How Agentic AI is reshaping insurers' core technical functions?

But I do think that a gentick eye will result in some of the biggest, widest ranging and most fundamental changes to technical functions that I've seen. You're listening to rethinking insurance, a podcast series from WTW, where we discuss the issues facing P&C, life, healthcare, and composite insurers around the globe, as well as exploring the latest tools, techniques, and innovations that will help you to rethink insurance. Welcome to another episode of Talking Technology. I'm your host, Charlie Smallcheck. AI is moving fast in insurance. Almost every insurer is now somewhere on an AI transformation journey. A generative AI in particular has moved rapidly from theory to practice. Many estimates actually suggest that around two-thirds of insurers are already using generative AI in some form. But when we look at where that adoption is happening today, most use cases are so concentrated in things like customer service and claims and sales, areas where efficiency gains are visible and the risks feel manageable. Everyone in insurance says they're using AI, but far fewer can clearly explain what a gentick AI really means or how it changes who makes the decisions, how those decisions are governed, and where accountability actually sits. So today we're going to go beyond the hype. We're going to focus on how AI is impacting the insurers' technical functions, things like pricing, reserving portfolio management. We'll explore where a gentick AI can play a meaningful role not just in automating tasks, but in changing control cycles and decision making and the way that technical expertise is applied. And why, ultimately, the hardest part of this shift isn't necessarily the technology, but it's the people and the processes and the operating models that need to be in place to leverage it. Today I'm joined by Duncan Anderson, who leads WTW's Insurance Technology Practice. Duncan, thank you very much for joining me today. Before we get into a gentick AI, I think I want to ground perhaps this topic of AI in your journey, because you've probably seen more change in this base than most. Can you talk us a bit through your background, what you've seen over the last 35 years in the PNC industry and how big a disruptor do you really think a gentick AI is? Yeah, sure. Now, my current role actually has been for seven or eight years is to lead our insurance technology business, but I'm not actually a technology professional, I'm not a software engineer, I'm an actry by profession and a large amount of my career has been focused around personal lines, pricing and related analytics. So that's my background. I've been with the organisation in one form, another first, just over 35 years now. So, seeing some changes in that time, I mean, it's hard to believe it, but when I started in the building I'm sitting in right now, we didn't have a PC on each desk, so I did a lot of work by paper, that's how far back my career goes. I've seen quite a few changes over the time, like the introduction of PCs, like the internet, for example, but in words of pricing and personal lines pricing, the introduction of statistical modelling in the 90s, the focus on demand, modelling and optimization techniques in the 2000s, the introduction of machine learning, which is technically form of AI, maybe around the 2010s, a lot of the automation that then followed. So, lots of changes over that time, big changes in distributions from writing to agributators and the like, but I do think that the changes from genitive AI and in particular, agentech AI, will be the biggest changes I've seen in my career for, well, I think the insurance industry, but for the technical functions in particular. So, I think this is a really, really fundamental wide-reaching change that I think probably will be bigger than anything I've seen in my career. I mean, obviously we've had a lot of discussion around about AI in insurance more broadly, and there's a lot of discussion around if it's kind of efficiency and productivity and coding support and the impact on the talent pool. A lot of that conversation focuses kind of on faster development or lower costs or doing the same work with maybe fewer people. But I think today we want to go a level deeper into kind of the technical function in insurance. So talk about pricing and reserving and portfolio management and the mechanisms that I can actually determine performance, capital outcomes or your competitiveness in the market. So maybe the first question, how do you think gen AI and agentech AI will affect the way that ensures actually run their technical functions? I think there's probably two questions there. One about generative, one about agentech and I think there's probably different answers there. But I guess the first thing to say, the technical functions quite a lot of different functions, quite a lot of different disciplines, quite a lot of different control cycles. But I think one overarching observation is that over time, over the last decades, these sort of cycles have existed in one form or another. But what has happened is that the analytics that's undertaken in each of them have got more sophisticated. There's been regulatory change, which has changed and they often increase the burden on what has to be done in each of the steps. There have been technology changes which have enabled the different techniques, different approaches, more automation, but fundamentally over all those years, the people-based structures have pretty much stayed the same. And I think one of the things that will change with agentech AI is that those people-based structures, what people do will maybe change a bit more than has happened in the past. Probably better to sort of illustrate it with some example. So there are lots of technical functions as pricing portfolio management, preserving capital and operational claims. Given my background on personalized pricing, let me start with pricing in portfolio management perhaps. And then maybe just to start with portfolio management as an example. So first thing to say is that AI has been used for years in the form of a predictive modeling and machine learning. So that's been around for a decade or two. So AI has been used in that sense to create predictive models, to predict things. I think what is changing with generative AI is that we're now getting into a richer sort of functionality where generative AI actually suggests things. It recommends things. It makes technical analytics much more accessible to a wider audience. So that's happening now. So just as an example, one of our software offerings we introduced a year or two ago is a portfolio management monitoring tool. It automatically ingests emerging data on a daily weekly basis. It automatically runs a barrage of tests to see if there are segments of the business which are performing differently to that which is expected. So by monitoring model productivity or all looking at underwriting factors to see if there are new segments that are deviating in a way we didn't want to see if we're writing more business and the like. So we produced an automated tool that surfaced these and if you like in a quote traditional way using machine learning techniques. What we've been able to do more recently is put a generative AI layer on that which enables a broader audience essentially to converse with the tool and ask it plain English questions like tell me if I've written more business in particular segments. Tell me if there's a segment where I've written more business and the loss ratio seems to be deteriorating. What are the five segments that I should be most worried about? Which rating factors seem to be being less predictive than I thought given my claims model? So these English language questions can now be asked in an English language response and provide it back which makes it much more accessible but also increases some of the sophistication that is possible because some of these large language models are very very good at spotting multi-dimensional patterns that maybe humans would be less good at doing. So the traditional stuff where we had highlighted where the top five things going wrong might be but generative AI enables you to look at where exposure has changed as well as how profitability is doing and spot connections between the two. So the generative AI adding a degree of sophistication and suggestion to that already and I think generative AI can be used in quite a few places in a portfolio management control cycle. So noting what's happening with the emerging experience it might suggest underwriting actions you might want to take it might suggest changes to the rating structure itself. If you decide to do some generative AI can be helped with the testing of a newly deployed rating algorithm. It's a kind of software engineer. It's quite good at supporting regression testing. Generative AI can be used for data enrichment, unearthing new predictive factors, new rating factors, new underwriting factors that you might want to use. So there's all sorts of areas where generative AI can help out in the portfolio management cycle even on the traditional predictive modeling sort of cycle, the pricing modeling cycle. You can get a generative AI or you can get automated AI traditional stuff running models more automatically and then generative AI, spotting potential suggestions that you might want to make to the core model. So I think lots of areas where generative AI can help. We see of them, to your assistance. But I think it's when you move to agentex systems that the game changes. So it's a gentigai, which is the force multiplier. So that is when all these useful tools that suggest things actually link up, interconnect, and still with an appropriate human in the loop, we'll maybe come to that later, we'll actually act and actually do things around the whole control cycle. So the whole control cycle actually becomes a much more automated thing where subject to appropriate governance and checks, things are actually done. And that has huge consequences for the speed of the control cycles. So certainly the sophistication is very good at spotting things that maybe humans would not. But I think the key thing is it means everything happens much more rapidly. The control cycles are much more rapid. And I think it moves to something much closer to a real time control cycle in all the different things across price, import, finally management, reserving, and everything. That's one material change. And I think I think there's another interesting consequence of this as well, which is that for many, many years before AI was a thing, we've been feeling as a firm that ensure technical functions should probably converge over time. That the need for faster, more granular analytics, greater agility, more automated insight pushed, ensures towards having technical functions, communicate more with the same metrics with the same data. And we see a sort of slow change in that direction as well. I think when you have agentex systems doing this, I think that becomes much, much easier. In part, some of the slowness to have that change, or the combined technical functions, a little bit down into human resistance to change, perhaps. And AI agents may have no such concerns over sharing information and data in that way. So I think that's a potential change as well. So that's a very long answer. But there's quite a lot of changes, I think, we will see from this. I don't know what the time scale is, but likely to be faster than we think, I think. There's a lot to impact there, certainly. I mean, I think I like the bit. We're talking about generative AI, almost changing the paradigm of how practitioners interact with the tool. And generating insight, but importantly, it being able to now suggest future action. And then you use the words, the force multiplier, which is agentex AI, which I think is powerful. And then lastly, I think that alignment, because I know that we've been working on that for a long time, the agentex AI as an accelerator to align the technical functions. You did also happen to mention the kind of the human factor in there as well. And I think sometimes this is where people maybe naturally get uncomfortable, but I think unnecessarily, in a lot of cases, in your opinion, what does this mean? Does AI replace actuarial and pricing expertise? Does it augment it? What's your view on that? I think it replaces it. I think it increases the value of deep expertise and it enhances it. There might be a question about whether you need so many experts. But in terms of expertise in general, I think that will absolutely be necessary. So I think what AI will do with agentex AI will do. It'll take away the drudgery. It'll do the legwork. It will do the handle turning. It will do the easy stuff. I think it won't be doing some of the hardest stuff where judgment is required, or whether ethical judgments to be made, nor can it replace human accountability, I suspect, in a highly regulated industry. So we're working a regulated industry. I think there will be humans accountable for decisions and the actions of an insurance company for some time to come. So I think there will need to be humans in the loop and they will need to make appropriate decisions interrogating the AI systems, understanding what is proposed, being able to explain what is proposed, and essentially approving particular things. So I think it doesn't replace experts. I think it enhances it. It doesn't replace human accountability. But I think that accountability will change in its nature. So expertise gets enhanced, but I think the accountability piece is really important. So what do you mean by how will accountability change? Well, I think ultimately the leaders in an insurance company will remain responsible and accountable for the ultimate outcomes. And I think that means they need to take responsibility and accountability for two things. Firstly, they're going to have to design the agentic system. They're going to have to work out what they delegate, if you like, to the agents, and when they wish to review exception. So there's a design thing that they have to do. And then once designed, they actually have to operate them and then review the exception. So oversight sort of evolves to being a policy-driven thing with an exception-based activity. So leaders need to define the guard rails, thresholds for review, what decisions can be devolved and what can't be. And then they've got to think about what will need to be surfaced so that they're appropriately correct and complete judgment can be made in each case. So I think that's, you know, leaders will design and operate the systems to help them with that, to help with governance. I can see a world in which you actually train up a number of agents that are actually governments agents, so a policeman agent. So sort of design them to be particularly for studious things, looking for issues to question and, you know, you put one set of agents against another almost to check that what's been done is okay. But ultimately that will not replace bottom ionicannitability. So I think that's what will happen there. I think also this, the idea of big lumbering cycles where you look at data and things in a particular set time period, that I think that will change. Whether this will be a fun change, I don't know. I'm reminded a little bit in the 1990s, when we used to get paper memos coming around every morning and every afternoon. And then one day someone put an email, PC on the desk and all of a sudden you can get emails all the time, which was great for a day or two. And then that changed the way we worked a little bit. So I think there may be similar changes in terms of the real time governance that will happen with the with the genetic systems when they're up and running. But I think that's the nature of it. I think we're a regulated industry. I think that will define what has to be surfaced and where the ultimate accountability sits. And I think humans will not be replaced for the ethical side and the ultimate financial accountability for any time soon. I feel. You mentioned that we are heavily regulated industry and I guess in that context, certainly everyone needs to live up to a certain standard and the tools do as well. Off the shelf AI though, I guess my question, is that sufficient or appropriate or do we need something I guess more domain specific and explainable? No, I think it very much has to be domain specific. I think they very much need training with relevant insurance background. I mean, I guess in a number of different directions. So the heart of a lot of what we do in pricing, observing cattle modeling, there's a very specific hard core calculation. Regulators are very unhappy if that changes for no apparent reason. So I think that the heart of a lot of what we do, we will need a deterministic algorithmic core. I guess by the way, I am including machine learning as honorary deterministic in that sense, I think. So I don't think we want non deterministic, you know, LLM is coming up with numbers at the very heart of what we do. But I do think we will have agentex systems running these deterministic things in the middle. So I think one thing from an insurance perspective, we will need a appropriate re-bub us deterministic analytical course with the agentex stuff built around it. But I think the other thing that we will need is that there is a lot of insurance specificity that needs to be built in for these things to be usable, using off-the-shelf stuff, where we have experiences in developing some of our own software, using off-the-shelf stuff just doesn't get the results that you need until you actually give it, some background and some knowledge as to what claims are, what premiums are, what loss ratios, how these things work. You also need to bake in strict governance everywhere and train things as well. All these agents are little bit like eager, keen to please interns and sometimes you get the, answers that look helpful until you scratch a little bit deeper. And they're all I'm so sorry, no, it didn't mean that, yes, you're quite right, it's not that at all. So you need to make sure that there are, they are well trained to be, to be reliable as much as possible and that they run checks on themselves as well. So I think just using off-the-shelf stuff without that appropriate training is very dangerous. And you kind of alluded to there that it's actually a bit of a different skill set, I think, for the people that are running these, you talked about, making sure that the training is right or making sure that what they're doing is baked in or having the kind of the right oversight. So I guess, what does that mean for the insurance professional? What skills become essential in an agent-driven technical function versus maybe how we've been doing it today? No, I think there will be a slightly different skill set needed. I think, insurance expertise absolutely needed still. But on top of that insurance experts will need to be appropriately AI-literate. So I don't need to be deep software engineers creating the, you know, the, you know, gobbins of this. But I think they need to be able to design a genetic systems, to design and interpret and challenge the AI agents. And probably if the prediction that there will be this sort of closer line of technical functions, you know, comes true, then I think there might be a little more need to understand expertise in more than one technical function so that you can converse across the boundaries a little bit more easily. I think some things will remain unchanged, the ability to innovate and to do R&D. I think that will mostly remain a human thing, although we have seen already that some AI tools can be incredibly helpful accelerating some of that innovation at R&D. So you can set, you can set some modeling off with some vague ideas and the way it can be automated and test things out very quickly is quite interesting. And even when we've actually derived some new algorithms and we have some patented algorithms that we have, the actual execution of those in terms of coding them up, some of the coding AI tools are really quite amazing now as to what they can do and that can accelerate things as well. But you know, in general, I think that there will be a need to learn. And I think this touches on, you think you said earlier about, you know, I think the technology is largely there. What we're facing now as the biggest barrier is probably the human change management thing. So people do need to start learning now. And I think the companies that will succeed are those that are on the case we're training already. I mean, I do know one organisation, I don't know what name them, but you know, for the last year, they were very good at getting on top of this for the last year. They mandated 45 minute lunch and learn sessions on AI every day for all staff. So you know, some companies are really making a big, big effort to drive familiarity with these tools. I don't think we're quite there, but we're not far behind. And you know, I think we can just see from the number of RFPs we're getting at the moment about AI transformation programmes. Obviously, clearly, I love insurers are concerned about what it means. But a lot of this is about getting people trained up, changing and thinking in the right way. Let me ask you a question. You may be putting on your other hat, if you will, because I mean, you're a practitioner, but you also run an insurance software company. So you kind of see it from that perspective as well. What do you think this means for insurance software platforms, you know, and I guess how do they need to evolve to support a world where humans and agents are, you know, working together, I guess. But thankfully, I think there is still very much a role for specialist insurance software, which I think is good news. But clearly, we need to change a little bit what we are building, as well as how we're building on, and I'll touch on that one in a second. So, I mean, there's some obvious stuff. There's an increasing expectation that there are sort of supportive, generative AI tools within the traditional sort of products that we have. So a lot of our analytical products involve constructing models, capital model or of cash flow projection model in life insurance or a cash flow scenario testing thing for PNC pricing. People expect AI support in that model building. So we've done some of that. People now increasingly expect tools to translate from one environment to another, so again, from Excel into something. So there's clearly roles that generative AI can play in that. And as well as the interpretation of the thing I was talking about earlier about our software, which furnishes sort of insight as in monitoring and emerging experience. So an interpretive layer around that's a nice use of generative AI. So that's one thing, but that's essentially using a last year's stuff almost. I think the one that is, we're keeping it very much in mind, is how our software and other software will play in an agentex system. I think in the future, people will not use one single provider of everything. They will have an agentex system which is using lots of tools along the way. So our tools will very much play nicely in agentex system. So we're making sure they can be controlled by and can control other agents in an appropriate way. But I think the other thing that we're thinking about is coming back to what's earlier about human accountability and sort of the interface layer. So we think as the insurer target operating models change, there will be more of a need for a sort of different type of interaction. So maybe less emphasis on the manual hand crafting of models and the turning the handle. And more emphasis on the surfacing of insight and recommendations for review. So there will be more of an interrogative sort of accountability layer, if you like, I think. And I think that will change the nature a little bit of what we have to produce. So I think we're pretty busy. We're going to work on a few of those things. So I think the things that we can now dream up that we want to build have increased and we weren't sure if ideas anyway. Thankfully, the one thing I've touched on slightly earlier as well, which is there are some amazing productivity gains we've seen in software engineering itself using some of the AI coding tools. So across the software development lifecycle, I'm not just coding a QA, Ruben product design, you know, UX and everything. There are material up lists we're finding in the use of AI tools. It's not the golden panacea that somebody have you believe, but there's never the list that's very material increases in efficiency we're seeing from that as well. Which sort of mitigates all these, the increased workload from all these new ideas we've got. But there, you know, maybe I'd go so far as to say there will be winners and losers potentially in this space, depending on, you know, how people adopt or how agile they can be. Who do you think, you know, if you think about like the next phase, who might win in this phase? And what's going to, what maybe what's going to separate ensures, you know, from those who truly transform from those who don't? Yeah, it's funny. Actually, I'm reminding of a lunch, I had with a chap who, who, who know who he is. I think he was quoting someone else as well, but one of my favorite quotes with AI is that with AI, you're either at the table or you're on the menu. And I think that, you know, that resonated a lot over the last year or so. Yeah, but what does that really mean? And I think the key thing now is doing stuff. Execution. So I come back to the thing. It's a human change management issue. I think the technology is there. Technology is going to get better and better and better, but that technology is no longer good. The constraint with the technology and the AI tools are there right now. There is so much that can be done. And the barrier is getting organizations to adapt and change. Those keeps of proof of concepts going on all over the place, loads of companies experimenting, but the ones that will win, the ones that execute and actually do stuff. So I think that's the, you know, properly executed change management programs. I think is the key thing. And, you know, how could you tell if someone's getting there? Well, I think, you know, we have this vision that, you know, if there's an analytical team with 50 people today, maybe that's actually done by five people and 50 agents in the future. So, you know, what will the winners look like? Maybe it's the ones that are actually managing get material work done by the agents in practice rather than in a, in a proof of concept thing. As we've, as we've talked through this, I think one thing comes through very clearly. A gentick AI, you know, isn't just another technology layer bolted on to what ensures already do. It's a shift in how decisions are made, how control is exercised and where expertise is applied. You know, we started talking about kind of faster models and better insight, but what we've, you know, really been describing is a move away from kind of slower, potentially committed driven control cycles towards something, you know, much more responsive and potentially a world where agents handle. I think you said the drudgery, but the, you know, the routine activity that, you know, they surface the exceptions and then that we could potentially connect the technical functions in ways that simply weren't possible before. And, you know, you touched on this isn't about replacing insurance expertise. If anything, it elevates or it augments it and the value shifts away from just manually producing outputs and towards having skills where you design systems and you set the guard rails and you potentially challenge the outcomes that you're getting from the agents, but you still own the judgment and the ethics and the accountability and the transparency. And the technology, though moving fast, you know, I think it still comes down to the people and the governance and the operating models. And as you pointed out, I think in my intro, you know, I think what it's not a technology problem to solve anymore. It's more of a leadership and a change problem and that the insures that win, you know, they won't be the ones with the most pilots necessarily or the flashiest demos. They'll be the ones who are willing to, well, to make a start and to redesign how their their technical functions actually run and, you know, and think about the role that humans and agents play in that. So first off, thank you very much for joining me today and the insight. I think maybe as one, I'll let you kind of have the closing question. I mean, obviously you've lived through lots of shifts. You talked about, you know, PCs on desks all the way through to aggregators and machine learning and automation, thinking about a agent AI in that context. If you had to give insurance leaders kind of one piece of advice as they, as they look ahead and, you know, what should they start doing now and FISIS on now to kind of make sure that they're on the right side of it? Well, I think map out what needs to be done and start executing bit by bit. Think about where your expertise lies, I think. significant expertise in the future becomes more valuable than it is today. So I think, you know, agentexistants will sort of multiply up that expertise. A lot of experts today maybe, maybe spend a lot of their day not being expert, but actually doing, you know, more drudgery things and more handle turning things. So I think those that really understand insurance and can sort of provide expert interpretation judgments and sort of strategic decisioning will become more valuable. I think there's an issue to consider around training in the long run as to how we continue to have experts in the future if there's less learning done by doing the drudgery. But I think it's about, you know, playing out a sensibly ambitious program and then bit by bit executing on it and try to get value incrementally rather than waiting for ages for one massive system. But maybe just let me come back to what I said at the beginning. I've seen many, many changes over my career, but I do think that agente guy will result in some of the biggest, widest ranging and most fundamental changes to technical functions that I've seen. Duncan, thank you so much for joining. It's been a pleasure. Thank you. Thank you for joining us for another episode of Talking Technology. We look forward to sharing future episodes with you. Thank you for joining us for this WTW podcast featuring the latest perspectives on the intersection of people, capital and risk. For more information, visit the Insights section of WTWCO.com. This podcast is for general discussion and or information only, is not intended to be relied upon, an action based on or in connection with anything contained herein should not be taken without first obtaining specific advice from a suitably qualified professional.