PegaWorld 2025: The Future of Customer Service 2030: Finding the Missing Links in Your Path to Autonomous Service

PegaWorld 2025: The Future of Customer Service in 2030 – Finding the Missing Links on Your Path

We're going to talk about the future of customer service and the the missing links in your path towards becoming an autonomous service business. We are genuinely thrilled so many of you have made it out after the keynote. So thank you for joining us. Um, my name is Simon Thorpe. I lead product marketing for our customer service and sales automation business. I am a customer service guy through and through. Nearly 25 years of managing contact centers and managing customer service programs.

So this is very, very close to my heart. And um, I hope, like me, you've been inspired by what you've seen so far at PegaWorld. It's been incredible. The innovations, the success stories. Truly, truly breathtaking. Um, which is a good segue for me because I want to, uh, introduce someone that I think is pretty inspirational. Um, not to give him a big, uh, a big, a big introduction, but Matt Lake he heads up the product team. So all of that awesome stuff you've seen in the Innovation Lab.

Matt is the arbiter of all of that cool stuff, so give it up to Matt. Matt, how are you doing? Thank you very much. Yeah. Thanks, Simon. That's a that's a great intro. I would love to have you follow me around and just give me that intro everywhere, I appreciate it. Um, it's great to be here with all of you.

I've been, uh, having a having a great time, uh, the innovation hub, seeing the customer simulator, seeing all the great technology and watching everybody kind of get to experience it for the first time. It is a little daunting to go see Kerim do a live demo of the product up there. It makes my my blood pressure is just coming down from that. Um, it's also a little tough to follow Rob Walker and Don Schuerman talking about autonomous, but we're really excited for it today.

I think on my side, my goal for this is really talk about some of the things we're thinking about, our vision for the product, and also hopefully give all of you some guidelines, some roadmap, some some things to look forward to and some ways to plan your own vision at your company. Looking at what are the metrics you want to improve? How do you iteratively get there? Um, thinking between that distance between now and ten years from now. All right.

So before we get going, we will have time for questions at the end. And we'd love to hear from you all. When you're talking about a topic like the future of looking 510 years in time, none of us really know entirely what's going to go on. We've got a pretty good idea, but we'd love to hear from you all.

So we have microphones here and here, and our colleagues at the back have, uh, have recommended you come up at the end of the session and please get very close to the mics, because we'd love to hear from you. And we're videoing this session, so I'm sure you'd love to be part of that. All right, Matt, let's kick off. Let's take a look at where we think the customer service industry is headed. And I'd be a fool not to say that AI is going to shake things up.

We've spent the last two days seeing all of the many innovations and incredible things that are coming, and that is going to be very, very significant, we think, in the world of customer service. But what does this autonomous future look like when we really narrow in on the customer service world? Well, we think there's going to be an increase in AI driven interactions. You know, we've got used to the many years of the traditional human to human interactions.

We think that's going to be largely replaced by AI. We certainly think we've got to be deeply focused on enabling our people, whether they're in the contact center or in the back office, if they're in a front facing, customer facing position, we've got to be thinking about how do we enable them to work in a world alongside AI, to really dig into and deliver the value that that promises? And we also think there's going to be a big, big push to modernize.

You've seen a ton of stuff over the past couple of days about legacy transformation. We think that is going to be key critical in the world of customer service, because if we're going to really get the fruits of AI, the legacy really isn't going to get us there. So just to give you an idea, and I'm sure some of you might have seen this statistic a little scary, particularly for an old call center guy.

But Gartner is suggesting that even by next year, even by next year, I could save enterprise organizations billions upon billions in human labor costs. That's massive. That's absolutely game changing. It really is. So the questions we've got to ask ourselves, I think, are, are we really thinking about hiring new CSRs or customer service representatives and back office staff over the next two, five, ten years?

That's a pretty contentious thought, but we'd argue, certainly from the conversations Matt and I are having, that most of your competition probably aren't. We're also deeply, deeply focused on how do you start getting your people that are working today fully enabled and up to speed, to live in that harmonious life with AI? Because that's here right now. You've just seen Iris on the keynote stage. Every single one of the Pega team are using Iris. 30, 40, 50 times a day.

But are we tackling the human elements of fear and concern? Are we bringing people on that journey so that they can really, really get the benefits of what the AI future could bring? And we also are investing in scalable solutions and self-service capabilities that are going to enable us to deliver on that AI driven promise. It's an interesting time. Certainly in my 20 years, I think this is the biggest transition we've seen.

Now I've put on screen here how we think service work might shift over the coming years. So fast forward to 2030, maybe 2035, maybe sooner. But we think the front office is going to be a fundamentally different place. We think it's going to look very, very different. You see this idea of tier one, tier two, tier three specialisms that many of us in the contact center have.

Well, we think that's probably going to be a thing of the past because leading organizations, they're going to stop, frankly, employing low skilled generalists because things like the triaging, the routing, the troubleshooting, that's all going to be managed by AI.

You know, instead, what we think you're going to have is small specialist skills, hyper trained, hyper focused, who are going to be really focused on the brand new or the hyper complex scenarios that isn't ready to be pushed or managed by AI or pushed into self-service channels. They're going to be kind of like the if you think of the bridge between your company policy and AI, they're going to be the ones that are pulling the strings.

They're going to be reviewing support cases, they're going to be training and tuning the AI. They're going to be approving new self-service solutions. They're going to be absolutely critical to the success of the business. And look, we think most of their work is probably going to be behind the scenes because AI is going to be front and center handling most of those first time interactions.

But if approvals are needed, if changes are needed, if things go awry, they're going to be the human in the loop that will manage that and maintain controls efficiency and make things run smoothly. So, look, I don't want to scare anyone here with this this view. I showed this to a couple of my ex-colleagues in the customer service world, and there is a little bit of a oh, this feels a bit scary, but we don't think contact centers and customer service and sales centers are going to disappear, certainly not overnight, but the shape of it is largely going to flip, and it's going to flip in a way that you'll have these lean specialist teams that will be delivering on that, I promise.

And the net result of that is probably going to be that holy grail that many of customer service leaders have been chasing for many years, which is a reduction in operating costs and hopefully more hyper personalized, intelligent customer interactions. But Matt, I can sense there's a few people in the room probably going, this is all well and good time, and it sounds good. Sounds a bit scary, but how on earth do you actually get there?

That's the thing that Matt and I get asked more and more every single day. And I know you've had a ton of those questions down in the Innovation Lab. So before we get into that, Matt, I think what we should probably dig into is what do we mean by autonomous? What does that really manifest in the world of customer service? Yeah. So I think that is really one of the key things that we dig into. And we talk about and we hear from Don and we hear from C from Kerim.

Um, but I am not going to try to predict like someone smarter than me, but around when we'll have general intelligence and when we will have, um, you know, a real life Hal or Terminator or whichever direction that goes into. But I think our vision around autonomous organizations are really around where AI agents are taking the lead and how we design, how we implement, how we operate. Um, and especially from that contact center experience and that that CSR experience.

And that's true for both the decisions that are made and the workflows that are run. This is really fundamentally about digital transformation and how we apply the tools we have, whether it's AI, whether it's workflow to help execute on that and do so in a way that's built for reliability, that's built for portability, auditability and strong governance, um, and not stuck in kind of the traditional departmental silos where things are isolated or things are coded into individual channels.

I mean, that's really core to this Center-out story that we're telling here, and it works. We see it in practice, and it's going to work even more and be applied even stronger when it comes to AI. So this is really our vision and how we guide how we see it going forward for the autonomous enterprise. Um, the the keys here are really how we make sure it stays organized around the work. You have processes you need to execute on. Your customers have expectations.

We want to keep that organization there. We want that strong architecture where we are layering AI on top of the workflows that you have. So you have that strong transparency of what's happening and predictability and auditability and breaking out of, as Simon said, that that legacy that you have and doing so in a true Build for Change fashion that you've heard from Pega of all these years to stay future ready as we go forward.

So we think of this as a spectrum, and the path to automation is not a binary choice of either totally manual or totally automated, but steps along the way. So thinking about what is manual? You've seen this in both your front office back office, but it's hands on. It's unstructured. Um, your CSRs are handling so many of the cases on their own. They have to understand and have a lot of information.

They have to store that in their head, figure out how to do things, and they're lacking that intelligent guidance and set processes that really help things get done consistently and quickly in the way you need to do it. Um, but we can move forward into this enhanced stage where things get a little more organized, where we have these clearly defined processes around basic workflows and knowledge management to help give people that consistent information that they need when they need it.

And then we can help. We can better measure things with those performance performance metrics that I think and I hope are things that you're thinking about and keys that you're tracking along the way. Um, but there's more we can do there, right? So what you've probably seen a lot over the last couple of years are more of these guided technologies. Um, the system starts pitching in. More AI is helping agents by suggesting the right action.

It's helping to actually fill out some of the data that they need to do. It's helping get them to the right place, the right next step with the right information at the right time. So it's really acting as that, as that copilot for the work that they're doing. But we want to keep pushing again up towards this, towards this kind of autonomous enterprise vision and do more optimization. AI is taking on a bigger role. Um, it's it's more straight through processing.

It's more hands off for the humans that are involved. And we see the humans moving into more of a supervisory role. Being able to oversee those processes, oversee the work that the AI is doing, providing the ability to step in when necessary or take control. Um, when when there may be an issue or that key moment with a customer, um, to do that.

But along the way, you can see we're getting more and more comfortable with the role of AI, the the role of being able to, um, to to take that control away and take the need for human intervention out of it, because we're seeing those steps along the process as we gradually get more, more automated. And finally, we're driving towards this autonomous future where a, a much larger percentage of the work is done. Things are running in real time. AI is understanding the situation.

It is making decisions. It is acting on those next steps. It is coordinating with other parts of your organization to make sure it has the most up to date data, the most up to date processes, and is providing the right context to the people who need it at the right time. Um, and this is truly, truly taking so much of the work now that is time consuming.

It's low complexity, but high volumes, taking it out of the need to have any human intervention whatsoever, and allowing humans to focus on those really complex, really high value, really, really challenging issues that you need them to be ready to respond to quickly. So I think, you know, hopefully this resonates with you and you can start to see the steps along the way to realize this vision of the autonomous enterprise. And hopefully that stands out in the Innovation Hub as well.

But, um, right now when when I'm having conversations, we see so many people who are still very much in this lower quadrant here of, uh, of manual and enhanced work. And we we are optimistic and hopeful and hope that you're seeing it, too, as you see these technologies of these, these next steps to help get you and move up this, this spectrum. But the result of all of this manual and, uh, you know, this lack of automation is that so much of the work is this huge spike in the back office.

It's the in the contact center. and we are struggling to to meet clients in the channel, meet our customers in the channel of their choosing and have the self-service capabilities. And what we want to do is really push this work down out of the back office, out of this huge manual effort, this huge expectation around having to have a lot of individual knowledge, long training cycles, um, challenging enablement and move it to this, this self-service, agentic experience.

And that's the thing that we want to really come through, as you see, as you see demos and we start to learn more about it. All right. So we promised to dig in and go into some detail about what the missing pieces are, the commonalities that we see, organizations that are on that maturity curve. What are they doing that you could be doing to move up that transformation ladder?

So I think the first thing I want to say before we get into breaking this down, and Matt and I got our heads together before we put this presentation forward. And we and we were really thinking about practical steps. But before we get there, let me make very clear. Yes, this is about technology, but it's also about thinking about your processes, how your organization is set up, your culture, your people. It's a combination of all of these factors.

And being able to harmoniously harmonize these things. Can't really say that, um, in a way that brings everything together. So one of the first things that we always suggest is take a look at your business goals, your objectives that you're striving towards. We've thrown up a few examples here of real things that Matt and I are being asked to to look at in our clients goals that they're setting themselves. And some of these they'll get to quicker than others.

Some of these are hyper focused on really getting that customer experience as good as it possibly could be, or others are deeply focused on that migration to digital self-service. Others are looking at how can we reduce our our labor forces. I think you've got to be really thinking very carefully about what business goals are you running out before you start attempting to move along this autonomous maturity curve?

Because it will really help you focus your energy and attention on what's going to matter most. But let's look, as Matt said, most people, particularly when we first start working, right, most find themselves in that manual place. That's pretty atypical. Pega predominantly works with enterprise, large, complex organizations, so that makes sense. So what are the things that stand out to us that mean that an organization can move from that manual to enhanced phase? Well, the first thing is.

You've got to have a plan. And that might sound very trite, But like all good plans, you've got to have the building blocks or the foundation to really progress from. You know, if you're going to build a house, if you want to extend that and build it over time, it's got to be solid. It's got to be something that that you feel safe and secure with. And this is exactly the same thing here.

You've got to have the foundational elements in place before you start thinking about the more advanced analytics and automation capabilities and AI things that we've been talking about through the course of PegaWorld. So first thing you've got to do is think about your processes. So standardizing, documenting, getting them written down. Now you're probably thinking, Simon, that's ridiculous. Of course we know how our processes work. So I'll give you an example.

I was with an organization a couple of weeks ago and we were using Blueprint. You've all seen Blueprint many, many times over the course of PegaWorld, and we were going through a one of their customer journeys and I was using Blueprint build out the how we thought that journey should be, what they thought and how it should be documented. So we went through the stages and steps and the personas and everyone that was involved. All very, very good.

So then I invited some call center agents in the room. I invited some, uh, supervisors in the room, some of the quality teams in the room. And I said, right, what we've done is we've outlined this process. You tell me if that works the same way as you do it, what do you think happened? They said no, nothing like it. Nothing like it at all. Have I lost audio? Thank you. Batteries are dead. Sorry about that, folks. Um, so. Yeah, you're absolutely right.

It was wildly different to what happened in practice. So you've got to understand your processes. You've got to understand what's going on before you even think about automating, looking at ways you can drop in decisioning or I to to speed up or drive that process forward. And that leads me on to the next point of structuring your workflows. You know, you've got to put in organized workflows that track customer interactions, prioritize cases based on your business rules.

That really needs to be the bedrock of what you're trying to do. Knowledge is another key thing that Matt and I see time and time again in terms of a successful jumping off point, that foundational piece, everyone's got to get on the same page. Everyone's got to be able to access information readily, no matter where they are in the organization, what channel they're interacting with, so that they have that clean visibility of what's going on. And clean access to company information.

And then the final sort of foundational element that we're really, really interested in, the critical piece is linking up your critical functions. Matt just showed that wave slide. I think we've really got to be thinking how can we, for the first time for many, unify our self-service, our digital channels, our mobile, our front office, our back office in a way that gives complete visibility to everyone involved who is involved in getting that service work.

That is one of the biggest problems we see time and time again. We need to break through there so that we've got that complete end to end customer journey. And that's exactly what we found at Cisco. So Cisco was struggling with this exact problem. You know, they were lacking in consistency around their case management capabilities. And they used some of this foundational advice, um, to really think about how could they drive that consistency.

How could they drive that connectivity across the millions and millions and millions of daily interactions they dealt with, both internally and externally? And they had a huge boost in productivity. But I think what was more Powerful was the fact that they were able to unify that channel position, unify the visibility across the end to end business.

So I think once you have that strong foundation in place, the next step is really moving up into that guided stage and thinking about how we can take advantage of things like intelligent routing to make sure the right piece of work is getting to the right person quickly and effectively.

Um, thinking about how you can bring in things like chatbots to do more self-service and automation, um, taking taking them out of, you know, the, the, the first response is to pick up the phone and call and talk to somebody and look for those other channels of, I can talk to a voice bot, I can talk to a chatbot, I can send an email, I can I can actually get service that I need in the channel of my choosing, um, giving agents a guided interface.

There's huge, huge value in helping to ease the load on those CSRs, helping them to have the right piece of information, the right workflow, the right piece of data, the right piece of dialog, and really help them feel comfortable, smart, capable in the role that they're trying to serve there. So being able to walk them through those interactions and make those suggestions also helps.

Make sure that your customers are getting very consistent, high quality service with every single one of those those interactions. Finally, we want to make sure we are enhancing those omnichannel capabilities. Having that seamless and consistent experience across all of those channels, really core to that Center-out message that you've probably heard so much about by now.

But again, that stuff really works, making sure that when somebody starts something in chat or they start something on your website that they don't have to re re go through that process because we are centered around the work and that work's getting done really quickly and effectively regardless of the channel. And I think when you look at these metrics, I hope as we're going through, you'll think about which of these metrics are most important to us.

And as you're seeing the technologies both here and in the innovation hub, how are they going to really drive improvements there? Because key to all I we can talk about ChatGPT and creating images, but we want you to be thinking about the use cases. That's what we're thinking about from a product perspective. What are the right applications? What are the right use cases, and how do we implement them in a way that the user doesn't have to think about it.

They don't have to say, how do I write a prompt? Or how do I know the right question to ask it in a way for the AI to help me? We want it to just do the work, make their lives easier, and be seamless throughout. So another great example of this, and some of you may have seen this session at PegaWorld, was about how Elevance Health used automation and really improved their agent experience, giving them the right piece of information at the right time.

They using voice AI to drive that conversation and and help listen in and allow the CSR to focus on the work that needs to get done. Not focus on copying and pasting or finding the right dialog. And one of the surprising and interesting things that came about from this was not just the the reductions in handle time or first contact resolution, but also how it helped reduce training time and improve agent satisfaction CSR satisfaction by giving them the tools that they need to be successful.

And I think that's something we can't underestimate when looking at these technologies. So one of the things we'd like to do is we think about kind of moving up that spectrum and how do we get organized and help assist the CSRs in doing that work is show a couple of examples. These are real world examples, um, with GA products. And I think what it should highlight is, again, that point around we don't want the CSR to be thinking about it.

We want them to just have the right tools, the right time at their fingertips to help them be successful and drive drive customer success. The first example I'm going to show is just really simple. How do we listen in on a conversation and get the right intent and avoid the CSR from having to go look for things, sort through a long list, find something, search for something, and stay focused on the conversation. So you should hear somebody's voice on this call. Thank you for calling U+ comms.

This is Jason. How can I help you today? Hi Jason, I'm moving to a new house in Springfield, and I was wondering if there's a fee to move my service to the new address. So just really simple, but you'll see that pop of a suggested action in the bottom left that says move service. I suggested. So by just based on the dialog in the conversation, we're able to listen and provide that suggestion in a timely fashion. Agency doesn't have to go looking for it.

They don't have to go searching and we get that right in front of them also, again, making sure it's consistent. We don't want somebody choosing the wrong thing. And this this puts it front and center right for them right in front of them. But there's also a real challenge around collecting data. As data is coming in. It can come in fast. The the CSR is trying to maintain a conversation while also type things in.

So let's look at an example where they're actually able to just have that data collection be done automatically with AI, and they can continue to focus on the conversation. The new address is 123 Main Street, Springfield, Illinois. 62712. The move is scheduled for May 18th. So you'll notice that data just popping in. And in this case, Springfield had already been mentioned when she said, oh, I'm moving to Springfield. But we're able to keep that and use it contextually.

And what I also want to call out here is this is data is being pre-populated really quickly, but we still ask that CSR to confirm that it's correct. And this is how building confidence in the AI and help giving you visibility into the work that AI is doing and adding that human as a supervisory role. But I think you can also start to imagine how this reduces error rates, how this reduces handle time and allows the the CSR to really stay focused.

If anyone here has worked in a contact center taking calls before, you know how hard it can be to try to get all that data in and focus on the conversation, especially when the customer is not giving you that breathing room to actually get it in there. And so we don't want to put them on hold. We don't want to have to to delay things. And this is, I think, really, really helpful on that front.

And I think if you if anyone attended did a show of hands, did anyone attend the US bank session yesterday? The fireside chat. I think one of the great things that came through there was that the advisors, this talks to a point I was making earlier about bringing the human along with the AI. They saw this as additive.

They saw this as giving them kind of rocket powered service, so they could really focus on what they wanted to do, which was talk to the customer, empathize, give the best possible service they could deliver without getting bogged down with the manual work and the typing and all that great stuff. Yeah. Really important. And I think giving them confidence is really, really one of the things that we hear coming from that, I think that's reflected in that training statistic we saw at Elevance.

And I like the next one as well, because just the, the way, um, when we think about random questions and all the different questions, all the different data, we expect to see SR to know and being able to put that on their finger at their fingertips, but then also start to take away the need to even do a search to even know how to ask the question by being able to detect when a question is asked. Go to our Knowledge Buddy.

Ask the question of the Knowledge Buddy behind the scenes and then just show them an answer. So they're not saying, okay, let me just find this for you, but instead it's just automating that whole process and further taking more and more of the need for the human to be typing, to be, you know, orchestrating it themselves and letting the AI orchestrate it for them. I was also wondering if you have a trade in program for mobile devices, would you be able to provide some information on that?

You plus comms does have a trade in program for mobile devices. So really super simple example. But you you can imagine the scenario where the CSR is like I don't know what our current policy is. I want to make sure I'm telling the right thing. There is some nuance to this depending on the device, and rather than having to go look for that information or ask somebody or put somebody on hold, they can have timely, accurate information at their fingertips. It gives confidence to the customer. It gives confidence to the to the CSR and I think makes a much more successful interaction. Um, last one here on a call I want to think about feedback. No problem. Thank you for calling Uplus comms. Have a nice day. And you'll see here. You know, you have that whole timeline of everything that's happened. You have summarization of not just the work that was done, but also things that happened in the conversation.

So we can capture, you know, there were there were some, maybe some challenges that we had here that weren't captured in that workflow. Um, but we can also start to give feedback directly to the CSR or log that for improvement later and give them that feedback and analyze that conversation in a lot of different ways and different metrics.

And what you'll see is that that can be used to inform future interactions or inform the way we build out our self service agents, because we know what's successful, we know what people are responding to and what's working well from a from a agent, whether it's a human agent or a virtual agent. And we'll use this to leverage down the road. So two more quick examples here.

We'll jump over and see how this applies to different channels, because one of the important things here and key to that Center-out message is this AI that's being built is not built into the channel. It is truly Center-out and it can be applied across different channels.

And so when we think about something like email, we can go through and have when emails come in or even a thread of back and forth emails come in, we're collecting and extracting all of that data through by using AI and saying, let's get to the right answer. Even if we have to stop and answer questions in between, we're going to drive to the the outcome that we're that we need to achieve of finishing that work.

So we've collected all this data, but we still want to have a human in the loop to do review, and especially when there's a compliance aspect to it or a regulatory aspect to it, they may need to bring in documents. And so in this example, part of this workflow is let AI collect all of the data. But let's have a human review it and actually pull up the the documents that are needed for confirmation and do the review. This is something that eventually could be automated. You can see this.

It's on that that step on that path to automation. But we still have one thing in the loop. And like you saw in Don's presentation, eventually you might decide, oh, this is a great use case for intelligent document analysis and processing. And let's have that be done automatically. But in the meantime, you're getting the confidence of saying, here's the analysis that we've done. We had a human review it. There were no discrepancies.

And we're getting that at a very high rate that we can start to feel more comfortable about future in a future where that's automated. Last example here I'll call out in chat. And again this is helping the CSR. So we're thinking about again how do we drive towards that autonomous future. This is giving that visibility. So rather than having the CSR type all of the messages go back and forth. They may be monitoring and navigating several conversations at once. And we don't want them.

That can be really hard. If you've ever done this we do it in the simulator a lot. It is exhausting sometimes to manage three different conversations, maintain that context and maintain the right information at the right time. So instead, we let AI manage and monitor that conversation so that when they switch back, the message that they need to send is rich and ready for them to just rather than have to start typing, have that delay. It's just there for them and they can edit it. Of course they can review it, or they can just click send and then keep that conversation going. So that's taking the mental workload off the CSR. But it's really improving the handle time of getting this work done quickly and effectively. Because there's no typing time. There's there's not the inconsistency, the spelling errors and all of those things that come along with that. But again, this is just one of those steps to a more automated experience where we're saying, you know what?

This is working great. The the agent has actually clicked send on every message in this interaction. This is a great target for automation in the future. So I'll wrap this demo portion up. But hopefully what I really want you to take away from all of these examples is each of these are stepping stones to automation.

They are showing how we're building on a better CSR experience, how we are enabling people to be successful, how we're reducing handle time, how we're driving towards better first contact resolution, better satisfaction, better for both the CSRs and the customer, and helping do things like some of the other the other follow on pieces of that like better retention, shorter training times, all of those things that really do move the bottom line when it comes down to it.

And when we show this to people, I think one of the things that jumps out, specifically when I talk to my my old customer service leader colleagues and show them the biggest problem they have is capacity. They're firefighting day in, day out. Next problem. And the next problem is the next problem.

What I love about this is it's starting to give capacity to those agents, to the leaders, to the situation that we're in so that we can start thinking about, okay, if we're giving ourselves breathing room, we can start thinking about what's the next stage on this autonomous journey. What are the things now that we've given ourselves capacity to move to? Yeah. No. Great. Great point.

And and I think it becomes more obvious when you think about it this way of this, this progression and saying, okay, we've got this guided piece. It's working well, we're getting better visibility. And then you can start to optimize. And to Simon's point, which I think is a great one and rings true in a lot of the conversations we have with our clients, is there are those struggles. It it does become very easy to get stuck and bogged down in the tactical things.

And part of what we're hoping comes out of this is that these are incremental improvements. These are iterative improvements that you can do and start to move up. And as you're realizing the gains that's going to free up your time, free up your energy to to work on some of these other pieces.

So that includes things like how do you do decision support systems, how do you do more digital digital containment and start to measure that and recognize the value that you're getting from it, reducing your your case resolution time because you're seeing how how much less manual intervention is required there.

And really these are these are tangible metrics that are going to drive success and hopefully help you take back to to your leaders and managers and show that there's there's value that you're deriving out of the system, out of this adoption of AI and really clear, crisp, targeted ways.

Another place that we've we've seen this is and another great PegaWorld presentation that I love is around digital containment and being able to implement a chatbot over digital messaging to handle a lot of those really high volume, low complexity use cases, but that are often the bane of the contact center of people calling in and say, oh, you know, when is my bill due? How much do I owe? Where do I mail this check?

And it's just like, you know, prevents the humans in in the contact center from getting that work done and being able to do things that humans are really good at making contact, solving complex problems. And so this gets to that optimization phase of analyzing your channels, putting that chatbot in place and then operationalizing it and incrementally improving on it to to continue to capture and deflect more of those, those interactions that come through as escalations.

I think once we get to the the final stage, there's a few characteristics that we're starting to see and look, not many organizations have got to this level yet. So, um, you know, we're still working through this with them to see what the art of the possible is. But the the things that I think we'd be thinking about are I, as at this stage got to be starting to make decisions for you, obviously under the guardrails and the supervision and the the controls that Matt's talked about.

But ultimately, if we're going to get the speed and the efficiency, AI needs to be driving some of that, um, that work, uh, and making those decisions under certain scenarios. We've also got to be thinking about optimization.

We've got to be creating a culture of self optimization, where your systems and your business is starting to look for ways in which it can improve, suggest new ways in which it can get better, find and adjust just based on live data that might be coming in to the business at this stage. We also think that you're going to have to have a pretty structured approach to how humans and AI work together.

So you're going to have to have a solid framework that sets those guardrails in place that everyone knows where their role lies. And as we've talked about at the start of this presentation, that might start to be the human being behind the scenes rather than being front and center with the customer. As AI starts to take on more of that responsibility.

And of course, the final bit of this puzzle as we go through maturity is we've got to be leaning into advanced analytics that can spot trends and new opportunities. You know, what are the things that humans want in terms of our products and services sets? How can we adapt in real time to those preferences and changes in environment and changes in buying behavior? So a lot of that will come in as we've built this capacity that Matt and I were talking about. And I love it.

I love this example of Virgin Media. This is Virgin Media Ireland. I think they've been at PegaWorld in the past. But the reason I love this is they're using advanced analytics, but they're using it in line with their human agents, their their call center agents. And they've managed to do again the magic trick, the the flick from being the call center to the profit center. So they're using real time decisions to recommend next best actions based on retention or opportunities to sell.

And their numbers have gone through the roof. You know, 30% increase across and upsell is absolutely massive. So this is a continuum. This is something that hopefully we've broken down in a sort of bite size way that you can start getting your heads around how and where you could really make changes in your organization. One of the things that we really want to encourage you to do is go and try this out for yourself.

Um, we have and Matt's alluded to this, a wonderful simulator experience down in the Innovation Hub. Um, this is really our way of bringing GenAI to life, and it has a number of different use cases here. I mean, we've we've seen organizations use this to bring down their training time. I actually put my 11 year old daughter on the simulator experience, and she was able to start answering and dealing with customers in under two hours, which is pretty amazing.

If you think the speed to competency for most contact center agents. But we're using it here to bring it to life for you. So get down there and you'll see all of the great things that Matt was presenting and demoing. You'll also see all of the the power of guided interactions, knowledge, interactive cases, all of the things that we really want you to see. So go and have some fun with that. Um, we're going to wrap up now and leave five minutes for questions, but just to close off.

Um, I hope you found this insightful. No one really, truly knows what the future holds. Um, but we think this is very likely to be the direction of travel. Um, and remember, it's not just about technology. It's really about reimagining how work gets done seamlessly across your organization, whether that's in the digital channels, whether that's in your contact centers, whether it's wherever that might be.

And it's also about reimagining what that experience will be like for your customers, for your employees, for your leaders. That's the critical piece here. Um, so thank you very much for listening. Uh, we'll invite you to ask us any questions you like, and Matt and I will stick around afterwards if, uh, if people don't want to come to the microphones and we'll certainly be in the Innovation Hub. So thank you all for joining us. We've enjoyed it. So thank you very much indeed.