InsTech - insurance & innovation
00:17:57 · Matthew Grant
[00:17:57] S1: Let's come back to the AI. We're watching this real because everyone's talking about the topic, can we drill down to what difference it makes? There's one application you had that I was really fascinated by, is not so much core business, but helps you run the business, which is your use of Gong, which is a tool that you use to, as I understand it, to listen into the conversations or they initiate it, but your sales team is having, and then the tool actually scores people on the quality of the conversations they're having and actually records the information obviously with the client's permission or prospective client. But I'd love to learn a bit more about that because it sounds very intriguing and it's a very practical application of the use of some of the new tools coming out.
00:18:38 · Richard Gunn
[00:18:38] S2: Yeah. So, unsurprisingly, we have put a big emphasis on how we can leverage AI internally. I would refer to Amrit as a... Who's our CEO and and cofounder, as an AI maximalist. And we internalized very early on kind of the mantra that you need to work out how to leverage AI better and faster than your customers and your competitors. And if you do, you will win, and if you don't, you will lose. And kind of that is what motivates me to make sure that we're not just thinking about how do we build agentic capabilities for our clients to use, but also how do we use them internally. Because if we can be 10 times more effective as a business, then we could deliver much more value for our clients. So specific to Gong and the go to market function or the revenue function, that's quickly become kind of table stakes to have some form of recording capability and then leverage AI to evaluate how those calls are going and how you can improve. Some of the areas that we look to do that, like, one is to support forecasting, for example, because you can scan those scripts and identify where you should have more confidence. And maybe we'll add some waiting now based on the words that they use in New York versus in in London, thanks to your guidance there. One area that we've recently built out, which is really, really cool, actually, we have business value consulting capability, so that's about working with our clients to, first of all, understand the value that they should get from our platform. And then once we bring them on as a customer, it will be... We'll go through business value planning, so, like, what's the most effective way to bring value to the business as fast as possible, and then we'll do a BVR, business value realization. So a year on, did you get the value that you expected from our platform? And if not, how do we need to approach it differently? And we've now found a way that we can do... We can pull our early conversation transcripts from actuaries, from underwriters, actually into court code, and then use that to create the first version of a business value report. And so rather than saying, wait, I now need to book in time with your underwriters and your actuaries, we can actually say, look, we've listened to our first five hours of speaking with you as a prospect.
00:20:23 · Richard Gunn
We know we're moving towards a kind of a proof of concept or a proof of value. But just to give you a high level based on the pain points you've surfaced and our viewer benchmarks across our 6E customers, this is the level of impact that a project like Hyperexponential might be able to have. Does that align with your expectations? Does that surprise you? What are we missing? Because obviously we'll be missing stuff at that point. We're not going to solve for it all without a specific kind of project related to it. That's just a great example where previously... Like, we built a team of many ex McKinsey and other kind of big four strategy consultants who would have spent hours and hours around that, and now we can run that out very quickly, and they can spend their time actually challenging it, seeing where the gaps are, and making sure it's specific to an insurer and reinsurer rather than a more generic cripple because you're spending a lot of your hours just doing heavy lifting.
00:21:08 · Matthew Grant
[00:21:08] S1: I'm glad you mentioned that point about pain points because... I mean, no surprise, it sort of goes back to Amrit's background when he was an actuary, he'd also, can feature engineer, software engineer, so he could write code, he said, I understand from him, there's some things he wanted to do, couldn't do it when he was working for an insurance company and built a company to solve it. Actually, interestingly, we've got a webinar coming up with one of the large US insurers and they're asking them how they deploy AI, and they look for where people are complaining. When they get a complaint from somebody about they can't do something, they go, Right, okay, can we deploy an AI solution here to solve that problem? What I think is fascinating is at one level is we're sort of hearing about companies still struggling to articulate the large investments in AI, but actually in the companies themselves, I mean, I think you're a great example of this, people finding solutions to actually just create that efficiency at the operational level. But then I'm sure you're also finding ways to accelerate both the development of what you're offering to your clients with the hx tools themselves? Is there anything there you just want to draw attention to that is tapping into large language models or agentic AI or other ways of just improving that experience?
00:22:20 · Richard Gunn
[00:22:20] S2: Yeah, I'll probably try and tie the two together. What we find interesting is you have the approach where you're looking for specific pain points and you solve for them leveraging AI, and that kind of brings people on the journey, has some great wins, and generates an immediate ROI, or hopefully, depending on what kind of report you read. But then there's also a question that we ask ourselves is, where is there an opportunity to redraw the traditional boundaries of different roles or different capabilities as a result of the evolving capabilities of AI? So this is something we ask ourselves a lot in the context of actuaries, underwriters, and IT, like even pre Gen AI, a key area that we look to add value was to bring together those key stakeholders around a risk and allow them to huddle and iterate on a risk in a way that's very hard to do and you haven't got the right kind of fluid level of technology that sits in between. And so it's so important to do in the Northern London market, in particular, as well as kind of broadly across the commercial B and C space. But we see it outside of insurance as well. If I think about our product org, very traditionally, you have three key stakeholders huddling around the product development, if you will. You have the product team. You'll have the design team who'll focus more on the UI and UX, and you have the engineering team who actually build it, and then we'll subsequently support it. And we're seeing with the evolution of AI and kind of enterprise grade vibe coding, if you will, or enabling you to push something through to semi production much faster than you could beforehand, those three roles consolidate into what's being sometimes referred to as a builder. And that's also exciting because you can start to think around, maybe we can have one of our customer success managers who works very closely with one of our clients. And maybe you're one of our clients, and you say, like, no. Richard, I've got some challenges with my experience rating and the triangles and how they currently work. I've seen it work in this way more effectively.
00:24:00 · Richard Gunn
And if you and I could mock that up and then I could push that straight through to a builder who could actually flow that through to the code, can suddenly see that the feedback and the iterative nature of working closely with the clients and at the cold face and how you feed that into your product could really accelerate. So it's a challenge I'm asking myself on the go to market side too. Like, should we be seeing the advent of the first full stack AE account executive who can support on the customer success side as well as the technical engineering side? Or actually, are we just gonna see kind of the opposite of that point of view would be the key buyers of specialization. You'll able to see much more productivity for each of the stakeholders. So where you have solutions engineer, could they support five clients rather than two clients or 10 clients rather than five clients? And could an account executive, because you've got agent doing a lot of the administrative work, Could they carry a much bigger quota, or could they support more clients as well? So there's a lot of kind of testing and learning on that, lots of very healthy challenge from VCs around benchmarks that they're seeing, around productivity at the same time as speed. And, yeah, we're excited both in terms of how we can hopefully put that in the hands of our clients with some other agenting capabilities that we've been releasing, also internally, how can we use it all the way from products and engineering through the go to market.