PegaWorld 2026: From Content to Action: Enabling Conversational Journeys with Pega’s Agentic Fabric

Please take your seat and welcome to the session of today from Rabobank from Content to action. We are going to address one of the questions that I think is the most pressing these days for organization, which is all about how to move from experimenting with agents in gen AI into changing operating models at scale and safely. Last year, probably some of you were here in the rooms and you listened to the wonderful story of how Rabobank implemented Knowledge Buddy within their financial economic Crime unit. Today we're going to basically take it forward and see what they've done and what they're looking into doing for the future. So with any further ado, just a tiny housekeeping. Before we start, we have microphone stands in the middle of the aisles here. Please do take notes of your questions. We will have time for Q&A at the end of the presentation.

And now please join me in welcoming on stage Ralph, our tech lead at Rabobank. Yes. Hello. Thank you. Our principal system architect. Thank you very much. You're too kind. Um, yeah. So thanks for the introduction, Chiara, and welcome us on stage. Um, and also for the people coming in. Welcome. Um, thanks for being here and listening to our story. Um, yeah, it basically is a story of, of Rabobank and what we did in the last couple of years, also with AI, with Agentic. So Jeff and myself are going to guide you through our journey together. So, um, hang in there. I think it's a good story. If you have any questions. Yeah. Feel free. Uh, that's also what I'm here for. But to start off things is maybe, um, with the show of hands who already knew Rabobank before stepping into this room. Okay, that's less than half.

Okay, then it's good that I have at least a slide about Rabobank because we have some things to do then internationally, I think. Um, but the Rabobank and basically in orange you see the Netherlands, small country, Europe, and in the Netherlands you have three large banks and we usually state is that it's three big fish in a small pond basically. Um, so we are an all finance bank in the Netherlands and um, we try to be on top of things, but internationally, we focus on food and agri, and we focus on innovating food and agri specifically. So that's what you see on the right side of the page is we do at least what we do internationally is that's mainly focused on the Americas, Australia, and mainly Europe. Then. Right. And the rest is the old finance bank in the Netherlands, basically. I won't bore you too long with that.

But to give you an insight also in what we are doing. So we have for a number of years a mission statement which is growing a better world together and growing a better world together is a bit of a large statement, right? But what we mean with this is that we try to do things. We try to do the good thing, be it in food energy, be it for our customers to make sure that they they thrive also. And we try to do that every day, every week, every month, every year. And while doing that is we move ahead. And with doing that is we make a better world together. That's basically our mission statement. I won't bore you with the rest of the slide. If you want to look at it, just take a picture of it because I'll skip to the next slide pretty quickly.

But we try to do a lot for our customers. Food and agribank in the middle for what we do in the world. I think that's those are the most important parts. But what we came here for is, of course, Pega. We do more than just Pega within the bank, but Pega is one of the stable technologies which we've been using for years on end already within Rabobank. And we started off in 2000 already. That was still on Pega five. I don't know who worked with Pega five in this room. It's just a handful of people. Yeah. No. Yeah. I also wasn't around then yet at Rabobank, but we started with payments. Payments exceptions. Then you see how we skipped a couple of years to 2013. That's basically where we started with business lending and housing. Um, and that was really a big step for Rabobank is what we tried to do there or what we did there is basically make sure that, uh, we made sure that we, uh, that business, that companies could, uh, buy their financial products online, which in 2013 was a really big step for Rabobank.

And that's really where we started to use also the Center-out principle of the Pega Platform, basically. So our goal there was, for instance, for example, is that, uh, the screens that a customer would see were also the screens that an employee would see while servicing the customer. So in 2013, that was already, um, for us, uh, very innovative. Well, we did a number of things. We started off a couple of things. So DLL, which is a daughter company of Rabobank with vendor finance, KYC is, uh, a lot of things there, as you also saw last year is that we had, uh, from Fech financial, economic crime, KYC, know your customer, uh, a presentation also on the main stage, which, which was really nice, but skip to 2017 is that's when we adopted PegaCloud. Um, so everything before that, that was basically on premise and everything after that is mostly PegaCloud.

Um, and we didn't transition any of those applications from on prem to cloud. We, what we did is just start greenfield in the cloud and keep all of the on prem stuff on prem. And that gave us a bit of a boost, um, that we didn't have to worry about the transition itself. So that really sped up things. Also in our, for instance, online journeys, which we, we all did with, uh, um, with all of our, uh, for all of our customers. Um, yeah. And skip to 2025. Last year is every year is we host a proud of Pega event. So to give you a bit of a scale of, of things within Rabobank is we have around between 600 and 700 Pega employees or people working with Pega within the bank. So that's a lot of domains, which we, uh, service also with Pega.

And that shows also the, the success is that we started really small and really automatically became bigger. So that was good. And at the final part. So last year, Alan Trefler was also in the Netherlands, and we celebrated our 25th 25th anniversary working together between Pega and Rabobank. So that was really a milestone. Um, to do so a bit of background on what we did, um, within the bank, but our story is about AI and agents, right? So let's skip to that part. And Jetson is going to tell you a bit more about the details of what we did with all of the genetic stuff and what our vision is. But to take you along into that journey is what happened in the last couple of years. And what you see is in we start in 2024. So like two years, three years ago is everything that was AI was basically scary for us.

And I think most companies struggle with that, right? We didn't want to directly put that live towards customers because we didn't know what the heck it was going to do. And still, sometimes that's a bit of a question mark, but that basically was the story of our lives at that moment in time. So while we gradually move forward in 2025, as you see that Gen AI was basically adopted a bit more. But the our squads and our solutions were basically very specific. So a team would have a very specific gen AI solution for a very specific problem, which wasn't scalable, because then you would invest quite a lot of effort in creating such a GenAI. But then only one team or one person or one customer group could actually start using it, right? So we want to scale things basically. So skip to 2026 where we're now trying to do that.

So to standardize all of these AI solutions so that they're also, uh, far better, uh, shareable between not just teams, but also between applications, which we have and also focus on our SDLC software development life cycle is to make sure that not every team is figuring out, like we saw this morning, for instance, with the test automation part with AI or with genetic stuff, is that they're not doing that on their own island. But we try to do that for the entire organization so that all of these 600 developers Basically get an opportunity to work with the same tool all at once. That's one of the goals. Well, and then skip to 2027 because we're in 2024. You see that GenAI was scary at this moment. If you look at Agentic, for us also, that's perceived still as scary, or at least we don't know what it's going to do.

So we don't or we're reluctant to give that to our customers. Right? Because once you customer chats with an agent, you don't know always what it's going to do, right? It might execute a Pega use case within, for instance, the banking app, which is great, which is good. It will guide you through that entire process. That's nice, but maybe it will do something else. So Jeff Akin is also going to explain how we solve that issue basically, but our vision also for the future 2027 and later is we're focusing on, at least for every squad have one agent. Could be anything. Could be some something customer related. Could also be something more related towards the software development lifecycle again. So for instance, having an agent in your team who is doing the work of a teammate, basically. And what we've seen already is a nice example is we work scrum, we work agile.

So, um, also within POCs is people also invented that. You can also have an AI scrum master for instance. So it will just ask questions. It will tell you things, it will ask you things, right. So it's, it's really fun to see is what that actually can do per squad. So our vision basically 2027, 4600 developments per squad, one agent per team, which we have. Okay, um, to take you along. And this is basically our software development lifecycle because a lot of focus is always given on the customer centric part of what Pega can do, or what agenda can do, and what kind of value that it can deliver, right? And what I try to state is that it is definitely still for Rabobank scary to just open up agents for customers, etc.. So where we do feel is that where we can automate or use a GenAI is basically in our software development lifecycle, and we had a bit of a chat or a bit of a think about this with a number of architects.

And we came up per phase with some ideas on what to do in each state stage of the software development lifecycle. So for 2027, and this is basically the key, there is every squad is trying to figure out, okay, what will work for us best, right? And try to figure out, are we going to do that? So I see a lot of people taking pictures. Yeah, this is great stuff. If you look at this, um, at home and try also within your organization to just have a look at what can we really leverage from these ideas, right? Is to just figure out, will this work for us? Um, and it might not, it might work. Uh, so that definitely is, uh, a thing to look into. One of the key things I still see some cameras. Okay, cool. Um, but to hand over also the story a bit towards, towards yet a bit more content on what we actually did.

Also with, uh, Agentic and with GenAI is we adopted the now new next, uh, methodology, right? Where are we now? What's going to be new and what's next? And you basically see also, um, a couple of things here already, but, um, we're now in the now part, and I'm going to move over now to Jetton is to share what's there in the next part. Yeah. Thank you Ralph. Uh, good afternoon everyone. Uh, so to take you along on this adoption, how we see it at the bank broadly, not necessarily just for Pega, but for across technologies, across platforms. The last two years, um, since GenAI has been a thing since 2022, there has been quite a initially a ban. And then, you know, things started opening up at the bank and that's when, um, the now phase where AI is an assistant has been experimented with for different kind of personas.

The part that just Ralph just spoke about is more about in the DevOps space, where you have all sorts of assistants for the developer community. Um, again, not necessarily just for the Pega Platform, but broader. But coming more to the business use cases, um, you probably would have heard the story from our financial economic crime last year where they spoke about an assistant, we call it Knowledge Buddy, which is an assistant to the, uh, agents in that space to help them out, figure out the information, uh, look through the instructions, etc.. But the same concept has also been applied in our customer setting, where in the past, uh, before we had this assistant in place, which we call it Robin, you'll see it in the follow up slides. Uh, Robin has become much more smarter with the information it has been fed up with. And based upon that information, what we have seen over the years, last couple of years, is that the customers are able to get answers to a lot of questions that they are seeking some information about, not necessarily want to take an action, but want to know what can be done if I have to block my card.

And then Robin will spit out the information in the chat and then the customer knows, okay, this is what the process is in case I need to use it or something. So that's something which has been working pretty well. Uh, what we have noticed in the last 2 or 3 years, but as you would notice, we all use GenAI nowadays for every little thing, more or less. This is very much passive in nature that you ask a question, you get a response, you ask a follow up question, you get a response. But that's where the next phase of GenAI, which is the agentic aspect, is what, uh, can bring more value is what not necessarily we see as a bank, but it's all over the place. We heard it this morning. It has to become better than just answering the questions that it does for now.

And what could that mean? That you have an agent in place which is able to take some instructions, figure out the things on its own. Still have a human in the loop somewhere, either internally on the bank side, or maybe customer could also be a human in the loop here to be to feel more in control of what he or she is doing. Um, and that's where we see that could be the logical next step of this adoption, where in a, in a, in a small setting, in a safe setting, we can bring those experiments into play for employees, for our customers, but maybe for our, uh, developer population. That's something we have to see. But more interestingly, interestingly, for the customers and employees, and that's something could be our next logical step and the next phase, which is hard to say anything about right now.

The multi-agent ecosystem where you have tens of agents running across talking to each other based upon, uh, a task that has been assigned and they figured it out themselves. So that's something we think is still a few years away, because we really need to get more confidence with the technology. And the phase in the middle is the one which can help us get there. So for instance, the one on the extreme left on the now phase, that's not a question anymore. Of course, the answers are not perfect all the time. We heard this morning from from Mart, uh, in one of the panel discussion that we're still working on, how to make that better. The answers, but it will come there eventually. And the same goes with the new phase where we need to get more out of the experiment phase, bring things to production, get used to them, and then start thinking of, okay, how the next phase could look like.

Um, so once we move on there, want to take you on what we did. Um, before we share in the agentic space, the actual experiments that we are doing, which we're going to go to production with. There's something we think we all will recognize as organizations, as enterprises where we're coming from. So from as a bank, we are coming from this place where we are highly dependent upon the branches. Everybody would walk into the branches probably a couple of decades ago. They're still there, but not as much. And moving to the app space. So the center of gravity, if I may put it that way, has been changing now because in the past, if you move into a branch, there is somebody at the bank and a human agent or someone there who's working it out, who knows all the systems, who knows all the applications.

You have a request, they'll do it for you. Moving to the right side, which has been the focus for the last couple of decades now, especially with the use of platforms as well. There has been focus on digitizing as much as possible to bring everything or as much as possible in the self-service space so the customer is more in control. But you still have a human who has been figuring this out what he needs to do. Where does he need to find that and run through those processes, those journeys? If not. Then call the the bank and then they'll probably help help you out. But again, also digitization for the customer in the app. The same goes in the in the middle office, back office, where you have the employees who can do it on behalf of the customers, again, through the apps, through multiple apps, etc..

So this is not new to anyone. However, this is where we think we are right now. So there has been a lot of investments made in the space where we have built like tens, if not hundreds of journeys, uh, which have been now live through our native apps, uh, a lot of them being built by Pega. Uh, in the past, we used to think, okay, what would Center-out mean? Also Ralf mentioned, of course it has to do with the customer sees the same screen as the employees, but it's also about. The customer is able to. We just define the journey once and you can just render it in any channel. And that's what we have done in the past. So to have the same app running through the, uh, through our website, the same app, the same feature, or the journey available in the app, and the same is available to the employee.

And that's where we thought like three, four years ago, that's where it's limited to. And that's what Center-out is. But now what we have started realizing is what if, if the same journey can be exposed on a channel for conversational or voice, and you don't have to really change a lot. So that's where, um, we start seeing a bit more the power of this architecture or that what it can really bring for you, because then you don't really have to define it for a different channel and not just the channels which are visible, but the channels that you can talk to being a conversational and voice. So that's something we thought, okay, that would be good to, uh, start doing some experimentation with. And in the meantime, last year, uh, at PegaWorld, we came across the Pega Agentic Fabric, which we thought, okay, this is something that could really play a major role in, uh, in this whole agentic setup that we can envision, uh, which we want to start experiment with.

Um, so again, moving from the apps to agents, uh, as you can see, the, the role of human from the very first one is where you always had an agent or an assistant sitting in the background, especially on the employee side, which is helping the employee, uh, while the customer is on the phone, uh, maybe the customer has an access to an agent as well or an assistant while being on the phone with the employee, uh, for, some use cases that has been slowly shifting now towards, uh, where AI as a proxy customer. And what I mean by that is, um, uh, the customer just asks a question and let the agent towards the bank figure it out what it needs to do. And, uh, again, the human in the loop can still play a role to, to help out that agent approve something in the midst of that process.

But again, this is like an agent sort of taking over and a human is, is helping or assisting that agent to, to do it correctly. And finally towards the autonomous customer. Again, that's the next situation, the next situation where we need to know, um, how that's going to look like. So that's going to be an interesting space in the next 2 to 5 years, probably where it becomes really autonomous, where you have a very less amount of human intervention involved on either side, be it customer or be it at the bank. Um, but again, it's not going to move that fast. So we still have to, uh, make sure that we still have branches in place. We still have the apps or the features which are part of the apps in place, whilst we are also serving the same capability to our Gen Zers and the people who are more tech savvy, who want to do more conversational way of banking their affairs.

So the three ways of doing things will still co-exist for for years to come as what we see, uh, which gives us also time to, to adapt and see how the technology is evolving and at the same time try to bring that, uh, gradually to, to, uh, to our customer base. So these are some of the strategic pillars at Rabobank again are more conceptual but very powerful as well that we really need to focus upon. We're not going to go into details of each one of them, but the first one being the excellent customer focus, where there's a high focus on making these customer journeys that we have made over the last few years, being available across channels like conversational and voice. And the idea is, okay, how with the least amount of effort and in a very safe setting, in a, in a compliant way, in a secure way, in an auditable way, we can bring those journeys to our different channels like chat.

And that's where, uh, with the AI powered virtual assistants, something that we tried, uh, where we have our chatbot called Robin, uh, where in the past it used to answer the questions, if you have any, as a customer and just tells you, okay, this is where you need to go and good luck from there. And you're on your own still. And that's something we tried, which will show in a video in a minute, is you you really need to talk to it that, hey, I want to block my debit card. What do I need to do for that? It will give you instructions. This is how you can do it. And it can also you can say, okay, can you help me doing it? And it will just execute the action for you based upon what's defined on the journey. So that's something that we have been experimenting and also bringing to production just some insights into what, what that Robin chatbot is.

So it's not just something that, uh, it's fully in the experimentation phase. Like I said, um, the, the chatbot has been there for a couple of years now and it has just been getting better over the last two years in answering the customer queries. And that's something we figured out with the amount of chats that we had, uh, in 2025 and the number of non escalated chats in the in just one month in December was lower by 7%. So that's something that's keep getting better. That means the conversation has been becoming more effective over time. And it is actually helping the customers who are seeking some answers or who want to perform an action. The last part you will see here, uh, I would want to emphasize upon the end to end integrations. And that was about, um, we, we brought a couple of our use cases of blocking a debit card or changing a daily spending limit through our chatbot as well.

So it's not just about answering as you see in the, the pictures above, but this is also about performing some actions, but these actions that you are seeing, uh, which were just a couple of examples, these were not Pega processes. These were just scripted processes that really look at specific keywords and figure out, okay, if a customer says, increase my limit, what does it need to do? Just ask for confirmation and probably update a couple of systems, the system of records, and that's it. Again, this is something we think it's not sustainable because you're not going to script each and every use case there and um, have a, have a life cycle of its own. So you have a journey defined somewhere in a platform like Pega or somewhere else and have a scripted version of that journey somewhere triggered from the chatbot. So that's, that's not really Center-out how we see it.

So this is something we thought, um, we should try for the journeys that we have defined in Pega. So we have a journey for a customer to change the address when they're moving house, which has already been in production for a few years now, where you just log in to your app, you go to, um, you find a feature that you want to change the address, put your new address and it will ask you a few questions. There are a few screens. You fill those up and you submit it, and it will be go through fulfillment in a couple of days and your address gets changed. So what we thought, okay, let's how about we try this journey from our chatbot to see if the, the, the actual agent that we have, the user is talking with, if that can basically perform that journey on the chat and, and basically, uh, take that action instead of just telling the user.

So for now, the behavior is, hey, I want to change my address. How can I do this? It will figure out this is the link to the feature. Click on this feature. You go there and you fill up the form again. And then you continue from there. So it's more like um, there is a bit of a friction there. So what we thought, okay, let's try exposing this journey through the chatbot. Uh, so we try to put the Pega Agentic Fabric here, the component that we heard about this morning. So this was the very initial version that was out. And I've heard in the newer version, there are a lot more capabilities to something that we are very keen on trying out, but that's something we integrated with our Copilot Studio, the AI agent platform, which figures out the understands the intent of the customer customers queries, extract the entities, does all the checks, etc. before it routes to a component like fabric and fabric will figure out if there is a journey for changing a dress that I know that I can serve the request with.

So you just put a couple of processes and see, okay, can I fabric figure it out? So by putting some set of simpler instructions there, it was able to figure out the the journey and start giving the right responses back. Like we had the three screens defined, it will start pushing back all the responses to ask the customer on those to get the input on those, uh, uh, that information that is needed to perform on the journey. Um, so with that, we'll move to a short demo. Thank you. Customers today sometimes struggle to find the right journeys in the app, like updating an address or checking the status of a request. When they can't, they turn to Robin. Our chat bot for customers and Robin only provides information based upon the knowledge sources and points them back to the app with the link to perform an action, often forcing them to contact the call center.

This creates friction due to channel switch, higher contact center load and not a seamless experience. with Agentic abilities. Robin understands intent and adapts. Recognizing whether a customer needs information or wants to take an action. Robin then either provides the answer or guides the customer through the process, confirming completion. All inside the conversation, Robin keeps everything in one place the conversation while reusing existing journeys without rework or custom scripting. That means excellent customer experience, scalable self-service, and lower contact center load. Robin. today informs and directs Robin tomorrow, understands intent, guides customers through the process and keeps them updated. This is the future of customer experience, taking self-service to the next level in a safe, guided, and controlled way. So I think the last part was very important to do it in a safe, guided and controlled way. So what, what, how this helped us is to basically whatever is defined on the journey that's being made, sure that everything has been taken into account when the customer is performing.

So that is where we are looking at how we can take our next steps to bring not just the first journey, but all these journeys that we have built over the years, gradually to our chatbot when it comes to customer, but also when it comes to the employees, the internal users, to roll those journeys out as well. To them over. The chat bot. If there's a need to be quickly. The kind of risks that we think of moving too fast. The reason we are being extra cautious is that we don't want. We can't risk it going wrong. Being a bank, very regulated industry or something, we need to make sure that we do it right first time. There's no room for going wrong here. Um, but at the same time, waiting for things to mature too much is also brings the risk of falling behind.

So that's something, um, we feel that we really need to move in the direction where we, uh, like I said, in a safe setting, in a small setting, try to start experimenting, bringing these kind of journeys live and start gathering some feedback, some experience there in order to, to have the learnings. And again, also look at the product, which direction it is heading into to, to leverage from those newer capabilities as well. Yeah. So the goal is to be innovative. We think this is definitely going to be the game changer in terms of the technology for the next upcoming years. The autonomous. The fully autonomous parts is still years away, but we need to get comfortable with the part in the middle that you saw in the earlier slides that we need to, um, start rolling things out in production and get some experience with, but at the same time, be more responsible, uh, before bringing any such capability to the customer or to the employee for that matter as well.

Be more human centered and inclusive. So it's not like this is the only future. It is going to coexist with the branches, with the features in the app, but with the chatbot and the voice bots. Uh, so at the same time, the this is going to coexist for a while before we are fully there. Um, and with that, Ralph, uh, I'll give it to you. Back to you. Yeah. Thank you very much. Um, yeah. So final two slides, uh, for you guys, uh, with a couple of results. Uh, which we got from well, in this case, uh, the fact is that we have Robin, and Robin is learning and Robin as an agent can also call processes. So that's really nice is you. And this is also basically what you would expect it to do. It initially had a bit of a wow factor, right.

Wow. Can it actually do that. Yeah it can. Great. But then also you should deliver because if you make one error, then people will think, ah, it's trash, right? So that's really the, the tipping point where you're looking for, okay, it must be good. So we really see is that we have higher customer satisfaction. It reduces some complexity. The NPS score goes up. That's really nice. Uh, and for us, also in the middle on top is it's important because it really helps us also to gain new customers, which where we have a focus on. So at Rabobank is we try to focus on an age group from 0 to 30. At this moment, these kind of things are really helping also to explore how to expand also there a bit. So it really is helping us also into into the future. Then the final takeaways from Jette and myself.

And these are just in random order. But you see on the, the bottom part where Jetton also was stating, right is move too fast, move too slow. Well, at least you need to have a view on what responsible AI is or what your take is on what responsible is for your customers or for your employees is to do that transparency, privacy, security, governance. I think this will resonate to, to everybody. Um, but if we go up is, um, I think a couple of learnings. So what we stated is do not just focus on customers. I think the SDLC is also important to make speed, but also confidence is key there because if you expose it to your developers or to internal employees, like functional maintenance teams or, or have people doing due diligence requests within a bank, that really also sparks some confidence in, hey, this actually works, or we can start using this.

So I think that really is important. Then the second point, incorporating with these stupid robots like we stayed is because Robin is just a robot, which you feed information and it will share that towards a customer, but really enabling the authentic journey there is helping it really progress into the future. And we're good with having a robot talk to people, but not an agent, right? Where we started also this presentation on, um, we don't want to just chuck an agent towards a customer and then hope for the best, right? So I think the route to incorporate it right now with an agent, I think is good in the future, that agent needs to go right somewhere down the line. But we also don't know where we don't know when we're not comfortable at this moment with getting that out of there. So we're taking it also at a progressive pace that we think is good.

And then the final part is how the human in the loop that keeps on popping up. I think that's also important to, to consider and not just to blindly trust on an agent or a robot, but to at least for certain processes, is to make sure that at least you have confidence is that you can actually rely on these, uh, these things. And with that, I want to conclude, um, so thank you very much for your time and attention, and I hope you still have a great day here at PegaWorld. Thank you very much. Thank you guys. Thank you everyone. So if you have some questions, please hang around. In the meantime, I'm calling on stage my colleague from Pega Scott, because we have a little something for the great journey that Rabobank is doing. Is it's working okay. Um, yeah, I just wanted to.

And if I could call up your colleague Mark as well, if you could come up while I'm introducing this and do we have a slide we can move? Yeah. Um, so my name is Scott Merritt. I'm the Blueprint delivery leader here at Pega. Um, and my team is responsible for kind of writing the playbook. A playbook that acknowledges that this AI infused SDLC is dramatically changing results and the outcomes that we're delivering. And a lot of the new technology that we heard about today from Kerim, especially Blueprint and Infinity studio, all of the new technology is bringing to us challenges, challenges that demand a playbook, a prescriptive way of delivering differently. And as part of that, technology alone isn't isn't enough to achieve these game changing outcomes. You need game changing teams. You need innovators, you need groundbreakers. You really need teams that want to innovate.

And as we think about Rabobank, Ralph talked about the 25 year history, that entire history, we have kind of been side by side with this team, innovating, challenging one another. They're leading our product strategy. We're pushing product strategy to them. And so we've kind of kicked off a new award this year. And really the spirit of this award, it's called Blueprint Delivered Award, but it's really about challenging yourself and your teams to deliver differently. And we wanted to start recognizing clients that are delivering differently, but not just delivering. They're actually taking these new technologies and changing their process and actually achieving those game changing outcomes. And I think you saw Agent Robin is a testament to that game changing outcome. And you saw Ralph's SDLC, that nice Infinity diagram that had all the different ways that they're challenging, the way that they work and how they could deliver differently.

And then I bring up Mart as well. Mart actually had a challenge and I won't go into all of it, but they had a project to kind of reimagine an application. It was initially estimated to be 18 months. This team challenged the way they worked, the team that worked it, the challenge. They brought our new technology to the table, and I believe they were able to deliver and reimagine that application in 100 days, from 18 months to 100 days. And that's really the spirit of this Blueprint delivered award. And I'd love everybody in this room who has a challenge and delivered differently with these Pega products. We'd love to have all of you stop by the booth, tell us about those journeys. And these are available for everybody that has achieved an outcome like that. And so I wanted to present that to this team while I had them all together.

And so congratulations guys. Thank you very much. Thank you, thank you. Thank you very much. And I'll get off the stage as they go through some Q&A. Thank you. Can I can I say one thing. Yes. Go on please. Oh please. Yeah. Because I, uh, I feel honored by this award, but I miss one person here and that's Marco Duizer because he helped us with this from Pega. But he's in the he was in the hospital, but it's going well with him now. But he was really the guy that stands next to me and made this possible. So Marco, if you hear the replay or whatever, thank you. Thank you. Well done. So open for a couple of minutes of Q&A. You're welcome to stay if you like. Please don't be shy if there's anyone. Walk to the microphone. Otherwise. There we go. Please. Sorry.

The light is right in my eye. Is this on? Hello, my name is Forrest Hoffman. I'm from Bank of America. So I think what you've done is phenomenal. And I think the rate of change is just ever so evolving. But our clients don't always understand that. So I was looking at some of the stats you had around the chat Voicebot. Robin integration and um, some things like, you know, increasing chat rates of over 25%, um, bringing the chat versus call volumes increasingly high, but still seeing customer satisfaction scores, um, that were in the middle had room to improve. So how do you change or how are you changing the messaging on encouraging users to use the chat bot if they used it six months ago or a year ago and had disappointing results? Um, we know you're actively improving the tool. How do you change that client perspective?

Yeah. So I think that's a really good question. Right. Because the clients right. If they have a bad experience once or twice, right. In terms of what they're looking for, they don't get it right. They don't use that channel. Right. So that's really hard. But over the period of time, right, it has become a bit of a natural thing for customers as well. Right. To try out because that's the quickest way of, you know, checking if they get an answer rather than waiting for a call center or ten minutes on the phone. Right. So that's a way in which it is automatically sort of helping out. And if they're getting a better response, right. Then we also learn from that. But at the same time, um, there's a lot available now information on our channels like websites and all right, that this is something the agent can help you with.

So you should try people who have not tried that because sometimes it's a, it's a natural tendency for people to just call the bank, right? Instead of. So that's also the way. So to educate the users a bit more in that space. So it is quite a quite a journey in that sense. But the part that Ralph mentioned about the 0 to 30 age group, right, the Gen Zers also, that's a very, uh, they are very keen on using these channels. So that's where they are using it a lot, uh, compared to the older generation. So that's where we have to find the right balance and gradually bring people on board that they can do a lot more on channels like conversational and voice. Thank you. All right. So I believe I have a red light here and time is up. Thank you, everyone for sticking around and for being an amazing audience.

And I believe the team here, uh, welcomes any further chats. Right. Thank you everyone. Have a nice rest of the day. Thank you very much.