AI Solutions Beyond the Hype

ReSource Pro recently conducted a poll of 150 insurance leaders on AI and digital readiness at Summit, its flagship client event. Did any results particularly stand out?

DAN EPSTEIN: One of the key takeaways is that it’s very clear agencies see that AI is real. It’s compelling. They believe the promise is significant, but they also believe that the maturity level is low.

When we asked people to look at the Gartner hype cycle, 62% felt that AI was in the earliest stage, which is called the innovation trigger, and only 1% saw it as at a kind of a steady state, more productive layer level. In that sense, it’s still seen as something that is full of promise but not yet ready for prime time. ReSource Pro is offering to share this primary research with readers who are interested in exploring the findings further.

What’s standing in the way of effective AI adoption?

EPSTEIN: One of the key constraints is the sheer fragmentation of the work inside most insurance organizations. We have seen instances where the same insurance organization has thousands of different ways they processed the same task.

We see an opportunity for significant standardization and centralization, and we work with agencies to help them get there.

It’s important for insurance organizations to look across their businesses and understand what level of customization is appropriate and important given the specifics of the line of business and characteristics of the risk to determine what level of variation is what you might think of as over-servicing or over-customization.

There’s been a lot of hype around generative AI. How should companies navigate that hype to find the best solution for insurance industry pain points?

CHRIS WATKINS: If you look at AI, there’s a core group of large language models that everyone is building on. And so, the deep science behind that isn’t what matters in applying use cases. What’s important to building effective use cases is the insurance domain expertise combined with knowing how to utilize these large language models and build engineering around them. The importance of having someone who deeply understands insurance and is able to translate that into an agentic platform is really what’s critical. We’re seeing a lot of companies that are overindexing on the agentic AI side but not bringing this deep insurance domain expertise.

How can a business effectively maintain that balance between human and technology?

EPSTEIN: Our survey was very interesting, because it was clear that our clients saw that AI on its own was not sufficient. We asked, “To achieve value and scale, how much effort should be dedicated to people & processes versus tech & algorithms?” Fifty-two percent said 70% [of effort should be focused on advancing] people and processes. Forty percent said an even split of focus should be applied to people and processes and AI—so 92% that think at least 50% of the focus should be on people and process.

If you’re going to rely on AI agents, you have to have confidence that they are doing insurance work correctly and not with any kind of bias or hallucinations. It’s going to be people with deep process knowledge and nuanced understanding of insurance that are going to be required to oversee the outputs.

WATKINS: ReSource Pro has been on this journey for many years. Take, for example, extracting data from PDFs. There’s a lot of domain expertise and engineering process discipline that goes into putting together a system to not only improve the accuracy of AI-enabled data extraction, but to understand where the inaccuracies are and have confidence in the results. Through close collaboration with insurance experts at ReSource Pro, we’ve been able to improve the accuracy from 80% to 99% with a 95% confidence factor of exactly where any errors might be. That’s important because otherwise you spend too much time auditing for errors.

What roles will AI play in the insurance industry of the future?

EPSTEIN: Ultimately, if you fast-forward three to five years, it’s going to be in every aspect across the value chain. It’s going to be driving processes, informed decision-making, and enriching data. The big piece that we have to index on is how we’re trained to use it. Being able to bring insurance knowledge and process discipline together with expertise in agentic AI and workflow orchestration will assure that we move from edge cases to truly scalable solutions