Thank you, everyone, for joining us today for first Investor Day. For those of you I don't know, my name is Laurence Madsen, and I'm in charge of investor relations and ESG. We thought it would be helpful to go through what is changing in our market and how Endava is positioning for it. This is also a great opportunity for you to hear from our wider leadership team. The presentation today will be in fireside chat format, and we ask that you keep your question for the end of all the presentations. For those of the webcast, you can submit your questions, and we'll take as many as possible during the Q&A session. Before we... I would like to highlight a disclaimer for everyone, and I would now like to introduce Scott Harkey, who's our head of strategy. Mic's working. Can you guys hear me? Hey. Laurence, I thought you were going to read the whole thing. I was prepared to wait five minutes while you read that entire legal disclaimer, but no. Welcome, everybody. We're glad you could join us today. So to kind of set the stage for today, we're going to go a little bit into really how we see the market in this AI-driven digital shift that we see and how Endava is best positioned for it. We'll talk a little bit about some future-looking things that we think are great opportunities and, again, how we're trying to position for those. But as Laurence said, it's really about giving you exposure to some of our broader thinking. So we have executives from across the business here to speak about different parts of the business. We'll kind of dive into some of those things in a conversational format, but really just trying to give you some of that, again, broader exposure to our thinking, so I think the best place to start is with our CEO, John Cotterell, who's going to come up and talk a little bit about some of the opportunity that he sees out in the market. John. Thanks, Scott. Thank you all for joining us. Yes. It's good to see such a good turnout, actually. I appreciate it. All right. So the first question's an easy one, right? And it's about the key trends that you see out in the market and how you think about the shifts that are happening in the market. Yeah, sure. So yes, we're in a what we're calling a digital shift. AI has significantly changed the way in which digital transformation is going to operate going forward. The two main reasons. One is it brings so much capability to apply to products. But then the second reason is in order to access the data, the processes that exist within an organization, in order to truly benefit the products that you're going to create, you have to get into the core. And that is not easy with a lot of the legacy core systems that exist out there, particularly in large enterprises. So there's that sort of traditional wave and approach, which is around ideating new product, getting it into production, which is our traditional capability that you guys have seen us operating with for so long. But then alongside that, there's this core modernization need of getting into the core systems, making them digital-friendly so that you can maximize the benefits that come out of it. So the exciting thing is there's a larger TAM for us to go for. The addressable market is bigger because instead of just building around the outsiders, we've done for the last 24 years, we're actually needing to get into the larger programs around transforming the core to enable those new products that are coming through. What are the things that organizations typically find to be obstacles that are stopping them? Why is it hard to do this modernization? So I mean, this is about the engineering challenges attached to the AI transformation wave. So there are engineering challenges around AI itself. How do you make sure that you're not getting hallucinations? How do you get the reliability that you need if you're going to be implementing it in your enterprise? How do you make sure that the security levels are high enough? And that's before you get into the, so what are the use cases that you're going to get the benefit from? And I see that as our job. That is what Endava and organizations like us exist to do, is to help our clients envision that through our ideation, but then get it into production. And then, of course, underneath that, you've got the big one that I was touching on a moment ago, which is you have to modernize the core, which is a big engineering exercise, I tell you. I'm sure we'll hear that from some of our panel later. So you put those together, and actually, there is a significant engineering and technical challenge around this next wave of digital, which we largely bypassed in the last 20 years of digital transformation. Not completely, but largely. I think the thing that I would add to that, we're also in an environment that has shifted and raised the bar from a business case point of view. So we've come off the back of the post-COVID wave. Lots of money got thrown at technology. Not all of it delivered good returns for organizations, frankly. So CFOs have raised the bar on business cases. There's more levels of sign-off that are going on within organizations. Interest rates have gone up, so the returns that organizations are looking for have been raised. You put that sort of fiscal ask alongside the technical challenges that you're getting with this new technology, and actually putting business cases together that work is much more difficult than it was two years ago, and so we need to solve that problem as well. We need to plot the routes through that make it more cost-effective to drive change and give a much more solid understanding of what the outcomes are going to be and the impact it's going to have on business. I think that's a point I'd like to highlight, is just that as a company, we have always done an amount of the ideation side of things, helping our clients figure out what to go do. But I think in this wave and what we see out there with clients is the increased need for that as well, right? Because we're in the early stages of the implementation of a lot of this technology, organizations are doing a lot more thinking about, well, what do I go do, right? It's no longer just the engineering, how do I physically go do it? The strategy side and whether that's the technical strategy, right, the technical architecture, or that's the business strategy, that's become an increasing part of what we do and what we're getting asked to do, I believe. Yeah. I think it's actually visible. We did a survey with IDC and going around C-suites in large enterprises and asking about their approach to generative AI. And you can see that 38% are actively doing something. Given the potential impact of AI, I would say that is lower than I would expect. But 70% believe that they need to do something, and it's going to have a pretty significant impact within the next 18 months or so. Making this happen and the step-up in activity from doing something from proof of concepts to actually executing at scale is around us helping solve those engineering problems. Yeah. I think in the way we just articulated, right, that 70% of people, they need help figuring out what to do. They know they want to do something, but they don't know what to go do. It's only the 38% today that it's the engineering of actively going and doing it. But obviously, we expect to see that shift over time. So how are we capturing this need, right, from a capability standpoint and how we position for this? How are we meeting the need of our customers that are looking to kind of embrace this journey? So the first thing that we're doing is focusing in on what are the AI use cases. And you'll see some of those later today. We wanted to bring some of the things to life. The core modernization space is critical. And actually, the way we've approached that, to say, how do we use the tools and accelerators that we have and apply AI to it to massively accelerate the potential out of those programs? You all know we did the deal with GalaxE in March. Well, it concluded in early April this year. And that brought a whole set of accelerators and capabilities in terms of being able to understand the core, read the code, tell us what it's doing, that we've then been able to pull into that approach. And it's been a big step forward in terms of the sort of core modernization capabilities that we can go to customers with. In fact, the sorts of comments that we've had, one customer said to me, "This is a complete game changer." They've been struggling to work out what they do with the core. And actually, his words are complete game changer. And another customer said, and this is one who's actually used it through GalaxE for many years, said, "It's faster. It's cheaper because of the productivity tools that are being applied. And I get a more assured outcome. Why would I buy from anyone else?" So that's where we're focusing on being able to make a difference for our clients. That's a pretty strong testimonial. Yeah. Actually, the more people we show it to, the more of those comments we get. One of the ways that we've looked at how we take things like core modernization to market has been the introduction of Dava.X and really using that as an area of focus. Can you tell us a little bit more about that and what does that represent and how do we think about that? Yeah. So Dava.X for me is horizontal, right? So Endava is very organized around industry verticals. So our go-to-market is all around how does technology impact your business? What are the use cases within your specific environment? One of the things we always had within the business was all the technical capability that underpinned that. But we'd never really pulled it together in a clear way that enabled us to articulate to market the technical competencies on a horizontal basis that went across all industries. And so we created Dava.X basically to pull that together. You can see them on this slide. So we split them into two areas. There's accelerate, which is a bundle of ones which are in the market, but they really need investing in and accelerating to help our clients get the full benefits from it. Then we have some inventive ones which are probably two, three, four, sometimes five or more years out, but actually, in getting our roots down into that space, actually want to pull it together across the organization and make sure we hold our lead as that moves forward as well. These areas change over time, right, or can change over time? They will. I wouldn't be surprised if we were adding one or two each year. And some of them will become such common practice that they don't need articulating as a differentiator into the market. So then maybe let's talk about accelerators. That's another piece of the kind of capability stack, if you will. How do those fit into things? So we've talked about accelerators for many years. And we highlight them as accelerators rather than product because they're all about how we can use reusable software or how we can use capabilities in terms of praxis and approach or in terms of software that we've created to actually enable us to do our job faster, more productively, and deliver more benefit for the effort to our clients. And we've pulled this under the Dava.X framework because in each space, we have those tools and capabilities. And we'll go through a couple of these when Matt comes up and does the sort of more technical presentation with you guys. But we call them accelerators because they're basically about us being able to operate faster and deliver better outcomes and better articulation of opportunity to our clients. We've put one up there that's in the industry domains, and we have a few of those. We just gave one example there. All right. If we zoom out and we look at the broader kind of impact of the organization or these things on the impact of the organization, if you've been paying attention to our earnings, you notice the continued diversification of our business, right, both geographically and from an industry perspective. How do you think about that and its importance in the kind of long-term stability of the company? Yeah, so I thought it would be interesting just to reflect back on what's happened since IPO, which was just over six years ago, and there's been quite a lot of shifts in the shape and how Endava looks. So a big one is a shift in our industry diversification. You can see at IPO, we were hugely banking and financial services, payments financial services oriented, and TMT, huge part of our business. Over the last six years, we've significantly diversified. That was a very clear objective that we had, and so if you look at this last quarter that we've just reported, you can see a much better spread. There's still a little bit of a slant if you add up the financial services ones in there, and we'll continue to diversify away from that. But I thought it was worth putting up just to show that that shift is gathering momentum, and we've achieved quite a bit. Likewise, the geographic shift. You can see we were very, very focused around the U.K. at the time of IPO, with Europe following. And actually, we were light in North America. And now North America is our largest segment, which I think more accurately reflects the market opportunity if you look globally. And so that will give us a little bit more stability if you look at what's happening from a macro point of view in different regions. And we still need to diversify out of the U.K. But once again, just a process that's underway. And then the final area that I just wanted to highlight is where our people sit. And at the time of IPO, we were hugely focused in Europe. The red and the blue added together was our European footprint, the red being areas that are within the European Union and the blue being geographies we were in outside of the European Union. So this is where our people sit. That has shifted, still with a strong Europe core. But actually, you can see LATAM is gathering speed and Asia-Pacific. And that has given us a global footprint now, which particularly with our large global clients enables us to have a different conversation about being able to support them in all of their geographies. I mentioned on the earnings call yesterday that the footprint that we now have in India through the merger with GalaxE is opening doors that we didn't actually really realize were closed, if I can describe it that way, in the sense that clients who have a strategy about having delivery in India are now engaging us in conversations, not specifically about delivering out of India, although that's part of it, but seeing us as a global organization that can map onto their India strategy as well as the other locations that they have in terms of where they operate, and so having that India operation is opening up opportunities for us to deliver nearshore as well as opening up the operation in India. I've certainly had the experience of that being a literal check-the-box exercise, right? Where you're going through procurement and it's like, "Do you have India delivery capability?" And if you can't check the box, you don't even make the list, whether they want it or not, right? So it certainly opens some of those doors. So certainly for some people in the room, those are all nice words, John. But what do the numbers look like? And so I want to invite Mark Thurston, our CFO, up on stage to talk a little bit about some of the numbers. Welcome. You got to say something so we know the mic works. All right, Mark. The first question is just to give us a little color on the short-term outlook. Yeah, yeah, yeah. I mean, we had our earnings yesterday. We reiterated the full-year guide. So we're at GBP 800 million-GBP 810 million revenue, which represents constant currency growth between 10% and 11.5%. And that will drive an EPS on an adjusted basis of 112-115 points. And we put out a good quarter as well to boot. Good to hear. What about margins in the short term? So again, looking at adjusted PBT Q1, we're at 9.9%. We expect profitability through the year to improve. We're not at the level that we have been historically, but that's what we're going to be driving, improved profitability. Okay. And then the last kind of specific question is just around we talk a lot about AI from an opportunity perspective. But what about from a margin perspective? Do we think it'll fundamentally change the way we look at margins? What about from a type of contract? Is it time and materials still? What is your outlook on that? Yeah. I mean, at the moment, our revenue is over 80% time and materials. So there's a high linkage between the people we have working on a particular project and the revenue we generate. We do have some of our more advanced and larger clients with an output-outcome-based way of billing. And I suspect with the AI wave that is coming, actually, we will start to contract more on that basis going forward. But I think that shouldn't be an issue for Endava because it allows us to capture some of that sort of margin. And I think clients will value the outcomes that we are delivering because it will be differentiated. So I think it's a positive for us. Yeah. I certainly know from a lot of client conversations that they're trying to figure out the same way, right, in terms of how to think about what they pay for work that's been delivered with AI or how it's incorporated or all those elements. So it feels like in market in general, there's still a lot of ambiguity about how do you value it, how do you price it, how do you consume it as an organization and think about it that way. Yeah. Yeah. Awesome. Well, those are the questions for you, Mark. Given the update yesterday, you get off a little bit easy today. Oh, thank you. Mark will be around later too, so you have plenty of opportunity to ask him other questions. Thank you. All right. So we'll wrap up this section and ask John to give us a little bit of a kind of a highlight and an outro, but just to kind of reinforce a couple of the key points: Massive opportunity in the AI kind of driven set of transformation that we see, fundamental belief of core modernization being a key component of that and really a big driver and focus area for us, and we'll continue to see that evolve. We brought a lot of things to the table with Dava.X and accelerators and other things to really try to organize around this opportunity in the market, and that's hopefully part of what you heard. But John, as we close out this section, what's kind of the summary of thoughts that you want to leave people with? Yeah. I sort of thrown them up on a slide here. So AI is starting to accelerate change. We're seeing those projects coming from a smaller base that are actually getting into production systems. I gave some examples yesterday on the earnings call. We'll talk a little later about some of those as well. The fact that businesses need a digital core in order to be able to really get the full benefits out of this next digital wave that's coming through, and that as we see it, the Endava mix of capabilities, our history for the last 24 years of ideation to production, the engineering capabilities and the accelerators that we bring really put us in a strong position to help our clients as this next wave starts to build up. The big challenge today, right now, is helping clients to build their business cases to demonstrate the engineering solutions that are going to open the next wave up. And that's what we're focused on. And as we help fix those problems, we'll see this next wave come through. I think the exciting thing that underpins it from our point of view is that the TAM, the addressable market, as we look forward, is potentially much bigger than it was over the last 24 years because we're no longer just focused on building largely around the outside of the core, but actually needing to go into the core and transform that as well. It's interesting. I wanted to just throw up a slide. Obviously, it has the flattening off at the end. But since the last six years, we've essentially trebled just under our headcount and just under quadrupled our revenue. That takes us to $1 billion, often a difficult transition, if I may say, for organizations. But one of the reasons we wanted to do this investor day today was to say, "Okay, having built that scale, what is our focus? What are the foundations that we're able to build off from that, the ability with that scale to make the investments that are needed for the next phase, and actually to set ourselves up for this next digital wave that's coming out of the shift we've been talking about? Awesome. Thank you very much. Thanks, Scott. All right. It's really weird talking to a room full of people and there not being applause. I'm not saying you should applause. I'm just saying. I think you were saying. Well, it's good audible transition, right? It's like, "Okay, that ended. Now we can go to the next thing." But when you're all just sitting there really still and quiet, it's just awkward, let's just say. All right. Up next, we want to dive a little bit into how do we deliver the work that we do, right? So we are going to invite our Chief People Officer, David Churchill, up, and our Chief Operating Officer, Julian Bull. Look at there. Nice. You really need some walk-on music. That's what you need. It's playing in my head. Does it come up? You strut down the aisle. All right. All right. So welcome. Welcome. I had to get my head straight, right? I know you guys are ready. It's out there. It's you guys. All right. All right. Let's start talking about our overall go-to-market strategy and just really how does that position us uniquely in the market, Julian? Sure. So we've been kind of doing this for a number of years now. But for me, there's three core things that John touched on as well in the earlier segment, which is what's always driven our growth, and this is feedback from customers, which for me is always really important. First of all, is our industry expertise. We can actually get involved with. That wasn't me. I was going to say. We can understand the client's business problem, understand the opportunity. I think the second part that leads into our ability to help them with strategy and advisory services as well. So we can help them design what the future looks like. And then the third thing and the thing that's always underpinned Endava is our really strong architecture and engineering capability. And I think that sets us up really well. And you're going to hear a lot about this throughout this entire event around what's going on and the opportunity around core modernization. Because in my opinion, I think you need those three things to really drive core modernization to build those new digital cores to enable clients to take advantage of this big AI wave that's coming. And the good news for me, and it makes my life and our life easier being out there in front of the customers, is we've been doing these three things for a long time. So we've got really well-established practices. We've got great case studies. We've got a great deal of experience in this space. And I genuinely believe that that is where the market's going. And we're going to see a return to those sort of core capabilities you need. I think you highlighted something really important because I think in a lot of our messaging around core modernization, we talk about the technical. I mean, we talk about going deeper into core systems and all those pieces, and that's clearly like the chunk of the kind of sticky work, but for a lot of organizations, the first part of that is that ideation phase that you mentioned, right, and that's where that industry knowledge, understanding the business context of the thing we're trying to do, we've certainly seen really accelerate our engagement there. I think you see two differences in the marketplace. I think a customer will turn around and say, "We need to build a new digital core or a payments platform or whatever it is." Some vendors walk in and say, "Great. How many people do you need?" Our approach, and we drive this into everybody or the graduates that join us, is the first question, "Why? What is it you're trying to achieve? Why are you trying to achieve it? Let's have the conversation." And then that's actually build the approach and solution around that. You can only do that if you understand the industry. Otherwise, you're in the game of how many people do you need? Let's talk about how we deliver the work. We have a global delivery model. We look at a combination of nearshore. We now have an Indian delivery capability. How do we think about bringing all those things together and delivering what we do? It's that one for me, isn't it? You're looking at me, aren't you? David's looking at you too. Everybody's looking at me. Right. Okay. So we have, I think for us, yes, we've added India, but nearshore is very much core to what we do. And the reason for that is you can achieve synchronous delivery. You can be close to your client. It enables you to ideate. It enables you to innovate and collaborate and every other word that rhymes with those things. So that's absolutely key. And we started that off in Europe. But as we have built relationships with global clients, as our marketplace is growing globally, we have added over time. We then went to LATAM. Then in the last few years, we've gone to Malaysia, Vietnam, and most recently with the merger with Tim's company, GalaxE, we've gone to India. But what it gives us is it gives us a global nearshore model. But also by adding places like Vietnam and especially India, it gives us greater scale as a business. So as we take on these bigger programs of work, we can do a follow-the-sun model, but it also means that we can really scale up to deliver to those opportunities. So it's a combination. When we think about, we talked a lot about AI and the type of work that that's creating. How do you think about how we strategically engage in those? How do we bring value to those types of projects or initiatives? It comes back to the industry expertise, and it comes back to our strategy and advisory capability. But again, it's not just turning up and saying, "How many people? What capacity do you need?" It's, "Why are you trying to achieve this? And what's the best way to go and make that happen?" That's how we approach things. And I think that's the difference that we make in the market to our customers. Yeah. We kind of highlighted it before, but I think too, given where a lot of organizations are at and their thinking around using AI, that side of what we do is really important right now, right? Whereas sometimes it's about the technical capability and the depth and technical capability, but some of that more strategic ideation is super important right now. And also as well, and you'll meet two of them after us, but we've got some really super smart people that understand this and are innovating in this space, which you'll see a bit later on. Yeah. So I think, again, let's not underplay that. You've got to have super smart people that can work this out, and we've got them. So David, how do we think about delivery in this context? How do you envision AI changing the way we deliver or the way we think about building out our delivery model? What's the impact? Yeah. I mean, I think fundamentally, we're already starting to see a transition, right? So we're already seeing an impact of how can AI transform the way in which we operate. I'd say a few things about it. First of all, as we've engaged with our customers, as they've started to think about, "How do I navigate this changing environment?" They want to work with trusted advisors, people that understand what to do and can lead through that change. And so we have seen a slight shift towards that level of experience, the required capabilities to come in to lead delivery for our customers in the industries in which they have experience of change. And so we've seen some of that happen already. That's informing our hiring practices. We're very much looking at core data skills with high levels of experience around application of AI in their previous careers, as well as continuing digital shift around cloud-type experiences as well. So we're seeing those hiring practices happening across the company. But what I'm finding really interesting and exciting is also the opportunity for more junior talent to come through as well in our delivery model. The bar for junior talent is rising. The capabilities that an AI-native generation will bring to the workforce over the coming couple of years, I think, will be transformative. And how we then build teams across location to meet those opportunities of AI-native juniors on an accelerated career path using tools to drive productivity, I think, is hugely exciting. So these are the sorts of things that we're working through in our delivery model. Yeah. It's an interesting perspective because I think a lot of the rhetoric would be AI is going to eliminate or reduce junior roles, right? What is the need for a junior role if AI can do some baseline of activity? But you're suggesting that actually the junior roles are becoming more upskilled and leveraging these tools. So actually they become more capable and they're able to contribute more. Correct. And I think as well for the smartest and those who are able to apply those productivity gains to their workflow, the opportunity for high velocity in career progress is there as well. So we still want to put together the right shape of team from across our delivery locations, building on the heritage of our technical excellence. But those who are able to apply AI to their workflow, there's an immense opportunity. So let's talk about that a little bit. Let's talk about how are we nurturing internal employees to really help them be AI-enabled? How are we using AI internally in that context? Yeah. So we've already made investments in this area in order to bring those productivity gains to bear for each individual in the company. So our aim is to bring those tools to every person, not only engineers, also those working in different practices in the people function across the business. So one we know about, obviously, we've partnered here with OpenAI. We've got ChatGPT as an enterprise opportunity for our engineers to have an assistance in doing their work. And our expectation is that will drive those productivity gains. I think there's a few examples that I've experienced already in the way in which our people have innovated using ChatGPT. So one example is where somebody has created a knowledge management, the Endava Knowledge Management GPT. And that now gives the individual the chance to mine information from previous experience. Whether that information's in SharePoint, whether it's on Confluence, that prompt enables that information to come through to enable faster decision-making, to give depth of insight from previous learning of their colleagues. So rather than having to have those discussions, they can now pull that directly through that GPT. Another one created by our colleagues in marketing, which I use a lot, is the Endava Tone of Voice GPT. So previously, if I wanted to craft a message, I would rely on a communications specialist to work with me on those things. Whereas now I can put a few prompts into the Tone of Voice GPT, and it will give me a tonally accurate message. So now rather than me thinking through, "What do I want to say?" I can now spend my time thinking about, "What is the most appropriate method for delivering this message? When should I say it?" and so it's small things like that, the innovations that are coming through that enable us to work smarter, to accelerate the work that we do. Did you run your responses to these questions through the Tone of Voice GPT? I did. It's not. No real-time filter that's kicking it in. One of the interesting things that I've seen has been the way that individuals in the organization have kind of stepped up and become champions, right? When you take a tool and you release it to people, and I think it's important what you said, not just the engineers, right? There are actually a ton of applications of AI tools on the business side of functions that you really only discover when you unleash it and people have it, and then they start using it to solve things that are annoying to them. It was a part of an OpenAI session at Money20/20 recently, and the CFO was talking through some of the way that they think about OpenAI and using it in their own business. And she was going through an exercise they did internally where from the finance organization, they got the team in the room. They went through basically, "What are the pain points that you have in your day-to-day?" And then they came out of it with a bunch of GPTs and things. Just to make a point of opportunity, the way she described that, that was within like the last six months. So if you take an organization that the use of AI is so core to the organization, that's who they are, and think about where parts of their organization are in that journey, it just kind of paints the picture of the opportunity out in the market for what we do, right? There are so many organizations that are so early in trying to figure this stuff out. So I'm a big advocate of the enablement of our employees and just even thinking about that because then it starts to seep its way into all these customer conversations, right? Where it's not just about some big engineering complexity and how do we solve that. It's also just about like, "What's really annoying in your day-to-day? And is there a GPT we can build for that or some way we can quickly enable that to be easier? Absolutely. It's those incremental gains, right? So you are empowered now to find those solutions for yourself. You don't need to necessarily endure those problems. You can now find a solution not only for yourself, but it's highly repeatable for other people as well. And that's a core part of our culture that we want to enable people to support and enable others as well as themselves. And so seeing those GPTs written, seeing them used by other people for productivity gains and improvement in their work is great to see. Yeah. All right. Well, let's switch gears a little bit, Julian. Let's talk about our partners and how we think about the role of partnerships in our go-to-market. Yeah, sure, so we've worked with partners for many years, but I think one of the things that we have noticed is, and this is incredibly obvious, I know, but as we do the bigger deals with customers, as we get larger engagements with customers, it's not always about just pure engineering and building things. It's also about product and buying and integration, and so one of the things that we're doing is clearly we're working with the hyperscalers, so we've got some very, very strong relationships out there with the likes of Google, etc., but also as well, the industries are working with building their own industry-related ecosystems. So when they're in talking to customers and helping them through that strategy and advisory piece, understand the size of their problems and the art of the possible, it enables us to actually turn around and say, "Okay, we're going to build this, but we're also going to integrate that." And I think we're seeing a huge uptick in opportunity as a result of that. And the reason for that is because the partners love us, because particularly some of the more generic technology partners, it's difficult for them to differentiate with just a technology story. So what we enable them to do is start to build industry-specific go-to-market propositions. So it's helping them differentiate in the market. When we're actually engaging them and doing projects, our success rate is really high. So we make them successful. We make them look good in front of their customers. And in turn, clearly they like that, and they're bringing us more work. So we're creating a really nice ecosystem where everybody's winning out of it. Our clients, most importantly, our partners, and then us in terms of the revenue we're generating, but also the future business that we're seeing coming through the pipeline. I think one of the other interesting dimensions is a lot of the kind of flow of funds of enabling a lot of the work that we do is actually flowing into the hyperscalers right now in the sense that they're organizationally creating budgets to fund work at organizations that are our clients and funding work that we make it do for those clients. Yeah, absolutely, and I think particularly as well when we're helping them with those industry-specific go-to-markets, that's where they're investing because, again, they just mentioned that gives them that real point of differentiation. Rather than just being a cloud provider, they have specific solutions for the insurance sector or the media sector, and that's where they're investing, and that's where we're working with them. Yeah. And it's where they've chosen not to focus internally, right? They've not built out that internal knowledge of the industries. They're using partners. That's not their business model. Their business model is to work with trusted partners to make that happen, and that's where we're benefiting. Yeah. All right, well, one thing that we kind of touched on a little bit, but we didn't go specifically into, was just giving a little more color on India and thinking about how it fits into our delivery model. So I don't know which one of you wants to address it, but if we could talk a little bit about how do we see the opportunity in India in terms of the people that we have there, how it fits into our overall delivery. Just share a little bit about how we're thinking about. Yeah, sure. I mean, I think John touched on it earlier on as well. It's additive to our capabilities. This isn't about any sort of replacement strategy in terms of capability. It's about building a global delivery model for global partners, global customers. And so for me, what India gives us is all of the things we heard about, the checkbox, the ability to actually work with some clients that see that as a prerequisite. But more than that, it gives us a depth of talent, particularly through GalaxE and core modernization. We heard some of the testimonies that John spoke about with regards to their experience of applied core modernization to an AI to their customer base. And so we want to build out from there. And for my view, it's about a global delivery model. So it's enabling nearshore at scale as well. So as we extend our delivery from full teams in places like Romania or Argentina, it's about adding, where appropriate, those capabilities into the mix for the customer so that we can build a scaled delivery model that works for them as well as us. So that's how I see India in the mix. Not only India, but also Vietnam as a talent pool and a depth of capability that can add to our existing initial. I think it's probably also important to highlight that our way of delivering, our quality of delivery, our practices that we build into our team framework and other things, that they're such a critical part of how we deliver really well. Absolutely. That our version of going into India is to do that way there too, right? It's not just about adding a location. It's about how do we do what we do really well and that we've seen work really well for our clients and bringing it into a market like that where there's a big talent pool that we can leverage. I'm obsessed with what our customers think and what our customers think about us. And we've had some customers that have worked with Endava for years, and they've worked out of Central Europe. They've worked out of LATAM, and they wanted to go and check out what's going on in India for us. And you send a customer out there, and it's relatively new, and there's always a little bit of, "How's this going to go?" Every single customer has come back and said, "It feels like Endava." It feels like the Endava we've always worked with, and that has built so much confidence in us, in our business, that there is this delivery capability where they've got some incredibly talented engineers, but culturally, which is, again, a big we don't necessarily talk about that enough, but culturally, our culture is a differentiator, and they are culturally aligned to the way we've been doing things for years. For me, that's an incredibly positive message to hear from our customers. Awesome. All right. Well, thank you both. Thank you. Thank you. Can we go now? Yeah. You You can. See, the clapping helps, right? All right. Up next, we have our CTO, Matt Cloke, and our Head of Dava.X AI, Joe Dunleavy. We're going to talk a little bit about the tech side of things and dive deeper in it, including some demos at the end of this. So hey, guys. We're feeling bright. We're going to show you the end. Yeah. You're going to do a live demo, is what you just said? You're going to code something on the fly? Not quite. No, not that bold, huh? Not that bold. All right. All right. So as John showed earlier, we recently did an IDC report or a report in conjunction with IDC just highlighting the impact of AI on the industry. And IDC referred to it as the AI Everywhere phase, right? With all the hype around AI, is it real? Is it warranted? We've already spent however much time talking about how it's impacting our business. Are we overreacting? Are we overindexing on the opportunity there? Or is it really that transformative? I think it's a great question. I mean, I'm beginning to show my age because I can remember when people used to talk about things called the Internet and what would be the eventual endpoint of what we could do on the Internet, then we can all kind of remember getting our first mobile phone back in 2007 and going, "Oh, this is a bit different than the Nokia I used to have. I now have an iPhone," and AI is this technology that has held promise since the 1970s. But the litmus paper has now been dipped, and it's like, "We're off. We're off to the races." It's hugely exciting, and all of our customers want to talk to us about what is the impact of AI on their businesses and how we can help them on that journey. I mean, you see the same. Yeah. I mean, there is obviously an awful lot of discussion about AI right now. There's a lot of media coverage and stuff too. I think what's truly seen as generative AI has kind of made AI become much more something from the masses. I mean, prior to generative AI, people didn't quite get what it was. It was you go onto Netflix and it recommended something. It was you bought a product. But the whole thing changed really with the breakthrough of generative AI kind of became into the hands of everybody. So you now have your kids, your parents, people are using it. And I think it's great because the adoption is really much stronger, but there's an excitement about it. I think there's a genuine excitement. And what happens is when it's in the hands of everybody, it's now making more demands of the clients we represent to how do they show up in the use of it and also the value that it can deliver as well. But it's truly genuinely transformational. I don't think there's any doubt about that. I think that democratization part of it is an important point, right? Because a lot of technology, and you talk about the Gartner Hype Cycle and all these things where technology goes through these waves, and it's like the opposite of blockchain, right? Blockchain is a technology that's around, and everybody's like, "Oh, yeah, we're thinking about blockchain." And it's always like, "For what?" Right? What are you really solving it for? And you didn't come up with a lot of good answers for that, right? Correct. AI comes in, and it gets that initial hype of like, "Oh, yeah, AI. Everybody's talking about AI." And then to your point, right, suddenly everybody can have it in their hand, and they can use it. And it's like, "Oh, wow. I can use this to figure out how to compile my grocery list, or I can do some really complex business thing with it." But the fact that it's applicable to every individual that interacts with it, not that everybody chooses to, but there is something it can do for literally everyone who touches it. Yeah. It's the oh moment. It's where someone sees someone else do something and go, "Oh, I didn't realize I could do that with that particular technology." It's very democratic. So if we think about it in the context of organizations that we work with, what are the challenges that they kind of have to work through in order to even think about embracing AI? Yeah. I mean, in the conversations we've had, I think, as Joe said, the generative AI, it was like everything was based around the chat interface. So we've gone through that window where everybody wanted to build a chatbot of some variation or another. But now what we're seeing is a journey whereby people want to embrace that technology further inside their organizations. And as you will have heard from other people today, what people are realizing is to actually get the true benefit, they're going to have to go a lot further into core and legacy systems where they've never traditionally gone. It's kind of like, "That system has always worked. I know it's got some data in it, but please don't touch it. I don't want anything going wrong with it," so now the conversations we're having with clients are a lot more about, "How do we go into that core? How do we enable some really exciting use cases by taking the technology and going to change things that you can't do?" Now, again, when the generative AI November release of ChatGPT came out, no one was thinking in that particular way, but that's what people have to do, and that's the journey we're on. We keep saying people have to go, organizations have to go deeper in their core. Why? Give us the top-level version of what does that actually mean? Why do they have to go deeper into their core? So fundamentally, if you think about it from the perspective of where is the data that the AI is going to work on, where is it resident? But then also, this isn't just about extracting that data and presenting a nice interface. It's if you wish to be transformative, you actually have to go and change parts of your system where the AI can go and potentially write data, read data, do all of these things. So it's something that you can't just do around the edges. We're not going to build a brand new shiny data lake off to the side and take five years doing it. Let's go and connect it to the core. Let's go and enable some really innovative use cases. And I think just to add to that, it was mentioned earlier, and I still think it holds true. AI alone will not bring it. Let's not have it just go and solve the wrong thing because I think you see that with technology a lot. So part of going deep into the core is also fundamentally understanding the reason you're doing it in the first instance, right? It's very hard to know, is your AI project being successful if you don't even have a good metric to measure that by. So a lot of conversations we have with clients around their adoption of AI starts with fundamentally, where's the opportunities that they can start with that they can then build up upon and kind of get that. But it goes back to what is it you're trying to do and the reasons why you're doing it because AI alone will not solve that particular piece. That's a really, really important point. Yeah. Julian hit the nail on the head. The first question has to be, why? Why are you doing this? What is the outcome you're trying to change? And the other thing about Endava's position around this as well is 80%, 90% of what you're going to do when you're delivering an AI solution is actually core engineering. It's cloud, it's DevOps, it's software engineering. It's all of the capabilities that we have in spades and we're great at. And now it's taking that expertise and knowledge of AI and putting it on top of that to deliver great solutions. Yeah. And there's two parts to the AI adoption for clients. It's going to be that productivity boost, which we spoke about earlier, and the adoption of AI inside solutions. So I think when we think about technology in the past, a lot of it was about what apps can I build with this thing. Now it's about still what value in apps can I build, but it's also what productivity gain can I give to all of the people inside my organization because everyone should be asking themselves, how much more productive could you be if 20% of what people were doing is now supported by AI? That's really transformational because it's net new opportunity to apply and then the AI inside the solutions as well. It's a two-pronged opportunity. That ties back to the core modernization piece to me because I think when we talk about that, we talk about core systems, right? And we think I come from the financial services space, so I think about these old core banking systems that have been in place for 20, 30 years. Certainly, that's part of it. But I think the other piece of it is thinking about it from a business process standpoint because while a lot of the application of AI is about how do you make things better, faster, the way you work through that is by looking through a business process, right? And saying, what are all the steps in the business process that we could make faster, better, easier, cheaper, whatever? And to do that, you have to have data available real-time. You have to consume the data to make a decision or inform a decision. You have to then put data back into something to drive the next step, right? You think about something as simple as a chatbot. If it's just surfacing information, that's easy, right? You can pull it out of a database and do that today. But if you're interacting with the customer and you're then consuming information and then putting it back into something, and it's a back-and-forth thing, that's where it starts to become about unless you have really got systems that can communicate real-time, API integrations between various systems internally that can surface that data at the right point, you can't really build these experiences that enhance that process flow, if you will. Yeah. Absolutely. I mean, this is the movement that a lot of talk now around agentic and multi-agent frameworks. We're actually working with a client right now, and we mentioned the Morpheus tool earlier, and we'll do a little bit of a video about it as well. But just to make this real from a client perspective, that's exactly to your example. In the financial services industry, large insurer operating in the U.K. and the U.S. at a very large scale, and they have lots of process because that's typically financial services industry. You have to follow a particular structure and rigor. Breaking that down, though, into its different parts and then seeing how people alongside AI can deliver on that. But back to your point, if you don't understand the fundamentals of the process in the first instance, that's the first piece of work we're doing with them, is identifying the process areas and then looking particularly at one area we can do the deep dive into, then actually apply multi-agent on top of it, because an agent, an AI, or a person doing the wrong thing is still going to have the same output. Right. Right. Well, I kind of shot myself in the foot before I asked this question because I just advocated for why I thought it was so important. But do organizations really have to do this, right? Is core modernization necessary? Can organizations maybe we put AI on the side and say, say you're not a believer in AI, right? Or you think it's 10 years away before it really has an impact. Do organizations really need to go take on this risky, complex core modernization? Yeah. I mean, I think there's an ongoing client that we're working with, and we were talking to them about c ore modernization, and we were talking about our belief that it's this really important thing that they have to do. And what they're saying is absolutely agree with us. I mean, they've got legacy. NET, they've got kind of old servers, Java, which was the new normal, is now seen as this real kind of legacy and clunky code. And they may have decided, or for whatever investment reasons, that they've not gone down the route of modernizing it, so they've just left it be. The issue that they have is the knowledge and the expertise around those platforms and those systems have gone. And now they can see that there's pent-up demand within the business to deliver change, and they have no idea how they're going to do it. And again, we'll show you something later related to some accelerators that we have. But the challenge they had is to even understand an estimate and produce an estimate of what was required to do core modernization was taking them 18 months. So even coming up with an estimate of what was required to do was just this impossibly long time frame. So the ability for us to come along, use our deep industry expertise, use the tools, use AI to be able to start breaking into that thing. All of a sudden, we're now in a process where it's kind of like every three months we can look at a different batch of systems, gain a deeper understanding of it, and start moving on that modernization journey. And when we sat down with the CIO and started walking through this, they were just completely blown away. It was like, "Can you really do this? Oh, we can actually do that now. Yes. Absolutely. And that was right. It was the, "Have a meeting on a Tuesday. Right. I want you back in on the Friday so you can talk to the rest of the team and we can get moving with this." Because all of a sudden, something they couldn't do, they can now do, and they understand the importance of doing it. It's happening, whether you're part of it or not. From our clients and the dynamic of not getting involved in the conversation, you can't not. The reality is, and David touched upon as well from a people perspective, the people that are now entering the marketplace are AI native. They will have used whatever generative AI engine they have to go through life. The expectation is almost as much when they go into a place of work, it's just going to become part of the norm. The reality is, whether you're hiring people to come in inside your organization, if you're a client and you're wondering how to get your arms around it, it has to happen. There's no real way. Alternatively, the challenge is you get left behind, right? Or you become less relevant. We've all seen that happen on numerous occasions. Yeah. That was an interesting observation for me here. David mentioned that today because I hadn't really thought about it that way before, of the talent that's coming into the marketplace or using these tools in their personal life. And so as they go into organizations, they're expecting them to be available and to be part of how they do things. So it's not just about the kind of top led, "Okay, as an organization, we need to do these things." It's also kind of a bottoms-up employee saying like, "Hey, wait a minute. Where are my tools?" Right? This is part of the toolset that I need. Yeah. It's like we're all of a vintage where you went into an organization and there were files in filing cabinets, right? I mean, think about that concept today. I'm sorry. I'm not I'm not holding on. Yeah. You're the same age as me. But the reality is that's the new norm. That's what's becoming the new norm. And even from an education perspective, colleges and universities need to be thinking about the next round of software developers that come into the marketplace. There's no point in rallying if you're a university against the use of those tools for submission for a piece of work because the reality is the expectation of people like us to hire people is that this is something that they're going to need, right? Yeah. All right. So let's switch gears a little bit. Let's talk about Dava.X. Matt, I know you were a big advocate and champion for Dava.X. Tell us a little bit about what that means to you. I know John hit it on earlier, but from your perspective, how does it fit into how we think to take things to market? How do you think of it from a technology standpoint, what it means? Yeah. I mean, fundamentally, as Julian said, that we're a very industry-focused go-to-market strategy. And the ability for people, clients to come and talk to Endavans who understand banking and payments and insurance, all of those things is hugely important. And we've got great talent in all of our delivery locations, and we organize ourselves inside of capabilities. But sometimes what you really want to do is say, "Where are the things we can truly help our clients kind of move forward and fundamentally accelerate the things that they're trying to do?" And so the Dava.X capabilities are really about bringing together some really smart people inside of our organization who understand what our capabilities are and getting that all lined up to do great things for our clients. You saw on the chart earlier that we group those around accelerate and invent. And acceleration is really about how can we help our clients move forward at pace right here, right now. And invent is really about we've got to have one eye on the horizon. Where are we going next? What are the big transformative things that we want? And inviting our clients to come and invent that future with us. And what's really exciting for me is I remember a meeting when we first started talking about Dava.X, and you're sketching things up on the page, and you put up Quantum. And everyone's like, "Why have you put Quantum up there? That's a theoretical. That's off in the distance." And yet, we've had commercial interest around the fact that we have people inside of our organization who have a point of view, optimizing quantum algorithms, those types of things. All of a sudden, you're kind of like, "Well, this is interesting. Where is this going to go?" So as John said, we'll constantly review what Dava.X is, but we're really going to look at it in terms of this is either going to be the new normal and becomes part of our core capability, or we're going to do some things we can really help accelerate our clients. And what you're going to see also is the synergy of some of those together. Like Matt described Quantum, imagine the power of Quantum on top of AI and the success that we've been having, especially from a generative perspective. But just kind of peeling it back just from the group that we have from an AI perspective that I oversee, it's also as fundamental of having a go-to-market team inside the group that has the industry expertise to apply to it. So some of the team that are there have worked in data science and heading up data practices inside large organizations so that they understand because AI is such a large thing, and it's challenging to say, "Okay, where do I start?" Being able to actually go in and say, "And this is how we're curating from a go-to-market perspective," is what does this actually mean then for if you're an insurance company? What does multi-agent mean if you're in the travel industry? Because not all of these things make sense depending on the industry and the customer that you're trying to meet. So having that lens with the Dava.X allows us to be able to go in and have more meaningful conversations because a lot of the times, the first question is, "Where do we start?" And that's one of the accelerators we've built is going into with that industry lens on it to say, "We've seen other customers like you have success in this area," and it becomes almost like the jumping-off point for the rest. So Joe, maybe go a little bit deeper into how do we deliver for our clients in the context. So Joe runs the Dava.X AI capability. What do those conversations look like with clients? What are the ways that we position AI to them or helping them with AI? Make it a little deeper there. Yeah, absolutely. So it varies, right? Some clients will have had more experience with AI, and they may have used it in the past, in which case they may be asking us for success or sorry, success that we've done in the past, but opportunities in a particular thing. So they might want to do more with touching the client, in which case that's a more clear cut, if you will. But what we're regularly getting in was back to the IDC reference earlier is, "Okay, where do I start?" So for us, we're starting to see a lot of these parallel paths happening, going into clients and helping them with how to introduce generative AI, particularly from a productivity boost perspective. And that is sometimes more a traditional rollout of a change management program, getting a pilot going, getting champions networks in place and that type of thing, and then celebrating the wins and all the other stuff that is a standard rollout program. That's usually a great starting point because that gives people the idea, "Go, oh, what if we did this?" They start building their little custom GPTs. It's a great starting point for, "What then could we do if we add AI on top of that?" And so it's almost like stepping stones. It's a journey, really, effectively. But by enabling your entire organization, it gives everybody the sense to then be able to bring those opportunities to the table. So sometimes it's us going to them. From a go-to-market perspective, we've seen success in the use of AI in particular industries in use cases. And sometimes we're asked to come in and workshop, and we present those opportunities. Sometimes it's working it down with them, what's applicable to them. But ultimately, it's about what's the pain points? What are the pain points that they're trying to solve? And then is AI the right tool or something else alongside AI to try and support them? But ultimately, it's the starting point of getting the right one and then building some momentum from it because it sounds too easy, but the reality is technology alone is never the answer. It's technology applied to a well-understood challenge that is really the one that's transformational. And by the way, with clients, that gives those people who sponsor those projects the opportunity to get another one of those projects because you build on the success that you have. Right. One of the things I'd say about that as well is while everyone has been talking about generative AI since whenever it was November 2023 or 2022, we as an organization have been delivering machine learning and data science projects for a very long period of time. So not only is there kind of heritage in what we do in terms of we know what we're talking about, but we are also passionate adopters of the technology for ourselves. So the conversations that Joe and the team have are things like, "Well, how can this impact software engineering?" Well, the reason we know the answer to that is because we're embracing that. We're looking at how it can impact our people inside of our organization. How can we enable things? So I can think of a case last week with David and I on a phone call, and it's how I was advising him on how he could use ChatGPT to do some data analysis inside of an Excel. This is deeply embedded in what we're doing on a daily basis, and clients want to know that. So when you go and have a conversation with a COO or a CTO, they want to know how you start that journey. Where do you go? How do you build up the champions? How do you engage in all of those processes? So there's a lot of why and how before you actually get to, "Oh, and the answer is this technology over here." Yeah. And it's bringing the people along the journey as well because that's the important part, right? And whether that's people inside your own organization or the people inside from a client's perspective as well, right? It's fundamentally by exposing people to the power of it. That's where the ideation comes. That's where the innovation comes for the next big opportunity. So when we zoom out a little bit and we think of AI as almost like a technology stack, right? The infrastructure, the models, there's lots of people doing those, and it's probably not us, right, from building the net new versus enabling it. But you get to the application layer, and that's where I feel like there's still a lot of ambiguity and a lot kind of to be seen as to what does that look like? Can you share some perspective on how you think about that and how you think about how we're engaging with clients around that, right? Yeah. I mean, we use the phrase the art of the possible, and I think that's correct, which is we've seen that journey that people have gone on where it's kind of like maybe there's an exciting chatbot, and it's kind of like, "Well, that does a particular thing, but it's not very exciting." So it really is being able to sit down with people and work out how do you embed this in at that application layer. And when technology works seamlessly and something smart happens, it's like magic is happening. And you might have AI at some point inside of that journey, or it could just be that cool engineering that was done by someone where they thought very deeply about how do I solve that problem in an efficient way. So, you hear us talk a lot about AI and kind of like how is that making an impact, but actually, it's delivering technology and having really smart people who are able to do that. It's just another tool, right? Absolutely. Yeah. And there's also, I mean, the growth of what we're seeing with regards to the breakthroughs in AI in terms of supporting from a software delivery perspective. To your question, it's extremely fast-moving, and it's really, really impressive. We're talking step changes in the matter of weeks with releases of different things that are coming out. Some of the frameworks are getting to be more specific into from a software delivery perspective, and it's really, really impressive. The things that we've seen even the last month has been we've touched upon it when we show the videos. I mean, we've started applying different models that are coming out, like OpenAI's o1 there recently. We've applied to a multi-agent perspective, and we're talking levels of accuracy, almost 20% improvement as a result in a software delivery perspective. So it's a really, really interesting space. Yeah. For those of you that aren't technology nerds, I'll even include myself in that like we are. No. Power Power to the nerds. Part of the reason people are so hyped up about AI is exactly what Joe just hit on. It's moving so incredibly fast from the complexity of the underlying models that as a technologist, when you start to think through what that can do and the power of it and the impact, it's tremendous. And I think it's easy to kind of gloss over that because all the conversations up here. But I think when you really get into how organizations are thinking about how they can embed it in things, the cost savings, the efficiency savings, it's going to be completely disruptive to so many things. So there's some logic behind the hype, if you will. But Joe, so we're going to do some demo videos here in a second, and the guys will introduce those specific demos. But just before we jump to that, can you tell us a little bit about how do we even get to these demos, right? How do we think about the things that we build or the way we think about the accelerators or whatever it may be? Why these things, right? Why do we build things at all? Why do we have demos of things to show as opposed to we just go build stuff for our customers? Kind of how do they fit into our go-to-market? It was touched upon. I mean, we have very bright, capable people, and that's not the sole answer. But the reality is when you've got people who live and breathe technology and are passionate about the space, these things bubble up to us, right? What we'll talk about from a video perspective is this: Morpheus is a multi-agent platform which we've built very much focused around doing this in heavily regulated industries where hallucinations are really, really something that you have to get your arms around and that type of thing. All right, Joe. Hold on. No, no, no. What does multi-agent mean? So, multi-agent is basically where you take a process and break it down into its different parts, and then how can you have teams of people and teams of AI agents work on the same thing? So we assign roles to each AI entity, right? Assign roles. roles. Yeah. And then they interact with each other and they do a thing. Working alongside people because obviously, it's still very, very important that you've got that safety net there. But in that particular use case, to go back to your question, we've had people who have been on the edge of this for like 12, 14 months, really excited about where the generative AI thing was going. And in that particular case, members of what was then the Dava.X AI community bubbled up to Matt in this case saying, "Look, this is something here. We're really excited about it." And it was a case of internally investing in that opportunity. So if you get bright, capable people who are following along and are getting excited about technology, and then you enable them to make an investment in building something, very much iterating on it, getting the feedback and adjusting it, it's really, really powerful. That's why these two particular use cases that we're talking about bubbled up ultimately from people who are interested in the technology. And we've now got to the stage with a large amount of investment up to this stage to build something like the Morpheus platform. What has the client reaction been to that? Because it's cool to build cool stuff, right? But ultimately, it's got to solve a client problem, right? Yeah. No, I mean, it's really, really impressive. To make this real from the demo perspective, what we're going to show is it's a very complex process in this case in the healthcare industry, supporting clinical trials. It's a lot of work, it's a lot of effort, and the standards around this is very, very arduous to get your arms around. So it takes a lot of people power and a lot of effort to get through these processes. So what we've done from this use case using Morpheus is what does the same thing look like when it's done alongside people, but using this agent approach that I talked about? The impact is absolutely incredible. We're talking, and it'll be in the video, which we'll show in a second. It's step change. It's the ability to potentially do more clinical trials with the same amount of people, which in that business is a massive, massive opportunity because that is the more trials you do, the more products that you can produce. It's really an absolute differentiator for that industry. So that's what the video, just to tee it up. Now, Morpheus, just to finish up before we play the video, this is a use case we're talking about from a health perspective. We've also worked with the insurance industry at the moment as well in use cases. So Morpheus itself is not tied to any one particular industry, but what we'll show is its use in the health industry. And if that's okay, I'll get to go ahead and play the video. Pharmaceutical research and development are costlier than ever at over $2 billion per therapy. However, leveraging artificial intelligence can reduce both risk and costs. Traditionally, after clinical trials, two skilled clinical programmers interpret requirements and write code compliant with complex standards such as CDISC. This creates data outputs, which are then checked for anomalies. Generative AI could write clinical code; however, its tendency to make mistakes makes it unviable in a setting with high accuracy requirements. It could be used as a programmer's co-pilot, but this doesn't necessarily reduce costs. Agentic AI offers a solution. Our multi-agent model creates teams of AI agents with different roles to understand requirements, write code, check code, run code, compare dataset outputs, and ensure code is compliant. These agents collaborate and learn, refining the output for accuracy. In collaboration with pharmaceutical industry leaders, we train our agentic AI against clinical artifacts, proving accuracy while ensuring auditability. This approach has enhanced the human experience, reduced risk, and is expected to offer immediate annual savings of more than $20 million. So just to finish up, in that particular use case, that's a use case that's applicable to everybody in that industry. So that's why we're really excited about what it can potentially do, working with a client at the moment. But as I said, it's a meaty problem solved from an industry perspective, and that's where the power of this agentic opportunity is. Any industry has complex processes. What would it look like if those complex processes can be broken down and then teams of people and AI agents work together? Think about the opportunity and the real impact of that. So more to come on that, obviously. We're excited about that, and I'll just pass over to Matt who's going to talk about it. Just quick. So this is proprietary technology that we've built that we can use for clients across whatever. Absolutely. Yeah. It's one of the accelerators out of the AI pod, and we're excited because from when we made the press release back in April, we're now actually at the point of deploying it with clients in production, but it is proprietary to what we've built, yeah, and it's really, really, really cool. Even though you said it has to, we are really proud of it, but it's also something that's genuinely solving real business problems as well, which is cool as well. Awesome. Yeah. So I get to share something which I'm really, really excited about. So as we've gone through today, we've talked about things like our great people in terms of the delivery locations, the skill and talent that we have access to. We've talked about the Dava.X capabilities, how we can help people move forward. And we've also talked about the accelerators that we have. Now, some of those accelerators have been in existence in Endava for 10, 15, 20+ years. The merger with GalaxE brought a whole swathe of new accelerators into there, so you'll see names like Maps and Dash and Quality and all of these types of things. And by bringing these capabilities together, what this allows Endava to do is focus on what is the problem we're trying to solve for our clients. So we're not selling them as products. We're really saying, how can we use these things to make ourselves be smarter, make better decisions, de-risk outcomes, and all of these types of things? So with the great work that was being done in and around Morpheus, the question was kind of, well, how can we apply this to ourselves? What can we do in terms of embracing this technology? So what we've done is we've taken a number of our accelerators, and on top of that, we've applied large language model technology. And it's currently called Compass. It's a working title because I'm no longer allowed to name things inside of the organization because I come up with terrible names. But what it does is it gives the ability for us to go deep in understanding systems. So again, back to that core modernization activity. We can consume code bases. We can consume software assessments. We can consume information about what exists inside of an organization and then give every single person on the team the ability to talk to a large language model and get information. So a developer can talk as if the person is a solutions architect. A solutions architect can talk to it like it's a business analyst. A business analyst can talk to it as if it's a tester. And within this framework, not only can we get insight in terms of what do these systems do, but most excitingly, it puts us in a position where we can start doing forward engineering. So if I have to make a change to a core platform, where do I need to make that change? And the model can help us understand the database, the code that needs to change, and how would we make these changes inside the organization? So when we look at this, we will see other people in the marketplace talk about, well, we're applying large language models in our software delivery process. But what we're really doing is building on top of the accelerators, which are truly unique to us. No one else has these accelerators. So hopefully, I was told this was quite a technical video when I showed it to some people last night. Any questions? I will be here. I'll be out in the coffee, which will be coming up shortly. So let me set it up in my own words. So core modernization, right? Helping organizations figure out how to transform their technology to some future state. It's very complex because of lots of ambiguity about current systems. This is helping provide visibility into current systems through some tools that we have, capabilities we have, combined with a way to kind of inquire about that information in a really interactive and kind of human way. Correct. And the demo that you're going to see was based on the original large open-source project that we built on top of. It is being used by clients. So this isn't just a smoke and mirror demo for the purpose of you guys in the room. This is something that we're now beginning to have very meaningful conversations with clients. So earlier, we talked about that financial services client and him having that, "Oh, wow," moment. Now I can go and do a core modernization. It was directly off the back of showing and explaining what Compass is able to do for us. Awesome. Run VT. Will it roll? Endava Compass brings together data from multiple sources, code bases, documentation, dependencies, and metadata powered by multi-agent AI that leverages Endava's deep modernization and transformation experience, incorporating insights from Maps and Dash and cross-referencing data from Chronos. Now, let's observe Compass in action. Now you can see the platform identifying and extracting business domains and subdomains directly from the code base. Here, Compass translates technical structures into business terms, organizing components by their functional areas. This automated approach provides an overview of business domains without requiring manual effort to read through code. Once the business domains are extracted, you can observe Compass mapping these domains to the underlying technical components. Each business domain is matched with relevant code modules, creating a cohesive view of how business functionality aligns with technical architecture. This saves considerable time that would otherwise be spent tracing these connections manually. Now you can see Compass diving into a specific business process. The platform generates flowcharts and sequence diagrams to visualize processes embedded within the code. By automatically deriving these views, Compass significantly reduces the need for manual reverse engineering, giving teams a straightforward look at core processes. Here, we see the platform's multi-agent AI system at work. These specialized agents collaborate in real time, constantly cross-referencing new data, such as code updates or documentation changes. This means that insights are refreshed continuously, ensuring the system view stays up to date with the latest changes. The agents function independently but contribute to a shared understanding of the system, supporting real-time insights for all users. As we observe, Compass provides different users with the specific insights they need. Developers can review code dependencies in detail while architects get a structured view of the architecture, and business analysts can locate features with a clear connection to business value. Each role benefits from the same system insights, but in a format tailored to their needs. As we've seen, Compass is more than a tool for analysis. It's a continuous resource that grows with your system, supporting teams as they adapt and innovate. By connecting to repositories and documentation in real time, Compass provides clear, immediate insights, empowering teams to move forward with confidence. For those working on complex transformations or modernizing legacy systems, Compass delivers up-to-date insights grounded in years of expert understanding, no matter the language of your code, your documentation, or even your team. Compass is built to empower every user speaking your language. Estoy cansado de escribir. Resúme en español. Por supuesto. Este sistema de gestión de atención médica. Supporting your goals. So one of the things I want to say about that demo is that last bit at the end uses the OpenAI real-time voice interface. So as much as the IT industry works around English as being the core language, what this is actually able to do is to allow our teams, wherever they are in the world, to use their own language to be able to do that interaction but still be able to generate the code bases that they have. I think that's a brilliant example of, A, how you're focusing on the people who actually have to make these changes, but also allowing them to really be their best and leveling up using technology. Brilliant, brilliant example. Awesome. Feel free to ask Matt and Joe any questions you have during the break. Just a bit, we're going to take a break here in a second, but just as a note to those online, because we do have a bunch of people tuned in online, we're going to take about a 10-minute break. I don't know exactly what time it is. A 10- to 15-minute break. And then we'll be back online 10 minutes. So we're going to repeat these demos for those of you online in the room as well. If you want to see them again or ask questions, you can. But we will take a 10-minute break, and we will come back then. Thank you. Thank you. 2019, our stated ambition, right from the outset, was to become our customer's favorite insurer. A big enabler of this is being able to make better decisions using data and technology. Working with Endava, we created a data spine which allows us to link and expose all the relationships across our application, which was not previously possible. We also built a visualization tool which sits on top of that, which allows us to clearly see all of these business relationships and search on specific identifiers. Leveraging the technical expertise which Endava brought to the project, we're now able to start offering our users a unified user experience based on a componentized architecture. Collaboration with Endava has allowed us to stay on track and deliver against our long-term business goals while responding to changing business and market conditions. We partnered with Endava to enable access into the cloud. The cloud brings us the cost performance and sustainability benefits of Arm-based servers. Once proven, Endava helped us build a high-performance job execution engine as part of our technology stack, allowing us to automatically scale up and down to meet demand. This used Arm and legacy-based compute platforms. We wanted to create a digital customer experience, a good customer experience that enables them to order a car fully specified online without any pain points and to pay a fixed monthly fee for that car. With Endava, it was a lucky meeting, but I think it was two companies that had a lot in common and shared values. And your abilities in the sort of cloud and particularly Microsoft and Azure space meant that you had the right credentials for us. But more than that, it was the right attitude and a willingness to work together as a partnership. We're doing a subscription. We're doing peer-to-peer sharing, and we're doing mobility all through one app. One of the things that we've seen now is AI has really changed the game. AI creates a lot more opportunity for the way our customers run their businesses and interact with their customers. We recently partnered with IDC to launch a co-branded report Navigating the Digital Shift. We thought that it was crucial for our customers to be able to see what was truly happening within this technology landscape and how the shift could potentially affect their business. 70% of organizations can see that generative AI is either disrupting them now or will disrupt them very shortly. With this next wave that's coming through, AI needs to get at the data and the processes that sit in the core, and so there's a big change going on where we need to get into the core, modernize the core, create a digital core. 44% of businesses consider core modernization a key priority. What the report shows is it's really important to understand your data. A third of organizations believe that they can't make progress on their AI journey because they have poor data quality or a lack of understanding around their data sets. We've been working with customers for over 20 years. The reason we've had relationships for that length of time is it's built on trust. Because with AI, you always need a human in the loop. When we started Convex in 2019, our stage of ambition, right from the outset, was to become our customer's favorite insurer. A big enabler of this is being able to make better decisions using data and technology. Working with Endava, we created a data spine which allows us to link and expose all the relationships across our application, which was not previously possible. We also built a visualization tool which sits on top of that, which allows us to clearly see all of these business relationships and search on specific identifiers. Leveraging the technical expertise which Endava brought to the project, we're now able to start offering our users a unified user experience based on a componentized architecture. Collaboration with Endava has allowed us to stay on track and deliver against our long-term business goals while responding to changing business and market conditions. We partnered with Endava to enable access into cloud. Cloud brings us the cost performance and sustainability benefits of Arm-based servers. Once proven, Endava helped us build a high-performance job execution engine as part of our technology stack, allowing us to automatically scale up and down to meet demand. This used Arm and legacy-based compute platforms. We wanted to create a digital customer experience, a good customer experience that enables them to order a car fully specified online without any pain points and to pay a fixed monthly fee for that car. With Endava, it was a lucky meeting, but I think it was two companies that had a lot in common and shared values, and your abilities in the sort of cloud and particularly Microsoft and Azure space meant that you had the right credentials for us, but more than that, it was the right attitude and a willingness to work together as a partnership. We're doing a subscription. We're doing peer-to-peer sharing, and we're doing mobility all through one app. One of the things that we've seen now is AI has really changed the game. AI creates a lot more opportunity for the way our customers run their businesses and interact with their customers. We recently partnered with IDC to launch a co-branded report navigating the digital shift. We thought that it was crucial for our customers to be able to see what was truly happening within this technology landscape and how the shift could potentially affect their business. 70% of organizations can see that generative AI is either disrupting them now or will disrupt them very shortly. With this next wave that's coming through, AI needs to get at the data and the processes that sit in the core, and so there's a big change going on where we need to get into the core, modernize the core, create a digital core. 44% of businesses consider core modernization a key priority. What the report shows is it's really important to understand your data. A third of organizations believe that they can't make progress on their AI journey because they have poor data quality or a lack of understanding around their data sets. We've been working with customers for over 20 years. The reason we've had relationships for that length of time is it's built on trust. Because with AI, you always need a human in the loop. When we started Convex in 2019, our stage of ambition, right from the outset, was to become our customer's favorite insurer. A big enabler of this is being able to make better decisions using data and technology. Working with Endava, we created a data spine which allows us to link and expose all the relationships across our application, which was not previously possible. We also built a visualization tool which sits on top of that, which allows us to clearly see all of these business relationships and search on specific identifiers. Leveraging the technical expertise which Endava brought to the project, we're now able to start offering our users a unified user experience based on a componentized architecture. Collaboration with Endava has allowed us to stay on track and deliver against our long-term business goals while responding to changing business and market conditions. We partnered with Endava to enable access into cloud. Cloud brings us the cost performance and sustainability benefits of Arm-based servers. Once proven, Endava helped us build a high-performance job execution engine as part of our technology stack, allowing us to automatically scale up and down to meet demand. This used Arm and legacy-based compute platforms. We wanted to create a digital customer experience, a good customer experience that enables them to order a car fully specified online without any pain points and to pay a fixed monthly fee for that car. With Endava, it was a lucky meeting. But I think it was two companies that had a lot in common and shared values. And your abilities in the sort of cloud and particularly Microsoft and Azure space meant that you had the right credentials for us. But more than that, it was the right attitude and a willingness to work together as a partnership. We're doing a subscription. We're doing peer-to-peer sharing, and we're doing mobility all through one app. One of the things that we've seen now is AI has really changed the game. AI creates a lot more opportunity for the way our customers run their businesses and interact with their customers. We recently partnered with IDC to launch a co-branded report navigating the digital shift. We thought that it was crucial for our customers to be able to see what was truly happening within this technology landscape and how the shift could potentially affect their business. 70% of organizations can see that generative AI is either disrupting them now or will disrupt them very shortly. With this next wave that's coming through, AI needs to get at the data and the processes that sit in the core, and so there's a big change going on where we need to get into the core, modernize the core, create a digital core. 44% of businesses consider core modernization a key priority. What the report shows is it's really important to understand your data. A third of organizations believe that they can't make progress on their AI journey because they have poor data quality or a lack of understanding around their data sets. We've been working with customers for over 20 years. The reason we've had relationships for that length of time is it's built on trust. Because with AI, you always need a human in the loop. When we started Convex in 2019, our stage of ambition, right from the outset, was to become our customer's favorite insurer. A big enabler of this is being able to make better decisions using data and technology. Working with Endava, we created a data spine which allows us to link and expose all the relationships across our application, which was not previously possible. We also built a visualization tool which sits on top of that, which allows us to clearly see all of these business relationships and search on specific identifiers. Leveraging the technical expertise which Endava brought to the project, we're now able to start offering our users a unified user experience based on a componentized architecture. Collaboration with Endava has allowed us to stay on track and deliver against our long-term business goals while responding to changing business and market conditions. We partnered with Endava to enable access into cloud. Cloud brings us the cost performance and sustainability benefits of Arm-based servers. Once proven, Endava helped us build a high-performance job execution engine as part of our technology stack, allowing us to automatically scale up and down to meet demand. This used Arm and legacy-based compute platforms. We wanted to create a digital customer experience, a good customer experience that enables them to order a car fully specified online without any pain points and to pay a fixed monthly fee for that car. With Endava, it was a lucky meeting, but I think it was two companies that had a lot in common and shared values, and your abilities in the sort of cloud and particularly Microsoft and Azure space meant that you had the right credentials for us, but more than that, it was the right attitude and a willingness to work together as a partnership. We're doing a subscription. We're doing peer-to-peer sharing, and we're doing mobility all through one app. One of the things that we've seen now is AI has really changed the game. AI creates a lot more opportunity for the way our customers run their businesses and interact with their customers. We recently partnered with IDC to launch a co-branded report navigating the digital shift. We thought that it was crucial for our customers to be able to see what was truly happening within this technology landscape and how the shift could potentially affect their business. 70% of organizations can see that generative AI is either disrupting them now or will disrupt them very shortly. With this next wave that's coming through, AI needs to get at the data and the processes that sit in the core. And so there's a big change going on where we need to get into the core, modernize the core, create a digital core. 44% of businesses consider core modernization a key priority. What the report shows is it's really important to understand your data. A third of organizations believe that they can't make progress. Everybody, we're going to go ahead and get started. It's like everybody can make their way to a seat, please. For a lack of understanding around their data sets. We've been working with customers. Okay. All right. I'm going to let the audio guys figure out which of these mics they hear me okay? Cool. All right. This feels really weird because I'm slightly ahead of everybody up here, slightly behind, and I'm standing at a lectern. So I feel like I need to give a speech about something, but I definitely am not going to do that. So we're really excited about this next session because a lot of what we talk about or have talked about today has been internal. This is how we think about things. As Endava, this is how we're going to market. But we really wanted to bring in some of our customers and partners to be part of a conversation, and that's what we're getting ready to go through today. So excited to have them share some of their perspective. We have invited John back on stage as well to share some of the Endava perspective as it relates to that and just really talk through some of these same themes and trends that we've been talking about, but share a kind of broader perspective around that. So I'm going to start by having each of you introduce yourselves because while I was supposed to introduce them, I actually like it better when you introduce yourself in your own words. So why don't we start on the end, and Jim, you go first. Thanks. Good morning, everybody. My name is Jim Grech. I am at TD Bank right now as the Chief Technology Officer, but have had that role over the last 20 years at a number of different financial institutions and come to the Endava relationship through the GalaxE acquisition. I have a 25-year partnership with GalaxE at almost every stop that I've had in my career. And just going back to some of John's comments talking about automation, I have admired and used the year-over-year acceleration and development of the automation tools that are part of that GalaxE toolkit. Even going back to some of you probably aren't old enough to remember, but Y2K remediation was one of the first opportunities to really think about scanning and automation tools on how to deal with that. That's where that started and has really evolved every single year, which you can't really find too many other places, examples like that in the industry. Nice to be here. Happy to be here. Hi, everyone. My name is Nicole Pinto. I currently work at Stripe on the payment acceptance strategy team, working with some of Stripe's largest customers on optimizing their payments performance. I've been in the payments industry, payments and banking for 10-plus years now, and I'm looking forward to being here. Endava is one of Stripe's partners. We have a lot of shared customers that work on digital transformation initiatives. So excited to talk about that today. Hey, I'm Jonny Elliott. I am the CIO at Toyota Racing Development. So I've been there about nine years now. We do all of the motorsports in North America, representing Toyota and Lexus. We've been partners with Endava for a couple of years now, and they've helped support us in our single-make series, the GR Cup. Hey, everybody. Excuse me. David Smock, Solutions Engineer at OpenAI. I've been at OpenAI for 14 months. I've been at Slack, Salesforce, and then before that in the data center world at EMC. So I work with our strategic customers to bring OpenAI solutions to life, consult on them, talk about architecture and security as well. And I think you've heard a lot of Endava's use of OpenAI technology today, both from internal productivity with ChatGPT as well as building it into their platforms. So happy to be here and talk to all of you. Thanks. I won't bore you with an intro because you all know who I am. I wanted to hear it. Thanks, everybody. And thank you all for being here. We really appreciate you being here and the perspective that you will bring. To start things out, again, just to set the scene a little bit, we talked a lot about the changes the industry is going through. We talked a lot about how organizations need to transform. I guess let's start out by talking about what makes that hard, right? And Jim, just to start with you a little bit, maybe on your perspective, what are some of the barriers that organizations typically face when they need to adapt, when they need to adjust to all the change that's happening out in the market? It's a great dilemma that the industry, especially regulated industries, are going through. And I would break it down into a couple of different areas from my point of view. One is the ability to enable the business to take advantage of the tools. And the difference between that today versus five years or ten years ago is the rapid acceleration of innovation tools. The ability to be ahead of what the business requirements are as business becomes more technically savvy or even worse off, think they become more technically savvy and are expecting more faster. We need to be on the technology side. You need to be preparing for the next transformation much earlier in the process. And in some of our industries, we don't get funded that way. You get funded by business units that are looking for revenue generation opportunities, in this case around GenAI. You needed to be working 18 months, two years ahead of that in order to deliver it in a safe and secure way. That's a pivot to the next question. One of the things that we haven't heard so far in the conversation is you need to adopt a GenAI and be secure and safe using it. Goes back to data. We talked a lot about data. Data governance is critical because in a regulated company, you can't mess around with customer data, whether it's in the healthcare or the financial capabilities, and you're building data governance rules along with your ability to innovate at the same speed, and those two things compete with each other, and the third piece, from my perspective around security, would be the bad guys are using GenAI too. And from an enterprise protection or from a cyber protection perspective, you've got to be very careful that you're not opening up new vectors for attacks from the bad guys. So those are the things that we've got to be careful of. One other different vector that you've got to think about, which I didn't hear too much conversation earlier, was the assumption of where you're going to support the compute to back up GenAI. I think a lot of people would assume that you go to the public cloud for it. I think that's a mistake to just assume that's the case. And there are different models that we will be evolving over time. If you lock yourself into one support model, it's very, very possible that two or three years down the road, you're going to have to get yourself out of it because it's just not something that you can sustain itself and can afford. So those are some examples of the things you've got to be cognizant of as you're enabling. We've been talking a lot about core modernization, but how do you think about that in the context of some of those things you just mentioned? Yeah, well, it's a perfect example that it takes years to modernize a core. If a big bank has a $2 billion investment portfolio year- over- year, you hope that 80% of that is going toward modernization. The fact is, because you're delivering revenue-generating capabilities that far too often you're developing on legacy platforms that you prefer to be eliminating from the environment. But because you have deadlines, you need to support a quarterly revenue generation capability. You're developing capabilities on platforms that you want out of your environment, and you're extending that requirement. So to be able to use some of the GenAI capabilities that will go after core modernization is pivotal, but there is the push and the pull of what percentage of your development skills and your development budget can go toward it. And we don't have the answer for it yet. We're still navigating it. It's why you need partners to be able to go help you get there. Awesome. Nicole, if we kind of switch to you a little bit. I happen to also be a payments nerd and have worked in the payments space for 20+ years at this point and love the space, but it's constantly changing, right? There's constantly things happening, not just new technology available, but the business changes a lot as well, right? What are some of the kind of more complex things you've seen businesses having to deal with in the payment space as they make it through this wave of technology that's coming through? Sure. I think it's twofold. I think one is how to build the value story, if you will, for moving from, let's say, a legacy payment processor to a modern provider and modernization of the tech stack. And then two, it's sort of a case of build, buy, partner. And how much control does a business want to retain when thinking about adding additional partners to their payment stack? So we might hear a popular buzzword of payment orchestration. Do I want to build that middleware layer myself and manage all the dials and knobs, or do I want to rely on a partner like a Stripe or an Adyen to kind of manage that for me and kind of harness the power of their data network? So I think that's some of the trends that we're seeing from my perspective. I was not expecting you to drop Adyen in that. There's many modern providers out there. Fair enough. Fair play. Jonny, what about in your organization? How have you seen moving past some of these barriers? How have you guys embraced kind of innovative technology? Yeah, we had a couple of ways. I mean, the first one doesn't seem really bold or innovative, and that's embracing the internet, which is a bit of a stretch. But in racing, certainly when I started at TRD, racetracks had really bad internet connections, and people didn't want to use software or tools that were connected to the internet. So it was a no-go for that kind of thing. But we embraced it. We went hard on it and embraced the cloud. And we got a lot of races from it. It It was good. Secondly, on the core modernization piece that we've talked about much today, we're currently doing that with Endava. We have a very old.NET on-prem application that has been developed over 17 years by one developer. It keeps me up at night, the risk involved there. But yeah, it orchestrates a lot of what we do, sort of sits alongside our ERP system. And moving that into a new architecture is going to be massive for our business. So really looking forward to that. It's going to enable other capabilities that we want to do in the manufacturing space with some computer vision models, with sort of defect tracking on parts and parts lifecycle management. So yeah, it's going to be a big factor for us. Awesome. David, you sit in the middle of a lot of conversations, I know, about helping organizations figure out how to embrace AI in particular, but new technology. What are some of the biggest challenges you've seen and how you've seen organizations kind of move past some of those challenges? Yeah. It's not just regulated industries. Everybody, when we meet with them, wants to talk about, "I want to be in the headlines in a good way. I want to be the cost savings or the productivity gains or the new innovation. And I don't want to be in those other headlines." And I think there's some paralysis involved. It's how do I get started with it? A lot of times, it unfortunately might already be started without it being sanctioned, which is not great. This should be something that's controlled and embraced. I think the best customers that we work with and companies that we talk with have embraced a top-down and a bottom-up approach. You need to have the top-down permission from leaders to be able to embrace this and experiment. Hopefully, those leaders are using it themselves. It's been great to hear that today. You need to embrace it to understand it. You don't want this to be an internet or a PC revolution where all the new people are getting the knowledge and the advantage, and everyone running the place is kind of clueless about it. So this is something that everyone needs to embrace. And that top-down permission might be on a couple of initiatives, but then that permission gives the bottom-up experimentation room to grow. Some of our best customer stories that we have out there came from a subject matter expert who's living a workflow every day. And they understand the best way to have generative AI help them accelerate, be their super assistant, but you can't have that be in the dark. So that's like getting started. And then the other part is we actually tell people if they come to us and say, "We just want to automate everything, AI everything." That's a bad idea. This needs to be a journey. There needs to be evaluations every way. And I think it was said earlier about checking results. You need to make sure that this is actually helping you, but that's a way to make sure that this is working and to build the capabilities as you go in steps. So we like the human in the loop, the slow steps toward it with evals all along the way so you know what you're doing. Getting guidance on that is, of course, key. Nobody has a degree in generative AI. 27 years ago, we called it ML, but you couldn't call it AI. But everyone's learning as we go. And I think that's the best thing. You share the stories, you experiment, you give permission, you check with your peers and the industry, and measure the results as you go. Awesome. And John, we've been helping customers for 20+ years through these transformations. What are some of your key takeaways as to things you've really seen help clients kind of make it through that journey or get past the initial discomfort? Yeah. So I mean, the Endava approach, I touched on it a little bit earlier, which is the getting engaged around the ideation of how the technology can be applied in a specific client situation. What is the problem that we could solve with technology? What are some of the examples that we have when talking to a client that we've proven out in other parts of the industry or different industries quite often to help the client get their heads around perhaps what are the problem areas that they want to prioritize? And then having got that ideation flowing, how do you actually start moving that through the proof of concepts, getting the business cases lined up that I touched on earlier? And that's key. We trust the engineering pathway. We can see that we're going to get business benefits that justify what we're investing in doing this and then get it into production. And I think that process is even more crucial for this next wave than it was for the last 20 years as we've been building digital transformation. More critical because there's more work to be done on ideation of new product and capabilities, but also there's more work to be done on the engineering and getting into production. And the key to what we do in terms of ideation to production is joining all that up and integrating it. The industry traditionally has had one group of perhaps creative-type agencies that do ideation or strategy organizations. And the stepping stones from that to engineering have been difficult because when it gets to the engineers, they go, "I wouldn't have done it that way. You're not using technology in the best way," etc., and so you lose something in that transition. By employing an Endava, you get that integrated. Yeah. Not to throw shade on McKinsey or any other type of organization, but you often hear the narrative of the deck that you paid a lot of money for, and you get and the executives are really happy, and then they're like, "What the heck do we do with this?" Right? How do we actually make it real? I think that's part of our differentiator, right, is the strategy that we build comes from the angle of thinking about how we will build it as we're thinking about the strategy to go do. They're not separate things. Correct. All right. We talked earlier about the IDC report and this kind of idea of the AI everywhere era. I'd love to get some of your perspectives on just how AI is impacting your specific kind of industries that you work in. So Jonny, to start with you, how do you see AI kind of impacting the automotive space? What are some interesting things that are happening? I'd say the most obvious opportunity there is achieving L5 autonomous driving. It's the goal, I think, that we're going to get there. I think we're moving very quickly towards it. I think there was a period there, maybe a couple of years ago, where it didn't seem like it was going to come that quickly, and maybe people had started moving away from it. I think that's maybe changed in the last couple of years with how fast everything has moved. I think people are feeling more confident about it being possible and being possible soon. So I think, yeah, from that, that's probably the biggest one. But I think aside from that, just in the idea of AI being applicable to everything and everywhere in the manufacturing space, like I mentioned some of the stuff that we're doing at TRD with being able to use AI for defect and QAing of parts, similarly with manufacturing of the road cars. You make better cars, more reliable, simple things like that. And then the design process as well, something that we do in racing is CFD. We are running a lot of CFD studies to make the car faster. They do similar on the road cars, make it more fuel efficient. What is CFD? Computational Fluid Dynamics. So you put the car. Still don't know what that means, but. So, you put the car in a wind tunnel, so you measure it, or you can do it on a computer. So, you simulate the wind going over the car. So, you use a lot of computational power to do that, but AI helping in that reduces the amount of effort that needs to be done and can get to an answer quicker. So, similarly, in the design space for the road cars, I think it helps as well. Awesome. Before I go to you, Jim, next, I have to give a programming note that I forgot to give at the beginning, and that's to the people that are watching online. We are going to do a Q&A at the end of the session today, and we need you to submit any questions. I'm not talking to you all. I'm just looking at you all in the room. I'm talking to an invisible camera somewhere. We need you guys to submit your questions so we have them in the queue. So apologies for that diversion, but I forgot to say that at the beginning. So Jim, what about in the banking space? Yeah. So right now, the industry is really focusing on solutions that don't interact with the end customer directly. So what does that mean? We spent a lot of time. The first big GenAI pilot was to automate some of our contact center knowledge-based systems. And it allows the contact center agent to be much more efficient and much more fluent, and answers questions for them, but they are still the engagement with the end user. One of the reasons that's happening is because the industry is creating what those data governance rules should be. And like was just said, there are no degrees or standards yet, but the banking industry is kind of identifying that there needs to be a data governance standard so people aren't making up their own. So there's a lot of engagement with regulators and the interested parties around it. But while that's happening, we're spending a lot of time on making our associates far more efficient in addressing them. So what does that mean? That means FAs will be able to engage with a far higher percentage of their book. In the past, maybe 10% on a monthly basis might have been a good metric for an FA. Now you're going to be able to hit 90% with the tools that you have. On the tech side, there's pipeline automation, deployment automation. The GitHub Copilot is something we're spending a lot of time on how to enable our developers to be more effective. The demos that you shared show a lot of similarities, and there's a lot of opportunity there. We're also running hard with our Office 365 Copilot to make our associates far more productive during the day. There's some limits to how fast we're going there because if you're on a Teams call, I'm sure most of us have on a Teams call. Office 365 can listen in on a Teams call and give you a brief, but it also can collect information that should not be collected. So there's a lot of debate on how fast you go there. So we are not, for example, we are running hard with our Office 365 Copilot, but we're not including some of the real-time collaboration capabilities. So we're not collecting data that should not be retrievable or reviewable. Some conversation shouldn't be, and we got to be careful there. I think those are where we're going really hard while we work on that data governance capability. How do you find the balance between enabling the technology in the organization versus holding it back where maybe there's uncertainty or there's risk or whatever that you're not? Right now, and other banks are the same way, but we have a business digitization body. We have business functions that are about how do we digitize our customer experience. We have our line two operational risk accountability, and we have technologists, and those three bodies are represented in an AI governance board, and models are brought forward for approval and reviewed, and that body approves them, and there's a long list of people being very impatient in terms of wanting to explore it, but we're going at a manageable pace to mirror our very low risk tolerance culture and requirements. I was going to say fundamentally, it's a risk posture question, right? Right. And it comes back to what we talked about a couple of different times, the data governance, because it all has to do with data, and we got to be very careful with data we're using from our customers. So Nicole, jumping to you. What's interesting, I think, about your perspective is there's AI in payments, which I think I would argue payments has been using AI in a lot of the things that we've been doing for a lot longer than some of the other industries. But I think you also get exposure to lots of other industries through clients that Stripe works with. What are some of the things that you're seeing as some of the more interesting use cases? Yeah. One of the things I love about payments is that it cuts across industries and insurance, retail, media, etc. And so I like to think about how, or rather observe how AI is showing up in different use cases that Stripe can support. And so typically, people think of Stripe as a back-end financial services infrastructure provider, but we're seeing some really interesting results on the front end. So from optimized checkout, where AI is helping to dynamically determine the right payment method to surface, to optimize for conversion based on country, transaction size. So as you can imagine, for any business that's accepting consumer payments, that can be really powerful. And then also on the fraud component, we talked about that a bit today, but helping businesses fine-tune the right amount of good friction or bad friction that they want. If you think about collecting too much information at checkout, you might turn folks away, but you also want to collect enough information to prevent fraud and bad actors. So how do we leverage tools like Radar to help businesses optimize for that and optimize for conversion? And then I think in other areas where we are kind of seeing in the industry in general and at Stripe is the opportunity for embedded finance, which I think you're kind of alluding to as well, and how to streamline customer onboarding, due diligence processes to make them faster, but also more accurate. And then also credit underwriting use cases anywhere where there's that sort of embedded lending or payments component. Cool. David, what about you? Any specific industries that stand out that have really interesting use cases or things that you've seen? Yeah. It's fun because I get to talk across. We're not fully verticalized, so I get to have multiple industries in a day, which is fun. Finance has a lot, and it's interesting. I think there's a lot of horizontal use cases. Everyone needs to do customer support. If you've got coders and engineers, there's lots of ways to do it. I think in a retail e-commerce point of view, you've had personalized shopping experiences for a while. Something we've seen lately, which is super cool, is you get more engagement with those personalized results when it's explained why it's personalized to you, and it's not so much about those ML algorithms on the back end that are doing the recommendations already, but you can take that and explain to a shopper or a job seeker, whatever it might be, here's why these suggestions are relevant to you. Companies that have started embracing that are seeing much more engagement. Completely different media and entertainment. There's a lot that's happening with generative content. Our point of view is that this should be helping the creators create more things, and that's a lot of what we work with there. I think that's just a budding area. And then for me personally, education, I think, is a big untapped market, controversial market. But my kids are in elementary school. They're already using take-home computer game platforms to do learning, to do digital learning. So it's not a far leap when I've worked with them on this to engage with generative AI, the voice that you heard earlier, as a way to have a tutor that's personalized to you that augments that in-class education. And then for the rest of the world, there's a huge opportunity. If you talk to somebody in the Global South of using generative AI to uplevel education, improve outcomes, improve fairness and equal outcomes, I think there's a lot to come there that is just getting started and have to deal with regulation, but places to watch that are going to make a measurable impact on the world, I think. That's awesome. So John, we hit financial services, payments, automotive. What you got? What's left? Where else are we seeing impact of AI? Let me throw in healthcare and life sciences. AI is helping in and starting to help in drug discovery, in clinical trials, as we saw on the demo earlier. We're finding a lot of opportunities with healthcare professionals where you can bring AI in to actually start to reduce a lot of the essential admin that they're doing as part of their jobs and release them to have more time actually spent with patients and with making a clinical difference. And the other healthcare arena is escaping me. There was another healthcare example I was just going to pull out. I'll come back to it later if I want. I'll give you a chance later. You've got an in with the moderator, so I can come back to you and give you a chance later. If we kind of zoom out from that and we think about one of the things we saw from this IDC report was a call out of how big strategic partnerships were a key part of really driving through these technology initiatives and helping embrace them. Rarely is an organization doing something with just one entity anymore, right? It's all about how do we take all these different pieces, whether they're products or they're capabilities as an industry or industries, right? And so it'd be great to get some of your view as to how strategic partnerships fit into that, right? Fit into your business and how you're kind of going to market. So David, just to start with you, how do you think about partnerships? How do they fit into the OpenAI model? Yeah. It's how we expand out there and have this impact. We are not a super large company, and at least half of our company are researchers. They have nothing to do with coming out in the field. So we don't scale, and we're not really designed to. We work with customers. We bring thought leadership. We have some architects who will work on new use cases. But the way that the impact of any of this technology gets out there, most companies don't have this talent in-house or they need it augmented, and so our partners do come out with the strategy, the use case, but bringing frameworks, these concepts of evaluations, hands-on keyboard to help build some of this, as well as upskill the employees, do the education, just do all of the strategy. It's where the rubber meets the road from a lot of the conversations that we have, which are in the art of the possible, in the examining very specific use cases, and then we do want to work with our partners, make sure that they have access to the latest technology and thinking from our researchers, our architects, so that they can bring that out. It's really the key to having this be available and have people go from zero to one and just start to get moving. Jonny, what about you? How do partnerships fit into how you think about running your business? Yeah. So they're key. I would say probably the most important factor is the people. Working with people that you like makes a big difference. I like the Endava guys. You haven't met everybody yet. That's true. But it does. And teams work better together when they're all on the same page. And we've all met a lot of smart people who aren't great. But having good people together makes a big difference. So I think that's key. And I think aside from that benefit, the ability to spin up teams to solve very specific problems very quickly is what you get from having a partnership like this. I don't have resource to hire five people that are very skilled in certain AI-related things. So in order to get the thing I need very quickly, I can partner with someone like Endava. I can build the application, and we can get the production very quickly. So that's a huge benefit. Cool. Jim, what makes a good technology partner? If I could back up before I answer that question, for the banking industry, the need for partners is all about the war for talent. A very small percentage of a bank's talent are engineers, and our businesses want technology to advance as quickly as compared to a technology company. There's no expectation difference, but at a bank, maybe 20% of the people in a bank are technologists, and of that 20%, maybe 2% of them are actually engineers or people that actually commit code. So we don't have the same number of people that have the bandwidth or the experience or the education to take advantage of it, so we need partners. If that's what answering your question specifically, what makes a good partner? It's someone that can complement, supplement our engineers to allow them to deliver at scale and compete against expectations from the business, which are at the same scale of companies that maybe 60%-70% of them are engineers. We simply don't have that dynamic. So we need partners to help us bridge that gap. Awesome. John, any perspective on partnerships and what they mean to Endava? Yeah. I mean, Endava, as you all know, at the heart of our culture is a focus on people. And it's the people we work with on client side as well as the people in Endava. And I love hearing about the stories of a team has gone in to see a client. There's been a bit of a brainstorm. I think we had one with you a little while back, Jonny. And out of it has come something deeply transformative for that client. One of the frustrations that I have is often those things are so transformative and impactful that you don't want us to talk about them. And certainly that particular brainstorm was one of those. But that's what it's about. It is about us bringing through our people that understanding of what is possible technically with enough of a framework around the client's industry to be able to speak their language and then create those innovative moments where we come up with new things that are going to go forward. And off the back of those, quite often large programs at work because the execution is probably not straightforward, but the impact's big enough to make it worthwhile. Right. So Nicole, if we shift our lens and we look forward a little bit, right? We do a lot of work with Stripe as a partner and are out in the market talking to a lot of customers together. How do you think about Stripe and partnerships and kind of what they mean to how you guys look at the go-to-market, look at the opportunity, look at the space that's out there? Yeah, definitely. I think where we see a lot of value on the go-to-market side is helping customers go live faster, helping them kind of build the case again for their digital transformation journeys, and also having partners act as additional thought leadership and bringing their own expertise as well from different clients that you all work with, helping customers understand what does good look like, helping to provide that strategic insight as well, I think is really helpful. Yeah, those are some of the ways we see it kind of shaping up. I think, yeah, I picked on you earlier because you said audit in one of your answers. Just to circle back to that, I think that's the context that we're often talking to clients though as a reality is it's a, "Hey, we're trying to solve this problem. Who should we be working with?" Right? And so I think part of what we value out of the partnership is just being really close to understanding your product because then we become really good at recommending the right answer to the client, right? And I think that's where we've seen a lot of our SMEs really invest energy to make sure they become experts in your products as well that we've seen work really well. I guess I'll go to you, Jim, first, and then Jonny, maybe go to you. When we look at the emerging technologies that we think are going to have the most impact, what do you see? We've talked ad nauseam at this point about AI, but we'll keep doing it. But what else do you see kind of as emerging technologies you feel like are going to have a big impact on space? Talking about the speed and how fast it's coming with us, I think if we don't, as an industry, get ahead of understanding how we're going to consider quantum computing, we're going to. It's going to be here and we're not going to know what to do with it. It could be as transformational as anything we've seen before, but changing the way we operate. That's something that it's probably for my successors to worry about, but it's coming fast. If you don't get in front of how you're going to take advantage of it, you're going to lose the race because the people that figure out are going to be able to go much faster and much more efficiently to solve business problems. Did Matt Cloke offer to buy you a drink if you said that? No. But the key to it, and this is where I think Endava and GalaxE make a difference. When you're thinking about that, you need to think about it with practitioners, not just the thinkers and the strategists. You need to do it with practitioners that can get into the weeds with you and understand practically how you're going to deploy the technology. But I think that's the one that is coming at us. Cool. Jonny, what about you? What do you see as the kind of emerging technologies that are going to make the most impact? I was trying to think of something other than saying the obvious. I think for me, one of the interesting areas is predictive modeling. And I think for us in racing, we do a lot of predictive analytics during a race for strategy, when to pit, how many tires to put on. So we have a bunch of predictive models running. We've seen over the last couple of years with how fast things are moving in the AI front, the impact that's having there. You take that concept to everything else that we try to predict in business. I think the ability to have better predictions on models of anything I think could be pretty huge. Yeah. David, you can't say AI. I was kidding. That feels unfair. Maybe where do you see AI interacting with other technologies or where do you see it kind of pushing the envelope kind of beyond just the pure AI use case? Yeah, I think right now AI has become like a shorthand for large language models and generative AI. Obviously, there's a bigger space. I think the vision models are super crazy. The fact that you can start to fine-tune a vision model. We had a customer who runs a ride service internationally. They had some issues with recognizing particular street signs. The models, I think, were more used to Western European American types of street signs. Once they were able to fine-tune a vision model to be able to recognize that, their results just went through the roof. I think that was about 2001. I was I was programming a very crude camera that could see left, center, right, and try to navigate something. And the fact that I could pull out my phone and have video and just ask an interactive assistant to describe what it was seeing in real time is fairly mind-blowing. So I think the implications of that will go across the board. The predictions are big. I think healthcare outcomes will be very big. We have some customers who are using these capabilities to accelerate the clinical trials, but also to design and look at discovering new drugs and being able to predict if something is going like, let's predict this run out to the end of it to move faster to save time. And that could be anything from drug design to complex, what was it? CFD. CFD runs when you're limited on time, so I think just unlocking that will make us be able to go faster, so not just about making something sound like a pirate said it, which is fun, but there's a lot more implications beyond just the text that are being worked on. That's my favorite feature for my kids. John, how do we position against this, right? So our kind of ideation to implementation approach, how does that make us best positioned to help with all of that? So the thing is about having leaders in each of those spaces. And obviously spanning industries and having the scale that we have, we're able to have leaders who've had a go at the vision stuff. In fact, we've worked with you on some of the vision stuff who've really made progress as we saw with the Dava.X AI space on some of the use cases and actually be able to come into clients and go, "Well, we've taken some steps forward in a practical sense." And therefore drive those early conversations that come out of us bringing the experience that we have. Core modernization being another one of, "Don't do it the old way. There are new ways that are going to give you much more assured outcomes, enable you to do it faster and more cost-effectively." And as Jim was saying, there's a huge amount of the budget should be going into that. If we can accelerate the effectiveness of the budget that goes into it, it starts to address the problem of layering more and more on the underlying issue because you're trying to develop new capabilities. If you can understand the core, you can carve that bit off perhaps and do a bit of renovation while you're creating the new product. All of these are opportunities that come out of the acceleration that you can get from the sorts of tools that we're talking about. That's what I was touching on in the sense of the enlarged TAM that is available, the addressable market that is available in this next digital wave. So as we close this out, rather than ask you a question about, "Well, what does the future hold?" and some predictive thing, I want to make it practical. And I want to ask you, what is the advice you would give someone who's starting on this journey, right? Whether that's how they move past these barriers we talked about or it's where to start or where to look or just how to frame it. So Nicole, I'll start with you, but just what's the one thing you'd like to share with someone who's kind of trying to figure this out? I think change can definitely be intimidating, but the way I think about it is how do we empower people to gain familiarity and take baby steps. So in your everyday life, I think payments now shows up invisibly on the back end of our Uber app. And so AI is starting to kind of show up in that way. So how can employees of a company start to use that in their own day-to-day to enhance productivity, kind of automate routine tasks that can focus on higher impact initiatives? Awesome. Jonny? Find good people to work with is number one. And I think when we're building tools and the technology that we build, especially the AI integration, using it to enhance human expertise and not replace it. All right. Jim? I think it's key to understand how you can use your talent and make them more efficient using the tools. You can't replace them, but if you can solve the challenge of taking a percentage of technology professionals and make them more efficient by bringing them closer to the technology and enable them to use the technology for transformation, the banking industry by far has too many people that work in technology that are watching or governing or providing oversight and not actually delivering technology. And the banks that figure that out are going to be the winners. And partnering with people that can help us do that is going to be a key. Awesome. David? Yeah, I think the first part is what Jim was saying before: you need to start planning now for an eventual state, and that means just starting to use and integrate some of these technologies, experiment, see where it works. Don't start with the big bang, and then with that permission comes from tapping your talent. There's a lot of things that this democratizes. I know that's like an overused term, but at one of our financial services customers in a wealth management space, the individual on the team who can use this technology the best was not the lifer wealth manager who's been there forever, can cite the figures in and out. It's a 27-year-old liberal arts major, and she's really good at breaking ideas down into language and being able to then prompt the models to ask them what to get. And so there's a lot of surprising places that you can have this utilized talent that might not be able to code directly or interface with all this tech, but they understand those ideas. And I think those brain pathways, you don't get that without using this technology and understanding how to interact with it in your day-to-day life. Awesome. John, any final thoughts on that? I'm going to keep it simple. Get Endava involved. I'm sure I'm going to come back later to underline why, but yeah. Awesome. Awesome. Well, that's actually it. So you can't come back later. You'll have to convince people in the conversation after. I want to thank you all. Obviously, partners and customers are a big part of why we're here and how we think about being successful. So really appreciate you all being willing to come up here and talk a little bit. From a programming standpoint, we're going to shift to a Q&A when we're going to bring our executive team back up on stage to answer some questions that you have, and we're going to feed in ones from the group online as well, but if you give me a round of applause to our panelists. Thank you. You guys are good to. I'm going to stay here. All right. John has to stay. Thank you. Do you need anything to help? I'm sure we'll talk soon. Yes, absolutely. You can pick. You can sit next to David. You can sit next to John. I can next to John. It's going to be my question. I have not. I'm going to be for you. All right. So we're going to take some questions from the room first. And then Laurence is going to feed us some from online. Again, as a bit of a programming note, we're going to do questions for about 20 minutes or so. And then we do have a lunch afterwards where, of course, the team will be around and you can ask any questions. If you ask a question in the room, just raise your hand. Please state your name and where you're from so we have a good idea of that. And we'll go ahead and fire away. So let's start right here. You had the bravery to sit in the front row, so you can go first. We got a microphone. Do we need a microphone? Yeah. Here's the timer. Jonathan Lee from Guggenheim Securities and I have been expert on this. Tremendous to see the impact of GalaxE.Solutions on your capability expansion. I want to dig into some of the accelerators that are being used, right? So how should we think about the competitive moat around presumably being one of the few firms with the accelerators in the context of core modernization? And we want to better understand what's stopping some of your competitors, both large and small, from maybe replicating some of these as you compete around the TAM here. Do you want to talk about that, Matt? I mean, what was great about our initial meetings with the GalaxE team was the passion and energy that they had around their particular set of accelerators. Everything from GX, Maps, Dash, etc., etc. What we saw was also that we had equivalent passion around some of our accelerators. It was really the journey that we've been on since we've merged, looking at those and going, "Is this something that is easily replicable by our partners? Or is this something that actually would take quite a lot of effort for people to do?" When we dug into the accelerators within the GalaxE set, clearly there are patterns. There are various things within there, which mean that there are a unique set of capabilities. We're not going to stand still now. So whether it's coming across a new technology or, sorry, a legacy language which needs to be brought into the tool, there's a pathway that allows that to happen. So we can go further. Someone will come up with the next esoteric language that's required within that core modernization journey and will support it. I think the other idea that we're a nimble organization, so we can respond very, very quickly when we can see that there's something that we can take opportunity from. And I think that larger organizations will struggle to do that when they look at the things that we can do. So Compass is a great example of that. It wasn't just about how can we use those accelerators which are unique to us, but now let's go and apply something very new like Morpheus on top of it, and that will then boost us even further, so we're not going to stand still, and it is something that will differentiate us in our interactions. I think the one thing that I add to that is these accelerators, Maps, Dash, Chronos, and so on have been years in the making. These are not two or three years. These are 10 years plus of investment and refinement and improvement that has gone into it. So that's quite a learning curve for other organizations to go down. Hi. Jason Wolf from Ecolastic Management. Sort of a theoretical question or going to the contracts and how you do business is, on one hand, AI arguably relieves some of the capacity constraints for software developers and moves the importance of expertise and IP more important than actually capacity. On the other hand, we're seeing some of the BPO vendors actually doing some more AI-type work. And you're talking about core modernization, which is likely larger, longer-term contracts. So my question is, how do you see this transition impacting the nature of your work, the duration of your contracts, how you price them, what you're going to think about more outcome-based pricing, and just how that impacts the business over the long term? Yeah. So today, we're still doing it on a time and materials-type basis. So collaborating with clients around the possibilities through the technologies we've been talking about, AI. Actually, some of the core modernization stuff is in the fixed price space. Over time, I would expect that to shift to being more outcome-based in the way in which we contract for a couple of reasons. One is that we will get more confident in the productivity gains, in the impact that we can have through these technologies, whether it's because we're taking something we've done before and applying it into a new situation. So we'll have more confidence in taking on the risk. And the second thing is that for our clients, they're always keen where we can contract and where we can be clear about what we're going to execute on to move to an outcome-based solution. And actually, I think over time, it will be good for our margins to move towards doing that because it'll put things within our control. I know that's something, Julian, that in talking to customers, you're always seeing demand for in the market. Yeah, absolutely. And A, we are doing some outcome-based stuff right now. And I think particularly in the industries that we're really mature in, for example, payments. So it's starting to happen, but we also have a team that are focused on working with the market and our customer base to look at what these commercial models look like and what works for us, but clearly, as importantly, what works for the customer as well. Because there's always that wrinkle, which is sort of the procurement organization where the customer wants outcomes, we want outcomes, and it's how we take procurement on the journey, who have just been used to buying widgets to outcomes. And that's some of the stuff that we're working on at the moment. But we're starting to see the demand grow. We've got models and approaches to deliver it, and we're actually doing some of it now. So we're learning as we go. But to John's point, I see that as the future of where our business is going. And I definitely see it as an opportunity to maximize our margins as well through doing that. I'm the professional pointer. I just point at people, but. You do it so well. Hey, thanks for doing this. It's Puneet from JPMorgan. I have a related question to the earlier one. So what does all this increasing adoption of AI mean for client budgets? Specifically, does all this spending on core modernization data projects, AI models, does it represent incremental spending, or is it just spending being cut somewhere else and reallocated into AI? And then second, if you use more tools like the Morpheus and can generate 70%-80% savings, what happens to that saving? Will clients reinvest that saving in doing more projects, or does that result in net reduction in budgets? Yeah. I mean, the answer to the overall budget question ultimately will be driven by the level of business benefits that clients are going to get hands-on. But I think if you adopt a baseline conservative, if you like, scenario, that it's not going to change the size of budgets. What it's going to do is distribute the way in which people spend. So as Jim was touching on earlier, there is an imbalance in, say, a financial institution because they ought to be spending more on their core modernization. But the business needs and the new things that the business wants means that they're actually spending more money proportionately than they should on the new stuff. And they're layering it on the old stuff and making their legacy problem even bigger. Now, if the approach is around driving acceleration so that you can actually start to use some of that, let's create a new functional business capability that's got funding and actually use some of that to drive core modernization, you actually start to unlock this trap that many organizations are in and release budget to drive a new product and new capability whilst unblocking the core problems that they have. Now, without spending more money, that just means that you're going to shift the spend into the digital transformation space because it's that digital product mindset that's going to drive the change in the core as opposed to where the money gets spent today, which is just keeping it going and doing regulatory and those sorts of things. So we see that as being something that would we increase the TAM because we start going into the core. But actually, there is a reason why that budget will come our way rather than to the traditional places, which is all about the technology and the capabilities that we've been taking you through, which goes back to the moat question earlier. We're also seeing as well our pipeline increasing around us and Endava replacing traditional incumbent vendors where they just don't believe they have the chops to deliver what they can see coming. So a lot of the stuff we're doing now is we're seeing how do we come in, how do we displace a current vendor that's been around many, many years, start to understand their environment, start to understand their systems to look into the future of how we start to do this for them. So to John's point, maybe the budgets aren't changing, but our share of wallet is increasing because of the capabilities that we're bringing to market. Just. We see it as increasing in the future. Clearly, right now, it isn't because that transition is still something we're going through. Hey, folks. Thank you so much again for today. This is Brendan on Puneet's team at JPMorgan. I think something some folks in the market may be concerned about as it relates to AI and impact on tech services is that basically AI will get so good at programming, building software that companies will be able to attack their whole backlog, and effectively, there won't be any more work to do. So it obviously seems ridiculous for folks who are closer to it. And I think that you'll probably have you all raise your eyebrows on stage. But what would you say to an investor that's concerned about essentially clients running out of work to do? I think I'd say, "Wow." I mean, if you just take a step back and look at the billions that are being poured into legacy systems that are stuck in there that needs to be transformed, everyone acknowledges that they need to do it, that will become onto the backlog. And the sort of productivity we gained that we're talking about, these are not new to our industry. The open source, that's probably driven higher levels of productivity than we're talking about in 70% or 80%. I don't know if you want to expand on that. Yeah. I mean, yes, we all raised our eyebrows because I think it's a question that has come up many, many times since our friends at OpenAI launched ChatGPT. It's like, "Well, this is the end of software engineering, the end of that skill set." We have been here before. I mean, technology moves very, very quickly, and people tend to forget the game that was given by a particular technology, and then you move on to the next thing. So open source technology, no one would comprehend writing software now without having some element of open source software and reuse within it. Cloud infrastructure is another great example where there's a kind of like daft stupid moment. Why would you not use something that's available to you? The idea that work runs out is one that causes eyebrows to raise because having worked inside large financial organizations, there is just an endless amount of work that needs to get done. It will never get finished. It's going to be like painting the bridge and going back to the beginning and starting all over again. Good question, though. Yeah. There's a gentleman back there. You're doing very well with the pointer. Yeah. Great. Thanks again for doing this. This is Spencer Anson with Susquehanna. I wonder if you could just pick a few industries and rank order them by which is most and least structurally prepared to implement AI into their core? Do you want to throw one in? Julian? I think mine probably comes from the shock and awe of having worked inside a large financial organization for eight years of my career. Financial services, there's very much a mindset around change as a project that happens. So whether it's a regulatory change or a new request for a particular capability, and the project starts and the project finishes, and the CFO will stand at the top and go, "Well, that's done, and I'm not going to spend any money on it." So what that means is there are many, many systems that exist inside of banks, which no one has ever changed because the project has finished. There's no requirement for change on it, and now AI is kind of shining a torch at all of these things in the back and going, "Well, these things have to change." Someone has to do some work against these activities. And I think the combination of regulation, combination of the technologies involved, the mindsets of the organization, I would say they're probably going to find the change the hardest. It doesn't mean that it's impossible, but it's probably just the mindset that's going to have to change. One that I would reference is energy. So I was speaking to somebody recently about energy markets and whether it's consumption or production or distribution. The amount of consolidation that's happened in that market has led to complex underlying systems, data all over the place. It's not necessarily are they ready for it, but is there an absolute need for it? Yes. And I think this idea of partnership is really important. There's not necessarily one solution that can fix that, right? It requires a partnership mindset from various organizations, different types of capabilities because of the scale of the challenge is so large. And so I think there's a huge opportunity in energy for companies that can provide the solutions around core modernization as well. Scott, do you want to throw one? Yeah. I think large, complex, regulated industries are primed for, I mean, you hit on financial services. It's the same thing with healthcare, right? There's just all these layers of things that have not been updated or modernized because of regulation, right? And I think the imperative to do so has become so strong that you finally start to see some of that breakdown where it's like, "Well, we have to figure out a way to do it." So it's kind of like from a size of the problem to solve, regulated industries with lots of complexity, healthcare, financial services, insurance, etc., are the top targets for me. Sorry. Actually, this gentleman here has been raising his hand a couple of times. Hi, team. Thanks for doing this. This is Jesse Wilson at William Blair. So I had a question for David. How has the way you deliver services changed since going public? And are there any changes that you think are permanent versus temporary? Thank you. Thank you. So since going public, so over the last six years, I think there has been an evolution, certainly for the way in which Endava delivers. First of all, we've expanded, as John spoke about, beyond Europe into the Americas as well. So bringing nearshore delivery through Latin America for our U.S.-based clients has been a fundamental change and additive to our operating model. But extending that nearshore delivery capability has been the center of it. The thing that clearly has shifted us now significantly is our global delivery model where we can bring people from India, Vietnam, together with the heritage of capability we have in places like Romania to build solutions that we couldn't have delivered even in 2016, 2018 when we listed. So I think those things are certainly a permanent shift. We are now able to deliver for customers, whether they've got captive delivery in India, and we can augment that with our teams out there, whether it's a global client that needs presence in all of the geographies in which we are in. So I think those things have evolved. I I think from a technology standpoint and the kind of capabilities that we really focused on over that intervening period, there's been a huge shift in cloud, as we've spoken about, and that capability has certainly fundamentally changed what we do and how we do it. Partnerships with the likes of Google and across other providers as well has really shifted what we can do for our customers there too. We're technically out of time, but since I called on you before, I'm going to give you your question. And I'll be done with squeezing too if I can. Zack Ajzenman from TD Cowen, thanks for the time. Just one more on the potential GenAI threat to the industry, and not so much running out of work, but more so along the lines of customers taking more work in-house. We heard from panelists alluding to GitHub Copilot and what that's done to their own team. So just kind of curious to hear your reaction to that. And then quickly, as you move more to the core modernization, anything to call out in terms of the evolving competitor set? Is that looking any different today than it has been in recent years? Thanks. Do you want to speak to the in-house question? And, And, Julian, that. Yeah. I mean, there is great engineering talent that exists inside organizations. So banks historically have great people who work for them. I think they will realize a productivity gain that is inevitable, but there is a requirement always to bring in a level of expertise and broader knowledge into solving the problems that you have. There will always be requirements for burst capacity because actually hiring smart people is very difficult to do. Therefore, many large and regulated environments find it very difficult to hire the talent that they want because, "Well, actually, I'll go and work for a more nimble organization, or I'll go and work for a Google, or I'll go and work for an OpenAI." They will definitely benefit from having the likes of the generative AI tools available to them, but there is still very much a place for us to participate in that process. The last thing I would say is one of the things that, for me and my journey working with it in Endava over the last 10 years, is the ability to kind of bring knowledge that's been gained elsewhere and know-how and say, "How do you relate it within that industry?" We talked about payments earlier. Payments are always a key vertical for us, but actually, if you see what's happened over the last 10 years, we now talk about payments in automotive. We talk about payments in insurance. We talk about payments in all of these different things. So that's very hard for a traditional organization to replicate that kind of horizontal capability. With regards to the competitor landscape, with regards to core modernization, I think there's a lot of smaller companies popping up that are doing some really cool stuff with AI. However, they don't have the scale or the track record to get into the core modernization piece where there's risk, and that's why we're investing in these approaches and accelerators to actually de-risk these programs. So I think the new folks coming through, yes, they're doing some really cool stuff. They don't have the background or the depth or the scale to actually take on these things. I think some of our traditional competitors are starting to talk about it, but I'm not really seeing a great deal of traction there. And I think a lot of them, and the reason we've won in this space for so many years, have spent so long building an organization that is about delivering bodies at scale and not delivering solutions. I think we're still in the same space. This is about delivering solutions, and we're set up to do that. We always have been. We have that industry. We have that engineering expertise, and that combination, I think, is absolutely vital to do this stuff. Then you include that with the stuff that Matt, Joe, and team have been developing around the accelerators. I think puts us in a really interesting spot in terms of leading the market. All right. Well, that's going to end this Q&A. Thank you all for participation in that. I believe our last segment is John is going to wrap us up for the day and give some reflection on the day. But if you guys want to exit stage right, that way we all. Thank you. Thanks, team and thank you all for joining us today. I hope you found that useful. My objective was for you to be able to hear how we're seeing the digital landscape shift, understand how that's impacting our business today, but also understand how some of the emerging trends around AI in particular and around core modernization are actually opening up opportunities. We're very aware that it's visible to us at the sharp end, having the conversations with clients, selling those early stages of the deals, doing the proof of concepts, and starting to see clients drawing us into AI implementations and core modernization programs. But the road from that to revenue takes months, quarters even, because of the need to do that proof of concept work, establish the program framework for change, get the contracting done, and then even then to actually ramp the teams that are deployed. So we very much recognize that we're giving you a look over the hill rather than what's going to happen next quarter. But I hope that you've found that useful in actually understanding what we're working on, the conversations that we're having with our clients, and why we believe we have a right to win in these spaces. Also, I hope you enjoyed meeting some of the wider team. You get to spend a lot of time with Mark and I, and less with the wider team. And they are the guys who are doing a lot of the work with clients and a lot of the work around shaping our position in the market. So I hope you found that useful. We will find other opportunities for those guys to come to investor conferences and so on over the year ahead because I think that's useful for you all. So thank you very much again. And I hope we've made the case for why our clients out there should be buying in Endava and why we have a great future. So thanks.