Imagine it's the year 2027 and Agentic AI is everywhere. Business areas such as banking, healthcare, supply chain management and even HR are transformed due to Agentic AI's ability to automate tasks, analyze data and make real-time autonomous decisions. But for these AI use cases to exist, scale and flourish, proper insurance measures need to be established.
Janet King, SVP of Content Strategy at Digital Insurance sits down with two industry leaders from Xceedance – Brandon Nuttall, Chief Digital and AI Officer and Gavin Lillywhite, SVP Operating Leader UKI & Europe – to share a future-focused perspective on the insurance products that need to be unlocked for Agentic AI opportunities to truly take hold and transform industries.
In this on-demand Leaders episode, viewers can expect to learn:
- The major role insurance companies play in helping Agentic AI use cases survive, scale and grow
- What's expected on the insurance product roadmap to support an AI ecosystem
- Current examples of Agentic AI opportunities that insurance companies should be capitalizing on
- How to make sure your company is positioned to develop and execute new insurance products
Transcription:
Transcripts are generated using a combination of speech recognition software and human transcribers, and may contain errors. Please check the corresponding audio for the authoritative record.
Janet King (00:09):
Hello everyone, and thank you for joining us. I'm Janet King, senior vice president of Content Strategy at Digital Insurance, and I'll be your host for today's Leaders episode. Let's first imagine it's the year 2027 and agentic AI is everywhere. Business areas such as banking, healthcare, supply chain management, and even HR are transformed due to agentic, AI's ability to automate tasks, analyze data, and make real time autonomous decisions. Now, let's try to imagine the insurance products that will need to be in place to make sure these agentic AI use cases continue to exist, scale and grow. That's what we're here to talk about today, and I'm excited to be joined by two industry experts from Exceedance who bring extensive experience and proven leadership in the insurance industry. To today's discussion, they are Brandon Nuttall, Chief Digital and AI Officer, and Gavin Lillywhite, SVP and Operating Leader for UKI in Europe. Welcome Brandon and Gavin.
Gavin Lillywhite (01:08):
Thanks for having me. Thank you, Janet. Great to be here.
Janet King (01:12):
Well, I'm expecting this to be a lively conversation, so I think we should just jump right in. To start off, we're really flipping the traditional script here to talk about how insurance can enable AI rather than how AI enables insurance. So why do you guys think this is an important distinction? And let's start with you Brandon, and then we'll get your perspective. Gavin?
Brandon Nuttall (01:35):
Yeah, thank you for the question, Janet. I think what's really interesting is that the history of insurance has been about how insurance enables commerce, right? All the way back to the first coffee house at Lloyd's. Lloyd's enabling marine insurance to the fact that modern supply chains couldn't really exist without trade credit insurance. The history has been insurance enabling a marketplace that people can transact on. So really when we think about agentic AI and where we're going in the very, very near future, that will be a marketplace that we will be transacting on. And so realistically, as an industry, as an insurer, we should all be thinking about what kind of products need to exist in the future to enable a agentic AI to thrive and grow.
Janet King (02:22):
Gavin, anything to add to that?
Gavin Lillywhite (02:24):
Yeah, I think it's exactly the way to look at it is at the end of the day, we need to make sure that the efficacy of the product is doing what it should do. And if we think about all innovation, innovation can only happen if it has that safety net of insurance, whether it's prototypal development, even if we think about more modern day examples like offshore wind for example. Now we see that as an integral part of the power supply globally. 20 years ago we didn't have that risk and there was no product to do so, and the only way that we can get confident investment into big infrastructure products like that is by insurance. So insurance is absolutely the lubricant, if you like, of the global economy as Brandon said. So
Janet King (03:15):
That's great, and I love looking to some of those other examples and other industries like offshore wind is maybe providing some sort of clues and roadmaps for how insurers might think about this going forward. So thanks for bringing that up, but do you see this as an opportunity for insurers or more of a challenge to the industry's ability to make it all happen? Who wants to take that?
Gavin Lillywhite (03:42):
I'm happy to start with that. I mean, I think probably a bit of both. In reality, there's always a challenge to new risk because obviously the way insurers typically work is using historical data to design models and capital models for rating and pricing advocacy for the future. So I think the fact when we think about new risk, obviously the challenge is that often there is no data there or limited data, let's say. So that's the challenge. But of course the opportunity is also everything that the new risk brings because if we think about the challenge of market cycles, for example, market cycles really come about because there is too much capacity chasing too little risk essentially. So if we think about how we can actually start to use Agen AI capability and the insurance required around that to actually build new risk pools of opportunity, then for me personally, that's really quite exciting and should theoretically help to reduce the impact of a softening market.
(04:54):
I mean, we've just been in a hard market for the last four years or so. I think it's fair to say we're coming out that hard market now and that market is starting to soften and in some areas it's really soft already, for example, like cyber D and o, those kind of classes. So I think actually leaning into these new classes of business and new opportunities allows insurers to help grow that equitable pool, help diversify their portfolio, which in itself should be more capital efficient as well. So I think combination of both, but I would say the opportunity certainly outweighs challenge.
Brandon Nuttall (05:32):
Yeah, Brandon, I'd add one thing to that. If you zoom out and you strip it all back, tomorrow's trillion dollar company is a startup today, and that startup today is absolutely using ai. So either they are properly insured or they're not. And if you look out across the market and what the marketplace currently offers, I think you can make a credible argument that they're not properly insured. So realistically, this is about how do you enable that next trillion dollar opportunity? How do you enable that ecosystem of organizations that are able to win and grow in a market that actually, just going back to Gavin 30 years ago, we had no idea that offshore wind was going to be as important part of our energy mix as it is today. You can make the same argument about agentic ai. So realistically, if we assume that that's where we're going to something that is that structurally significant, it becomes incredibly important to make sure that we as an industry are enabling that startup ecosystem so that they can thrive.
Janet King (06:35):
Absolutely. I'm sorry, go ahead, Gavin.
Gavin Lillywhite (06:38):
Maybe I could just add to that is that if we think about how insurance products typically evolve rather than revolutionary, they tend to be evolutionary, let's say, and the challenge sometimes is that the risk is already there, it's just that the actual language of the policy wording and the coverage is not necessarily fit for purpose. And I think that's sometimes the challenge the industry has because people maybe just assume or take a chance sometimes that it'll be covered because the language is quite loose, it's not excluded, therefore it's probably included, but it's maybe a bit of a gray area. And I think that's sometimes the challenge. To Brandon's point around these kind of incubator companies and embryonic companies need to have proper insurance, but they will have more of a traditional insurance product I suggest. But the reality is that that might not necessarily be fully aligned to their needs of the future and the kind of loss triggers that might influence a loss pattern for them in the future.
(07:41):
So they might be thinking, actually they're insured because the language doesn't exclude it. And then in the event of a claim, we get to the point where the insurer say, well, that's not what we intended to ensure. Then you get into legal disputes, et cetera, and it's not necessarily fit for purpose. And if we think about the whole purpose of contract certainty, it is to ensure that the policy language is clear enough to be able to determine what is and what is not covered. And all parties are clear on that, but I suspect the reality is that there's probably somewhere in the middle, and I take cyber as an example of that, and particularly cyber physical damage, whereby some policies it's excluded completely, sometimes it's excluded with a buyback, but then when you start to look at some of the buybacks, they exclude the cyber loss, but include the ensuing damage for example. So it starts to get quite complicated when you start to unwind a business interruption loss, for example. So a loss of revenue type loss. So again about fit for purpose and making sure that the industry is on the right foot with the right products going forward rather than just relying on perhaps outdated outmoded coverages that are not necessarily fully fit for purpose.
Janet King (08:57):
I love that idea of fit for purpose. So what elements do you think the policies of the future are going to really need to address or include to make sure they are fit for purpose? I know when we were chatting about this before today's call, you talked about traceability and some other things. Can you just comment on that a little bit more?
Gavin Lillywhite (09:16):
Yeah, I mean, there's several elements is I think one of the big things to think about of course, is that we tend to move away from tangible to intangible assets and valuations, which in itself is a big challenge because typically the insurance market is predicated on asset type based policies, whether that's property buildings type policies or marine physical vessels, for example. Whereas a lot of the value in more modern companies is intangibles. It's intellectual property, for example. But trying to get really significant levels of cover for intellectual property covers is still quite a challenge. Then there's obviously, you talk about traceability, it's the traceability of the actual, if you think about AI code and AI solutions, it's the traceability of the code within that product, for example. So if you think about the common law of tort, it's about who has traceability, who can traceably be determined as the offender or the person that has caused damage injury or damage bodily injury or damage. Well, that's no longer so easy to do because the reality is that when you have AI programs overlaid by other ai et cetera, then the traceability back to original source is so much more difficult because the reality is a combination of, so that presents massive challenges for traditional liability policies, for example.
(10:56):
But these are things that will have to be accepted. And whether we think about that sort of a machine that's using an ai, AI determined program and then injures an employee for example, or whether it's a piece of code that causes damage to someone's network or causes design damage when we think about design insurance as well. So there's all sorts of, the reality is we've got to rather than sort of saying we need a new insurance product, it's about, for me, it's very much around making sure that our existing insurance products are fit for purpose for the new age of ai.
Brandon Nuttall (11:40):
And I think one thing I would add to that, Gavin, that I think is really important to emphasize, the reason why it's important to talk about this is because AI can make such a massively positive social contribution. I think we would all agree that as a society we need more cancer screenings and we need cheaper cancer screenings. Now, if a radiologist is reading a cancer screen and gets it wrong, we have an insurance policy for that that's called medical malpractice. But as we move into a world where nine out of 10 cancer screenings are done, at least initially by ai, really you start moving into a world where it's much more difficult to understand who actually is to blame, who actually has the liability for that loss. But on the other hand, you are delivering a tangible positive social good by doing more accurate cancer screenings. So if the insurance product isn't medical malpractice, it should be something else. But the fact of the matter is that if we do this well, we can unlock 10 times as many cancer screenings, but the same number of humans that we currently have today. And so that's the prize that we're all going toward when we're talking about agent tech ai and we're talking about tangible applications of ai. But the thing is, is that nobody's going to be willing to take that step forward into that future if we don't solve that insurance question first, which is why it's critically important to have products that put names on losses like that so that we can properly insure it. And that's going to be the challenge to us as an industry in the next 18 to 36 months.
Gavin Lillywhite (13:21):
And I might also suggest there's also a sort of a governmental regulator angle here as well, and I take sometimes the example of driverless cars, if the vehicle is driving down the road, and it is this old example about whether it chooses to hit the old lady that's 90 years old or the baby and it chooses the old lady at the end of her life, the reality is neither of those are great results, but the machine would make a logical decision. But if that was a human making that decision, there will be no question asked about it, about why did you make that decision? But because it's a machine that's making the decision, there's a harder societal question about whether that was the right thing to do, even if it made the same decision that a human would've made. So I think there's also about create an issue here around creating a safe environment from a legal perspective that allows also then the insurance market to create products that can actually provide those kind of indemnities, because obviously the premise of insurance is to provide protection for society within the law, within the legal framework.
(14:34):
So obviously you can't insure against any legal act. So I think there's also another angle there that would need to be supported by regulators as well. So that will also help empower those development of those kind of products.
Janet King (14:48):
Do you think those regulatory changes can be made quickly enough to keep this engine moving along? What kind of outlook do you have there?
Gavin Lillywhite (15:00):
I think it's the hearts and minds game, and it is exactly as Brandon said, if you think about the bigger picture, if you want to get to the end destination, and sometimes you have to make some difficult decisions along the way. So I suspect it will be a myriad of participation and development from regulators. But I think if you think about the might of the likes of Lloyds of London, for example, who have a pretty strong position with the regulators in the UK and other regulators around the world, I think if you can sell the story around the bigger benefit, then I think it will come. But unfortunately, regulators tend not to move too quickly, but I think it's one of those things where the industry needs to become a bit of a lobby as well against the regulators to say, look, the risk is here, the societal risk is here, but more importantly, the societal benefit is here or can be here, and that benefit can be accelerated if you give us the framework to actually put these products into market, which will help everybody in society.
Brandon Nuttall (16:09):
Yeah, I think to add to that, there is no global AI regulator or AI regulatory approach. The EU, of course, has taken the stance of basically regulate first ask questions later. The UK has very much taken almost the opposite approach in a lot of ways and has a very, very federated approach to AI regulation in the us. The US is in a pretty scrambled place at the moment, but actually even down to the state level, certain states have quite unique regulatory language. So I think of all the world economies where I think there's a big potential for AI innovation, I think that the UK approach, which is relatively permissive compared to a lot of other parts of the world, it is really set the stage for insurers that operate in London, in the uk, in the London market, Lloyd's market, to really do innovative, interesting things. I think the other thing to look at is to look at what the reinsurers are thinking of as well. Munich Re and Swiss Re, they've created reinsurance programs for reinsuring AI risks. So actually in a lot of ways the reinsurers are ahead of the primary insurers. So really these are risks that can be navigated. And so I guess my point of view here would be primary insurers should be thinking about how to navigate those within the constraints of their own market as well.
Gavin Lillywhite (17:44):
And there's another interesting angle there, exactly as Brandon said, that ultimately new products are born by the support or grown by the support of reinsurers because obviously that gives the primary insurers the confidence. But if you look at what both Munich Swiss Re are doing, traditionally they were pure reinsurance companies. They're now becoming a hybrid of reinsurers and retail insurance companies. So they've both got their own insurance divisions. So there's also probably a bit of a wake up call to other primary insurers to step up rather than the market simply be dominated by those reinsurers that have both the reinsurance capability as well as the primary. But again, in reality, there's room for everybody on this kind of stuff because if we think about the kind of capacity we need to build, going back to my point earlier around mirroring the products and updating the products that we currently have where we see multimillion multi hundred million dollar limits on policy or billion dollar limits on policies, we need as much capacity as we can get.
Janet King (18:52):
Absolutely. Early on, we talked a little bit about mindset. So you guys have foreshadowed some of this, but what mindset shifts do you think need to take place for insurers to help pave the way for broader AI adoption? We've talked a little bit about some of these macroeconomic regulatory and sort of industry frameworks that need to be in place, but what about the mindset itself?
Brandon Nuttall (19:17):
Yeah, so I'll take that question, Janet. I think what's really interesting about that question is that these risks are not theoretical in nature. I think to a large extent, these risks are already here. There's a stat that I love, which is 90% of all gen AI usage inside of enterprise is essentially shadow it. It's people using chat, GPT or Gemini from their phones or from their computers and retyping things. So these are risks that already exist that organizations are already running. So I think the first thing from a mindset shift is to say, these aren't theoretical. These risks are here. And actually, if you create an insurance product that can ensure these risks, people should already be teed up to buy them. So that's one. I think two, and this is important is there's definitely a first mover advantage here. So if a agentic AI and AI being at the core of modern business and modern operations, if this thing branches out the way that we think is going to branch out, realistically first mover advantage should be very, very significant If you're the first to integrate this into an SME product, if you're the first to corner the market on startups, if you're the first to really land the mid-market messaging to get mid corps on board, you really stand as an insurance company to be really the hub of the agentic AI ecosystem. So I think that with that as context, there's a massive opportunity and really it's about how do we as an industry think about building the products to capture it.
Gavin Lillywhite (20:58):
And maybe I would just add to that, I think sometimes the challenge is if we think about how insurance companies are managed, it tends to be sort of at a product level then up to a global or multi-line chief underwriting officer who in turn reports to board of management. When I think about my time in cyber, the reality is that often the layer below the board have a much more technical knowledge of cyber than the board of management does. So if you talk about what mindset changes properly needed is that for those board members that don't truly understand this, they need to really engage and really take those in their teams that do understand it and can help really develop that differentiator rather than simply saying, no, we don't understand it, therefore we're not going to do it, which we have seen in the past is fair to say.
(21:52):
So I think if you want to accelerate it, it's about maximizing the capability you have in the organization. Even if that capability is not the most senior capability, of course you're not going to hand over all your decision making to a junior underwriter just because they get agen ai, but it's about really creating an ecosystem in the organization that allows much more creativity across the spectrum, if you like, rather than just the sort of, it has to go right up the top for a decision type thing. Because candidly, a lot of the time those board members in those very senior positions have not, they won't have grown up with the Gen TKI just as they didn't grow up with cyber, but they still need to make decisions about it. So that's why it's really important to make sure you make full use of your workforce across the level that you've got because obviously they're the ones that are closest to it, they're the ones that truly understand it and really embrace them as true advisors into that development of product.
Brandon Nuttall (22:52):
I'd also say, and I think that we as an industry need to really start shaking off the stereotype that insurance by necessity is somewhat stale and somewhat slow. I don't think that that has to be the case. We have seen that innovation in this market can land quite rapidly and really take root. So just thinking about personalized for a minute, I think we all know how quickly that price comparison websites totally changed the marketplace for personalized insurance. And I'm not saying that agentic AI insurance is going to be like price comparison websites, but the fact of the matter is is that whether it's an internal trigger or an external trigger change can happen very, very rapidly in our marketplace. So again, I think to a certain extent we need to park the assumptions, shake off the cobwebs and think about how we can move quickly to meet the challenge.
Gavin Lillywhite (23:46):
And I guess coupled to that also is then making sure they've got the right kind of people in your organization. So you might need people more data scientists for example, rather than just employing more of the same of what you had five years ago. You need to ask us also start to think about, well, what's kind of people do you need in the organization? But in order to do that, you need to determine what kind of organization do you want to be? Are you going to be a vanguard at this or are you going to be a laggard? Because there's no point employing people to develop these kind of solutions if you don't utilize them. But I think that's a really important point for me because again, to Brandon's point, the insurance industry does have a bit of a reputation of not being the most exciting, but in reality actually, if you find your right spot, there's anything for everyone. And we need more people from different organizations, more tech people as well, not just more of the same insurance. And if we think about how I could think about my own experience, you grew up through the ranks and you are trained in a certain way. And I started in the industry 30 odd years ago. Well, it's very different now to what it was 30 years ago. So if you try and train someone the same way today, candidly not going to be necessarily fully fit for purpose.
Janet King (25:03):
Yeah, absolutely. And I think we talk a lot about hiring and people and insurance, and I would completely agree that I think insurance is a much more interesting place to be than the industry gets credit for. There's a lot happening, there's a lot of data to draw on for ai. There's a lot of interesting stuff happening with sensors and all sorts of new technology that they're using to process claims. I really think it's a very interesting and exciting place to be. And we talk about that a lot at digital insurance. So I hope that message starts to get out because I do think it's an industry that's really poised to drive innovation in this space. So I thank you for bringing up all that talent stuff. So technology is clearly going to be a key underlying thing. So with all of these opportunities and change coming their way, how are you guys at Exceedance helping insurance firms stay ahead and what do you guys have planned for the future?
Brandon Nuttall (25:59):
I'll take that question first, Janet. I think it's really important that we help our clients, our customers cut through a lot of the hype and get to what is working and what is working now. So if you take a look at the extent to which AI is currently embedded in our market, it's clear that there is a lot of opportunity, there's a lot of potential, but there's not a lot of execution. One of the things that I'm focusing a lot of when I talk to my clients is I talk to 'em about actually what insurance is doing. When you take a look at other markets, you take a look at retail, you take a look at energy, you take a look outside of financial services. A lot of things we're talking about in terms of low hanging fruit from using ai, it is not unique.
(26:44):
All these problems have been solved before in other industries. So really what's unique about insurance is simply, I say simply it's about navigating the regulatory complexity, is that making the things that have already been proven elsewhere work for us. And so then it's basically looking at the quick wins, looking at the low hanging fruit, and then linking that to a broader end-to-end strategy that takes us to a place. Going back to what you're talking about, your kind of reduction, Janet, we are in 2027 and actually we are in a place where we can really think about building and embedding proper agent AI with decision-making capabilities. And we will be in that place because we spent 2025 and 2026 not getting caught up in the hot AI model of the day, but investing in core digitization, investing in core data and analytics platforms, investing in understanding the governance and the risk, and building those foundational capabilities to assist our people in the short term so that we can start confidently moving some decision-making capability and authority to the AI as a technology matures.
Gavin Lillywhite (27:55):
I think what I would add there is that from my side, some of the clients that I've been talking to, I always sort of give the example of when I used to be a property underwriter. When you're underwriting really large complex risk managed accounts, global risk managed accounts, it's not unusual to maybe have a thousand odd locations, whether the client has, and as an underwriter with your risk engineering team, there's no way you can even, well, first off, you don't get the data for a thousand locations. And secondly, there's no way you can wade through a thousand risk engineering reports given the amount of time that you have to underwrite risks. So for me, if you can actually have an underwriting assistant sit in alongside you, so maybe you concentrate on the large 10, 15 locations that you really go into drains up exercise from a risk engineering perspective, but then you have the machine that does the other 985 locations that actually is that support mechanism.
(28:50):
So you still do your underwriting, your case underwriting, but you also have this machine that helps you with the other locations that you quite simply don't get time to underwrite. And for me, the analogy I always give is that from my experience, is that typically speaking, not withstanding large cat losses, but if we take fire losses for example, more often than not those fire losses come somewhere from a location that's either in the middle or at the bottom of the schedule of values often overlooked. And typically it's because that particular location doesn't have the same grade of construction as the rest of the portfolio, or it's a business interruption bottleneck. And these are the kind of losses that can really come back and bite you. And often are actually for the really large insurers are often just, sorry, our net losses. So they don't even hit the treaty.
(29:37):
Maybe it's a 30 million loss or 40 million loss, but that would absolutely knock out your underwriting profitability. And yet you spend all that time protecting the really large risk, which is essentially really just protecting the treaty. So I think there's an opportunity here to actually sort of use the combination of both agen AI and traditional underwriting, because for me personally, if I had back then the tools that were available now, I think I would've been a better underwriter. I'm not suggesting I was a bad underwriter, but I think I could have been even better because it really is something that actually helps you, helps you with better, more targeted risk selection. So for example, you might say, look, actually this machine, the computer's telling me I've got two rogue locations here that I ought to do something about. Maybe I buy some factory insurance on those locations.
(30:28):
Whereas previously I might have just been focused on the really large locations and that's all I was worried about, for example. So I think there's a real opportunity. And I think the other thing I was to add here is that part of the discussions we are having at the moment is to try and take clients on the digital and AI journey. Some are more advanced than others, and then we get to the discussion where we have discussions around, well now I've, I've got run this past the unions or the workers' council for example. And yet in reality this is about ultimately existential survival. So you have CEOs talking around investor report, investor calls saying, we need to write 50% more premium, but with the same resources. Well, the reality is there's no way you're going to be doing that with the same quality unless you employ some form of agent ai, underwriting assistant. It's as simple as that. So the reality is you'll be able to do more with less resource. And that doesn't mean getting rid of people, it means being able to write more business. And if we extrapolate that, as we mentioned earlier around creating new risk pools, it might be more of a case of re-skilling some underwriters, but it's not about making loads of underwriters redundant.
Janet King (31:46):
Absolutely. Do you think the industry feels the right sense of urgency around this opportunity?
Gavin Lillywhite (31:52):
Personally, I think having been in the hard market for the last four years, it hasn't been top of the agenda because it hasn't needed to have been because obviously underwriters have been back to profit, rightfully so. They need to be in profit to continue. But I think that will start to come to the fore much more now as the competition starts to increase again. So we see more capacity coming into that finite pool of risk, which will ultimately drive down pricing. So the smart insurers will really start to see actually there's an opportunity to do something different here in a slightly less competitive market or less commoditized market markets. That's a better way of saying it. So I think it could have been better, but I think it will accelerate Now.
Janet King (32:36):
That's great.
Brandon Nuttall (32:39):
To paraphrase Warren Buffet, you don't really know who's been swimming naked until the tide goes out. And I think in our market, the tide is certainly going out now, and I think when the market has been so hard for so long, it makes it so that you can justifiably under in technology. And I think it is the organizations that have kept up with their investments that are going to basically stand the chance to leverage the soft market to build enduring competitive advantage. I think that's the real opportunity for organizations that maybe they've invested in digital transformation, modern platform since 2020. And I would encourage your audience, Janet, that if you have an organization that's properly invested in digital transformation in that timeframe, they're almost certainly primed to make the best out of the current difficult market situation.
Gavin Lillywhite (33:34):
And that brings another really important point for me, Janet, is that digital transformation is not just about efficiency saving, it's about growing new products and or writing more business with the same or limited resource. So it doesn't have to just be an efficiency plate, it's also growth play as well. Absolutely. Whether that's new product, whether that's more of the existing product, I'll give you an example. Some clients might say, well, look, actually, I can't write a piece of business unless it pays me at least $50,000. So for example, if we can actually support them in reducing the cost of transactions and reducing the cost of unit cost of distribution, maybe that number could come down to $20,000, for example. So all of a sudden that opens up more opportunity even within an area of business that they're quite familiar with and quite comfortable with, don't really have to do anything different other than the fact that because they put a more efficient distribution play, they can now write that kind of business.
Janet King (34:28):
I think that's such an important point, the growth angle and just growing the big pie. I think that's such an important point. Brandon, before we wrap this up, any final thoughts?
Brandon Nuttall (34:38):
So I'm just going to riff off of one of the final things that Gavin said. So global insurance market penetration is still less than 10%, right? There is a lot more insurance that could be sold than is currently sold. And so if mere digitization could grow the market, it should have buy now. So the fact of the matter is that much of the world is still underinsured or uninsured, and so I'm looking forward to having more conversations with my clusters, with my clients, with my stakeholders, with my peers, about how we can leverage AI to really unlock what I think is a critical moment in our market.
Janet King (35:19):
Well, thank you and thank you both for having this conversation with me today. I think it was quite fascinating. I think it's a great learning moment for our audiences. So thank you very much Brandon and Gavin for being here. Thank you to Exceedance for joining us and sponsoring today's episode.
