw channel a seamless transition across channels. You saw coaches and buddies. You saw predictions. You saw the customer service agent being able to capture information from the voice, and the voice was putting it directly into the system. And you saw those next best actions and the and then the summary at the end. Right. There was a lot of things going on. And I love Seattle, and I love seeing how Pega can affect end users, both customers and the end users of the system.

As someone from the customer service world, I know how valuable it is to have a single system, or even just a few systems so that I can focus on my customer that I'm talking to and not worry about where to click next. So I think it's awesome. So I think it's obvious that Anu and I are very proud to be a part of Pega, and we are very excited about being a part of what's going on here in 2025. But we're not the only ones you see, Gartner and Forrester recognize Pega and key areas and real time interaction management, customer service automation, low code capabilities and then our industry leading pre-built solutions like sales, automation and others and everything we've talked about here is available for you to go look and see and demonstrate and discuss. Go see it in the Innovation Hub this week, right? Go run a Blueprint. There's a great customer service agent in the customer service simulator. You get to go be an agent and you get to pick whether you want to talk to an angry person, a happy person. You can also try and be the world's worst customer service agent and get the lowest score of the day.

Or you can try and be the best customer service agent, get the highest score. But it's a lot of fun to sit in there, and I recommend you grab a colleague to go do that with so you can compete on compete with that one. That's a lot of fun. And um, and talk to all those Pega experts and all of our partners that are so passionate about their jobs and the technology. They really love what they do. And that's an amazing experience down there. So all right, well, we're at a time I think we have eight seconds left. But thank you. Thank you for joining Jeff and I today.

And we're going to stick around if you have any questions. Thank you.