Name: Time for Change: The Evolving Role of Procurement & Finance in Building an AI-Ready Business
Uploaded: 2026-04-28T10:16:14.647Z
Duration: 49 min 21 s
Description: Time for Change: The Evolving Role of Procurement & Finance in Building an AI-Ready Business

Transcript for "Time for Change: The Evolving Role of Procurement & Finance in Building an AI-Ready Business": Right. Perfect. I think we've hopefully given everyone a chance, to join. So without further ado, we will kick off. So by way of a very quick introduction to myself, it's obviously fantastic to have you all here today. Thank you very much for carving out your time. I'm Pippa Gillibrand. I'm a director at PwC. I joined about two years ago having spent four years at the Financial Conduct Authority, the last couple of years as their chief procurement officer. So I've debated and tackled many of the challenges that we're going to be discussing today, and have seen those in in my role. So I know it's not easy, but it's also a fantastic way to be able to engage that conversation across your peer groups. Prior to that, I spent thirteen years at Deloitte. But at PwC today, I lead our procurement transformation proposition across all sectors. So that includes everything from operating model, managed services, and digital. And then I also lead our procurement advisory business for our financial services clients. Hanno, over to you. Yeah. Great. So thank you very much for including me here. I'm Hanno Dietlefsen. Don't remember the Dietlefsen, just call me Hanno. Hanno from Hannover. That's where I'm located. I'm the typical German guy. Started my career here at Cooper as a presales building up the mid market space. So, basically, I was the guy that pulled in when they wanted to know what we are talking about when we talk about Cooper. So I will be the stand in today for Dennis Bruder who, couldn't make it today. So I hope that I would be a good replacement as well. Thank you. Thank you, Hannah, and Rob. nice to join you both here today, Hannah and Pippa. So I'm Rob McCargo. I'm PwC's technology impact leader. I cofounded our AI practice about ten years ago, and it's fair to say that the AI narrative has moved on a fair bit in that time. So I really try to attempt to demystify this noisy world of AI into a way that business leaders can translate that into real value and, sort of break down some of the hype that we're sort of focused on at the moment with the AI agenda. Brilliant. Thank you both. So our agenda then, so we've got a super, engaging agenda for you. We've got a couple of key presentations to to talk through first and foremost. So Rob's gonna lead the PwC's view on that macro AI landscape as he talked about, and then Hanno will then help to kind of really translate that into a practical solution for procurement and finance technologies and functions. Following the presentations, we'll go into a bit of a panel conversation. So we've got a couple of questions that we will have as starters, but you will see on the right hand side of your, of your Goldcast screen that there is a Q and A panel on the right hand side. So if you've got any specific questions that you want to ask to any of us, then please do drop those in the panel. We will keep an eye on it as we go along and try and address those towards the end. If we don't have time for them at the end, we will obviously make sure that we will circle back to everybody with an update on those on those messages as well. We also have a little bit of, audience participation, please. So we have on the right hand side in a second, you'll see that there's a poll will pop up, and just ask you to really to look at that, to start kind of engaging, and looking through in the thoughts in terms of where you see your, your journey and your maturity today in the conversation, in terms of AI. So. we appreciate this as a journey for everybody. Some are starting out, some are much more mature within that. It's ever evolving and there are so many different facets to this, including, you know, how you engage with your stakeholders. What's the strategy from the broader business. But we would love it if you were able, please, just to share your thoughts. I'm just gonna see how well I can say. Great. So that's working because I can see people are voting, so that's positive. Let's give that a moment more. I think I think it looks pretty much as, as it looks like what we see in in in the market when we talk to prospects or customers. I think that reflects pretty much what we see out there out there in daily life. So really agree. I know I think it's a lot of the things that we're seeing at the moment. Lots of clients will reach out to PwC from our point of view and and ask us how how do they get from that, that position of starting to have a conversation around education and experimenting, and how do they build that into live pilots use cases, and then moving it forwards beyond that. So it's it's no particular society, surprised to see that we aren't necessarily at the bottom with the scaling and integrating or AI first transformations. But it is great to see that people are experimenting and are open to those conversations. And that's very much why we are, of course, here today. So thank you all very much for that. So Rob handing over to you then please to talk through the AI landscape. Thanks, Pippa. Yeah. And I agree with Hanno. I think those, data do actually sort of resonate very closely with what we've seen as well. Let let me just start with a bit of a macro picture. So for the last, twenty nine years, PwC's run our global CEO survey. We launched at Davos at the World Economic Forum. And I think this year in particular is quite interesting because we found that, for the first time, investment in AI, cloud data, etcetera, has become the number one priority of CEOs across the, the the countries we survey. But I think, for me, it's it's not only part of the story. We found that there's a bit of a gap between commitment and actual return. So the survey shows that only about a fifth of organizations say AI has increased revenue in the last twelve months. And in many cases, those gains are really quite modest. Meanwhile, about three quarters report little or no revenue change from AI, and about 60% report little to no cost change. So I think we're in a market where clearly enthusiasm is high judging by numbers joining on the the call today, but value's still really quite lumpy and uneven. I think this is the really key leadership question that's shifting from are we simply investing in AI to are we turning AI into measurable business performance? I think there's also a really clear sense of urgency. I mean, I've got a a a an r and d team in AI. They sort of see the latest AI models dropping on a a daily basis and try to keep pace of this. So we know that there's a real race to stay ahead of this, but there's also kind of sentiment at board level that, CEOs are questioning whether they're transforming fast enough to keep pace with this level of technological driven change. There's also, I think, a particular focus here that's relevant for colleagues on the call today across finance and procurement. Clearly, the colleagues in the call are here focused right at the heart of the key enablers that make up AI success around discipline in investment, process redesign, real levels of trust in data controls, governance, and the essence of value measurement. I think we're also seeing that CEOs are central to funding this shift in terms of reallocating capital to from elsewhere to these transformation projects. But at the same time, it's very clear that there's a small number of organizations that are starting to pull away, a small number of vanguards that are capable of scaling AI across the organization, deploying AI powered products and services that Hano is going to go into the Cooper roadmap on later, I'm beginning to use, of course, the holy grail of Agenctic AI, which is consuming just about every one of our conversations in the last twelve months in particular. So I think at a headline level, the big takeaway for me today is that AI is now strategic, but actual scaled value is still rare. What I'm gonna sort of talk about today as well is, what we've launched quite recently is our, global AI performance study. So this was really trying to set out to answer, with some more than simply anecdotes, the organizations that are seeing that level of measurable return on AI. It doesn't just look at the levels of overall AI use, which is quite a blunt measure, but it looks at AI driven revenues. It looks at AI driven efficiencies, but, critically, those foundations that make those outcomes possible. And what it does is it examines the high performing organizations that do it differently across a number of these management and investment practices to create what we call an AI fitness index. So I'm gonna go into that in a little bit more detail on the next slide. So what we did, first of all, is we surveyed 1,200 organizations globally, and it's really clear across the different sectors. We're gonna make this available through the, the webinar today. And, with these organizations demonstrating AI fitness that are pulling away from the pack, there's a number of things that distinguish them. First of all, they're not simply just spending more. It's really treating AI as that business discipline. And I think this is where CPOs and CFOs in particular start stepping into this breach, tying AI investment to revenue, margin, risk, and strategic priorities, backing the winners, but critically stopping what is not working ruthlessly and scaling those use cases at prove value. In terms of the AI fitness index, we can see here, we can see that at a headline level, only 20% of companies are capturing nearly three quarters of all AI driven value. So we're seeing a really small number seeing the significant share of the return of this. And I think, as you can see here, the headline figure is that these AI fit organizations are achieving almost over a seven times AI driven performance boost versus their peers when you add revenue gains plus cost reductions together. So it does tell us that this is simply not about buying the latest tools. It's about organizational readiness to turn those tools into real results. And, again, to double down on this, this is where colleagues on the call today are central to this mission. This is no longer the preserve of the the CIO and the CTO function. I think, finally, we have to get into what leaders are actually doing differently here. They're not just using AI for efficiency. I mentioned before, it's about growth, but, critically, it's about reinvention. The best performers are much more likely to be using AI to reinvent business models and pursue new value pools. They critically also combine AI with strong foundations. You can see here across data, the modern technology stack, really robust governance, and fundamentally workforce capability. And this is not just technologists, but these are AI enabled finance and procurement professionals within your teams as well. They're absolutely ruthless when it comes to the rigor around measurement of value. They systematically track the business impact of these initiatives rather than assuming value appears over time out of magic. And, critically, they also scale it across the breadth of the enterprise. They don't leave AI trapped in isolated pockets or pilots in single functions. They embed it into workflows and operations as well. So in in summary, just to sort of hand over to, to Hano next, I think three things stand out for me. If you're sort of recommending the sort of the thing main takeaways at a high level strategically, what we should do on this journey is, first of all, build those fit for purpose foundations, that level of modern text back, trust the data governance, the risk management, and the workforce. Otherwise, AI stays quite fragile and hard to scale. Secondly, embedding AI across the enterprise. The biggest returns come on AI is built into these real workflows and decision making. It's not that the site experiment in a distant innovation hub somewhere offshore. And thirdly, aiming for growth and reinvention, not simply the it's the elixir of those productivity gains and the cost based efficiencies. It does matter, of course, but it's about creating new products, new services, and new ways to compete. So So I think for colleagues in the call, it's about moving beyond isolated automation and towards how your functions can create the conditions for enterprise wide AI performance. Simply put, strong foundations, scale adoption, and a clear link to business strategy is what's gonna convert this massive amount of AI ambition into genuine return on investment. So with that in mind, I'll hand back over to Jupe for a moment. Brilliant. Thank you very much, Rob. Really, really helpful, really insightful. And I'm I'm sure people are thinking about questions. And as a reminder, do feel free just to pop them into the right hand side of the panel. And we'll come back with a couple of specific ones for you shortly. But, Hannah, over to you, I'm going to switch screen for you to pull up your materials as well. Yes. Thank you very much. So I'm going to take over control now. I'm going to share my screen. Let's see that I choose the right one. Nothing like a tech testing our tech abilities. so let's let's start. Let's start the presentation. So you should be able to see everything now. Right? Yes. That is great. Good. So yeah. First of all, I mean, I introduced myself already, Hanno from Hannover. Just call me Hanno if you want to connect on LinkedIn. No problem there. You can ask me questions later as well. So what I want to talk to you about today is, really and I want to go one level deeper as as what what Rob said, and and I can totally sign up to that 100%. So, what we see and and this is how we do it, but it it also resonates with with other platforms as well. So first of all, the big picture is actually how to get the AI strategy into the into individual building blocks and how that all links together. Right? So we start with the procurement suite, you know, the design to pay or other or the quote to invoice and and other other competing, platforms like ours have have different sizes. Maybe they go from procure to pay, procure to invoice, procure to order, maybe single, single solutions. Everybody has has their core. You know? It's it's a it's a suite. And for us, it's, I think market leading, in the governor quadrant, for example. And we are able to cover all spend categories and offer a very broad and deep coverage with our procurement processes. And we do have this two sided network, and that's usually true for all the other platforms out there as well. You have buyers and suppliers, and there's all all kinds of transaction in between. And that is where the data is coming coming from. Right? All the data, and that's one of the things Rob just talked about, You need to have reliable, clean data in order to make something up of that. Right? And we have our $9,500,000,000,000 of spent data, which went through the platform over the last twenty years. And we had it in our in our, contracts very, very early on that we are allowed to use that data anonymized, aggregated to do something with it. Right? And that is really the domain expertise where we are building our AI capabilities, for the future on. And over the last quarters, we have heavily invested in our agents. So Navi is our agents. Other platform have have other agents, but it usually works the same way. Right? There's the agent layer on top of the data layer, which is beneath that. Right? And the agent then can perform tasks. Right? So with Navi, we have created the, as we call it, UX of the future. You know, and we are moving away from transition centric focus and from a more, manually entering data to generate an output kind of idea. Right? In a world with with Navi, in our case, with AI agents, we move into into an with what we call intent to outcome model model where, users just give a task or ask a question, and the agent then immediately returns with the result. Right? And Navi then orchestrates all the required, agents. So here are the agents, and Navi has access to that and can orchestrate and then get the results back from from that data. Right? So Navi can orchestrate the agents, and, the agents are deeply embedded in that suite in our case. So it's nothing which just sits on top and then, you know, just picks some of the data out which is available via APIs, for example. It's really embedded and lives in the whole data pool that we have. And now we don't only have just one agent or one one generic agent. We do have a fleet of agents which are capable of handling them different tasks. And we also just in general released our agent studio. Also, nothing nothing just, which we came up with other platforms do have that as well. They have, like, spaces where you can create agents. Think of it as the, gems in Gemini or the GPTs in chat GPT. You can use that and, create your own agents with your natural language. So you don't need developer skills. That is not required. And agents can be created just using your natural language, and that's a game changer. Right? Before that, was always like the the IT guys need to come program something then do reiterations and everything, and then you had, let's say, an automation built in the system. Now you can do that just with your natural language. Think of it as kind of a democratization, let's say, of business processes, really, because everybody who has access to that now can start creating their own agent regardless of their of their background. Right? And that's a that's a really great advantage, and and you can use that advantage in order to get your business ahead. Right? And to complete this picture, you know, we also have the idea of bring your own agent. We acknowledge that and and and most most other companies do as well. I mean, everybody brings an agent now. Right? Every every single point solution, there are general agents as well, and everybody is coming with an agent. So we need to make sure that everything which is in the platform also is accessible via other agents as well. And this is why we created this bring your own agent. And in the next release, in in r forty five, which is coming in May now in our case, and, the one after that, which is in September, we are going to launch the, MCP service so you can really access, data through our service here. And to complete everything, we also have the intake the intake, part. So, also deeply integrated into the suite, and it can be extended across the entire customer landscape. You know that from other competitors as well. So this is nothing nothing special to us. It's just where we see that the market is going, that you have one starting point, and then the agents kick in and perform task on behalf of the of the, of the user. Right? And it doesn't matter where it needs to go. It can be you can jump from here into a sales process. Maybe a sales process is a trigger which launches everything back into our system. Right? So triggers can be everywhere and starting points can be everywhere. And in order to make that happen, we have created something that we call the intake and orchestration. And, it's really built for heterogeneous environments as we see while delivering the seamless end to end, user experience, and that is the key key point here. And, what we what we are going to launch now in May is that the or we call it the era of agentic orchestration. You know? And these agents what we have what what do we have down here? These agents or your own build agents or maybe bring your own agents, all of that can be integrated into this orchestration. Right? So, this enables customer to automate entire end to end processes and relief procurement or any shared service, really, from low value tasks. And as a result, customers can realize larger business cases compared to using individual agents. Right? And this is also, what Rob talked about with the embedded workflows. Right? This is the idea. This is our idea how we can bring bring this to life. And this is where we see the market is going. Not just us. Other companies are doing that as well. But we think this is the right way to go. Right? And as a near term vision, we also plan to offer, these marketplaces where partners or even community members can offer, processes they build in our platform, and others can download that. So it makes it more accessible for everyone. For example, in the industry specific end to end processes. If somebody created that, they can make it available to others, and that is our idea of the Cooper community where everybody helps everybody to become better. Right? And that is our or that was always the idea of Cooper in the beginning since our found founding in 2006. That was always at the core of our idea is to create a community and make sure everybody is collaborating together in order because nobody of us, singular, is as smart as everybody together. Right? And this whole solution is also mobile available, and we are we are already live on on Slack that worked already. And in the next, release in September, we are also going to be able to do everything in Teams so you can access Teams via, or you can access all of this via the Teams interface because we've seen that most companies actually utilize Teams in order to communicate. That's a collaboration tool. Right? And so we need to integrate into that as well. And on top of that or let's let's go one step back. Below that and that is that is what we what we see. Below that is data. Right? So, we compare that why do we succeed in in areas where we see so many pilots are not being pursued, where pilots are failing. And the thing, it all comes down to data. Right? And we have you all have heard the phrase garbage in, garbage out. Right? And with AI, that's the same thing. That's a make and that that that's make or break with an AI. Right? And if the data that you're feeding your AI, if it's wrong, it's just fraudulent, if it's biased, then the outcome will be the same. Right? And if it's incomplete, again, you will have a problem. If it's outdated, you will have a problem. And that is not not good at all for challenges you see in finance, in procurement, in supply chain, in payments. And that's why everybody is trying to build up those data pools in order to make something out of the out of the AI. And, and and the idea is or what you really need is a big foundation because, would you trust your bottom line to an AI and and train on it if the data is untested or if it's thin or or, the one grounded, or if it's if it's not if you can't really rely on the data, how how on earth would you give it to an AI to make all of your own and make it available to all of your business units, if the data is not correct. You know? And and that's that's the main thing which need to be understand. You do need to have an AI with a solid, clean, clear data foundation. Right? And when the dataset is deep, when it's trusted, when it's domain specific, not just general, but domain specific, then AI just doesn't analyze what happened yesterday. It can start to anticipate what you might need tomorrow. And that can be, accelerating sales sales times or cycle times, recovering margin, optimizing spend, optimizing trade routes, whatever it is. Right? But the dataset is the most important part, and we are building our AI on those datasets. Right? And for example, this is how the landscape of agents looked in 2026 in January. This is how it looks in May. So next month, you will see this coming out, and then we have way more available agents. And then by 2026, Dennis and his team will will have lots, quite a lot of agents already launched and ready, to perform on on your behalf, really. And this is how it really how you can structure those, those agents. So first of all, you have those, agents which act. They make work happen. Right? And and that can be, for example, in invoice payment batch creations, purchase order confirmations to be read into the system and act on it. Let's say, an event creation or a sourcing event can be automatically created, which is, for example, this agent, what is this agent doing, or maybe compare what you are doing in a note negotiation after after a sourcing event and you want to award a supplier, then you can jump to the contract negotiation playbook. Right? So benchmark what you are what you are doing and the contracts against what is in your playbook for negotiations. Right? So that really helps. Then we have the assistance. Right? So that help you to wait to make work more easy for your teams. So if I if I don't know how a policy is in our in our company, I can ask the knowledge agent. If I want to get, some information on on, data which is in our system, I can just ask the analytics agent. And the analytics agent will create, a real good, what's it called, in slide and, and a graph for you on your behalf. So no more asking the IT to do something for you, but the agent can do it for you. And you will have the, outcome in just a matter of seconds. Right? And then at last, we have the advice, or web or we can put the agents into an advice more framework. And those agents help you and give you some feedback what you could also do. For example sorry. For example, with the with the cost formula agent. Right? So in a sourcing event, if you create a cost formula, that agent can help you. Just describe what you want to do, and the agent will bring this up for you and make a suggestion. Hey. This is how you could do it. And talking about measurable results. Right? Because because that's also what Rob talked about. Right? You need to be sure to bring measurable measurable results to the table. Sorry about my German. My German mouth is not fast enough for my English English language. So, this is this is what we see this is what we see in our data, and we can prove that. For category strategy, improve category plans accuracy by 25%, reduce transport and inventory cost by 10% in the supply and, supply chain design and planning. In contract management, cut down review time by 50%. This is real value what you can achieve if you leverage AI in in those, sessions. And that's that's all I wanted. That's what I wanted to share with you. Thank you. Pippa, back back to you and the slides. Thank you both very, very much. So appreciate it. Obviously, it's a a whistle stop stop tour really across all of these topics. So there's obviously a lot more to to do on this. If we maybe go back one, because I think what we'll do is we'll jump into a bit more of the, I'm gonna stop sharing for a second. You can see us. We'll go back more into the q and a at the moment. So got got a couple of questions that I wanted to post both of you over the next couple of minutes, if that's all right. And, actually, we've got a chat in the sideline as well, which I'll weave through. So, Hannah, to you first. I kind of you've both actually touched about upon some of those barriers of adoption that we're seeing. And I think we we certainly from an advisory point of view, that's that's almost the first question that our clients ask us is is how do we unblock some of those barriers? How do we move forwards with with the demand and the need to want to adopt, AI going forwards? But what's Cooper doing to kind of support that, those kind of barriers of adoption? So be that workforce skill or risk governance, Alexi mentioned in the sidebar bar around data the data granularity gap. So it'd be really great if you could touch upon a couple of those things. Alright. So great. Thank you for the question. First of all, it's a very valid question. I hear that quite a lot, actually. And, I mean, we we started actually pretty early. So, we addressed governance, for example, by moving beyond a black box model, to a framework what we call ethical AI. And we published, a paper, pretty early on in, I think, 2024. We published the the paper on ethical AI at Cooper, and we make sure, for example, we have the ISO, 42001 certificate for artificial intelligent intelligence management systems. We just got that in February 2026. We comply obviously, with, the EU AI Act or Digital Operational Resilience Act, the DORA Act. We we comply with that, obviously. And we have ethical principles for, our AI models are designed to be explainable, to prioritize data protection and fairness and accountability rather than just relying on manual defined static rules. Second, the risk part. Right? So we are leveraging the $9,500,000,000,000 of spent dataset. Right? And, that puts kind of the risk detection on autopilot. So we we have kind of like a real time monitoring where AI continuously monitors external data sources and come and the community feedback to flag issues with, for example, high risk suppliers. We have prescriptive recommendations where we say, okay. You can get better at this if you do x y zed. Right? And then we have automated controls where the system can automatically block, for example, an invoice submission, that might have missing information or fraudulent information on it. Right? And then we do have, obviously, the the the workforce skill and and upskilling motion. Right? You also need to always need to take that into consideration. And, we have the let's say, there are several roles in that. We have the technical adviser, the digital assistant, coworker, and autonomous execution. And we always try to make sure that everything is as easy as possible and also as easy to understand as possible. And we always make sure to have a human in the loop. Is that answering your question so far, Pippa? Yeah, no, that's good. And I agree, right? I mean, that human in the loop, I think is something that a is obviously so critical, but I also think it's quite a lot of comfort to clients as well. This is not just a robot taking over your data or taking over your processes. This is something that is still very much governed and controlled by by that HIL. Great. Thank you. So, Rob, we're going to get taking on from that. And I guess you and I will both know that a lot of the conversations that we have sometimes with our clients end up having quite an industry lens on it as well. What kind of tips can you give for our clients around how they commence those journeys? And I think just before you answer, it's probably quite a good one to flag that for those on the call, like PwC are also a Cooper client. So we're also on our journey with Cooper and how we leverage and best utilize some of that AI functionality. So this is something that we equally live and breathe in our own operational practices as well. Yes. We do indeed. Yes. A very avid Cooper customer of self, of course. It makes my life easier. I'm not paid to say that. I think the, top tips I'm seeing is I mean, I think I I mentioned briefly in the in the opening keynote, Pepper, about the fact that it's very, very clear now this has moved way beyond the CIO function. And, there's a lot of lamenting at the moment, isn't there, about the deleterious and negative impact upon jobs? And the more I see the, the application of these tools to use cases of material consequence in particular in highly regulated industries, the more I see an extra demand for human insight and expertise in particular with that level of domain insights that we see in particular these two functions here that hold the whip hand on many of these investment decisions. So I'm I'm quite optimistic actually in many respects about this. And therefore, what it means is that the colleagues on the call today, they they have to be now at the front of the pack around setting the strategy in place clearly. And if this it it has to be more than performative in terms of demonstrating that you're physically getting your hands on the tools yourselves. You're not outsourcing this to a bright youngster in the department. So I think tone from the top is another feature that we're seeing, sets the temperature really well and creates that sort of fertile adoption culture within enterprise as well. So, and I think with that in mind, in many ways, you're future proofing the the the people in your function to be that AI enabled professional. I I would also, of course, reiterate Hannah's point around trust and the responsible AI adoption side of things. I was one of the, originators of our PwC framework about nine years ago around this agenda. And I think there's a lot that of this sort of counter narrative around trust and around governance that I don't think is actually true. A lot of people think this is a break or a hindrance to innovation, and I constantly see this the other way around that I think if harnessed right, it becomes a a key selling point to your customers. It becomes a a key lever to pull around differentiating your product against the thousands of other agentic AI solutions in the market, and it builds that level of trust internally within your workforce to adopt these tools as well. So I I do think there's a kind of a a twin track here of the tone from the top from leadership to physically adopt this, not wait for IT to do it, and also putting trust right at the heart of this, so that, you can win in the market off the back of it, not just feeling like this is a break upon your innovation ambitions. Yeah. No. Thank you, Robin. I think we're, again, similarly, having lots of conversations specifically with our procurement clients around, you know, identifying those three to five really personal use cases. So making it real and investing the time and effort where you're going to see the the biggest impact. So not trying to boil the ocean. Let's take piecemeal by piecemeal and and build that from there. That's really helpful. Thank you. And I guess, kind of following on from that then, Hannah, over to you. So, this is ever evolving. Right? I mean, if you look at it on a day to day basis or a week to week basis, that there's a new development or there's a a kind of a new competitor that's coming in or a current competitor that's coming in with another angle of of AI and text to further harness their offering. How do, how does that get played, or how do we manage it on a day to day basis? And how do organizations move from that fear into actions? How do we help people become more confident to take that leap of faith into this this world that seems quite unknown, but also fast paced ever moving? Yeah. Okay. So so fear fear interaction. I mean, this is not only true for for, for companies, but also in in in personal life. Right? I mean, I think to overcome that, or and and and to and to introduce a new technology, but always the case. Right? I I started my career back in the days when we talked about the invoice, you know, invoice processing. And it was always, hey, in ten years, there will be no no paper, you know, the paperless office, you know. That was a promise I was made in in, like, twenty years ago. Right? And still, you see paper in office. So and that's kind of the thing you can see that with all technologies and everything's changing so fast, sometimes you lose sight and then you start and and fear kicks in and and you you stick to the old ways. Right? This is this is just human. This is natural, but you need to overcome this. And one of the things you can do is identify core needs, you know, just pinpoint to what, matters most to the business or to the goals versus this is a right now project, and then you need to project that into the future. So what is your long term strategic goal? What you want to achieve with that. Right? And that makes it easier to start, you know. Small steps in the beginning. What is my pain right now? And then project it into the future going going further. And then always, I mean, always verify if you if you, let's say, choose choose a platform, choose a technology, always verify what do those guys put in research and development. You know? As you said, everything is changing pretty fast at the moment. And funny enough, there are old old movies. I just watched an old movie. I think it's from the sixties in Germany. And there's and and then there's a headmaster of his school, and he says, in this ever evolving times of things are moving so fast, you know, and that's and that's sixty years ago. All people thought, like, things are moving faster and faster and faster, and that accelerated over the time. Right? And so make sure that the company you are choosing to be on your side as a partner going through these changes, invest in research and development and also in governance. You know? That's that's key because only commit they need to demonstrate a proven track record in research and development and security. That is, I think, an an ethical governance. Right? I mean, Rob also touched on that. I think that's that's becoming more and more, a key area that you need to check the ethical part of everything. Right? I mean, this this is this is key to also get get your workforce moving. Because if they feel like, okay. It's it's unethical. I I don't wanna work at the company which is unethical, which is doing unethical things, which is, like, loose playing loose and, loose with with my data because I'm an employee there. Right? So I don't want that. So make sure ethical. And and third, upskill your teams. Obviously, transitioning is is always work. You know? Transitioning from one state to another is always work. And, for example, companies like PwC can help with that because they have gone through those motions with other companies hundreds and hundreds of times. You have done that already. And not only with AI, it was with, you know, digital office. And before that, with this new thing called computers. Right? You know, where where four kilobyte is enough for everybody. We all know these stories. Right? But it was all always transformation. And make sure you have somebody on your side you can really trust who has done that a couple of times and try to upscale your teams. Brilliant. Thank you. And I guess just one very last question, and I'll touch upon in a second, a couple of the other bits that are in the sidebar as well. But, Rob, I mean, we and I guess also Hannah, but we we look at it all the time around, you know, these are this is the art of the possible. This is what you can do. What are the measures of success? Like, how can we truly turn around and say, actually, with this, this has been operationally efficient. This has been a good thing for us to do. What what do we see as our kind of core ROI metrics? Yeah. Well, just before I get to that as well, Pippa, it it what Hannah is saying is really interesting for me as well because we're gonna have to get used to the level of uncertainty we've never seen before. And I know there's a bit cliche there about we've never been, you know, facing as much change as we are today. It's true. But, you know, I have to be humble here. There's people like me five years ago saying everyone should retrain to be programmers, computer scientists. We now know that generative AI can do about 85% of the coding. We've got Vibe coding now, extraordinary changes. We did a forecast three and a half years ago, someone did, about how there's gonna be this acceleration and the growth of prompt engineering jobs, and there's been a report by LinkedIn saying that never came to pass. So we just don't know. And I think that level of uncertainty now is going to be the new normal, and, therefore, therefore, that level of comfort with uncertainty has to be something that you as leaders will have to just accept and have the adaptability and the teams in place ready to go. So I I think in terms of value, I think just to reiterate my point, this can't be simply about, first of all, measuring the number of people that have used AI. We we've tried that. It's it's not a great measure. There's been a lot of press releases to say that we've we employ 50,000 agents. Most of those must be awful agents. So it's not about number of agents, number of people dialing in on AI, but there are clearly ways of moving the dial from simply just looking at efficiencies and productivity, which, which, of course, is going to be something that people will need to prove a business case. Clearly, growth can be measured, but there's gonna have to be a way in your organization where that reinvention opportunity is captured. The. value from that business model change is captured in your own way, in the industry specific way. I think there's something in this around industry convergence, the way you're working with counterparts in different sectors that come into this equation too. So I think there's not one specific playbook for showing value, but I would urge people to move beyond the very, very low horizon of simply iterative improvements in making your current horse run a little bit faster. Yeah. Agree. And I think just probably to add to that as well, I think a lot of the conversations that we end up having is is based on how you link that ROI back to your original business case. You talked earlier about that senior stakeholder engagement, the business strategy, so on and so forth. Making sure that you then have that those success metrics linked back to what you tangibly said you would deliver in your business cases is really important, both for ensuring that engagement and buy in and then further support for whatever is your next steps along that journey. Perfect. Okay. So I guess there are just a couple of couple of things I just wanted to touch upon in the sidebar. So it's a bit of a question around kind of tech solutions to help support with that data improvement. So we'll come back on all of these kind of offline, but I think it's a really important question. It's also a question that we end up talking about quite a lot with our clients. And actually, interestingly, you just can use AI to help you on that journey as well. So almost leveraging it to get you into a better position to then leverage it into your AI solution. And And so I think that's quite an interesting one as well. For the Cooper team, there is definitely one here around the Cooper Compass and the Cooper community. So I think that's one we'll take offline and make sure that that's kind of responded back to as well. And then a bit of a query as well around how can AI help to solve tax determination as well. So again, another one that we can take offline. These are all really, really important questions, and they're ones that you alone won't be feeling or won't be raising. So we'll we'll provide the answers, but I'm mindful of time. So we'll get those covered offline and then get those issued back out to to this community as well. So if we can possibly pop the slides back up, please. Amazing. Thank you. So we're gonna ask for your participation again, please, as well. So, obviously, we've talked in generic terms around, you know, what can AI do for you? Where were you on that journey? But what do you think then at the moment is your most significant barrier to achieving your AI goals? And there are, five options down there for you to choose between. Give everyone just a moment. Although, definitely have a front winner at the moment, and interestingly, very much around that data and scalable infrastructure. So I think, again, not something that we were, unsurprised to see. Nice though to see that there is or seemingly, it's not the biggest blocker that there is budget and their sponsorship. Because I think from a personal perspective with clients, sometimes that quite often is the first hurdle that they they need to overcome first and foremost. So it's nice to see that shift from maybe if we'd done this twelve months ago. Brilliant. Thank you. So I think our lead winner there then is, is the data and scalable infrastructure. So thank you for that. So if we close that poll, please. Thank you. Okay. So just kind of coming to the close, I don't want to make sure that everyone has time. I've no doubt everyone's jumping into meetings on the hour, so we won't overrun and we'll give you time back to make sure that you can all make refreshed teas and coffees. But a couple of dates for your diaries and locations, obviously, the Inspire events are just about to kick off over the upcoming weeks. So please do take a note of those. I will personally be in the London one, so I will happily say hello to anyone that happens to be there as well. And then you have a number of them as well across, across other options. So you could even get yourself a a world tour ticket and go and see them all if you fancied it and some time out of the office. But really great events, really engaging, lots of your peers in the room as well. So it's a great way of being able to kind of share kind of war stories and success stories and and how you each navigate through those things. But all the information is on there. It's all online as well. Hannah, I don't know if you wanted to say anything else about the Inspire dates. Oh, just come. It's going it's going to be great, really. I've been at a couple of those already, and it's it's really amazing to see so many, let's say, customers of ours, talking. And and and it's real talk. They also tell you, hey. This this didn't went well, but everybody is more or less, saying the same thing. It was worth it. And if we are at the point to ever do it again, we would have chosen the same thing, to do the same way with with Cooper again. So, come if you want to hear real customers talk about real problems, talk about real solutions they are using, real idea exchange, and also, you know, maybe see one or two familiar faces there and and chat with them as well. So that's that's also possible. Yep. Exactly. They are a great way of engaging across across the market with everyone with shared shared challenges and and objectives and ambitions. Perfect. So otherwise then just a really big thank you then from all of us. Thank you for carving the time out of your busy schedules. I know it's a very busy time of the year normally for lots of people for but it's great to have so many people join us today. Hopefully, you found it insightful, interesting. Maybe you've got some more questions that you have taken from this. Do feel free to reach out to any one of us. We're happy to to catch up offline as well. But it's been fantastic having you all on the call today and for your engagement in both the polls and the questions. So thank you ever so much. And hopefully, we will talk to you all soon, if not see you at an Aspire event at some point. Thank you all. Thanks. Yeah.