On this episode of FNO: Insuretech, hosts Rob Beller and Lee Boyd sit down with Andrew Yeoman, Co-founder and CEO of Concirrus, for a wide ranging conversation about how AI is reshaping specialty underwriting and why the next phase of insurance innovation may look very different from what most leaders expect.

Broadcast across continents from the U.S. to the UK, the episode brings a global perspective to how AI is being applied inside real underwriting workflows today. Andrew combines deep domain expertise with a pragmatic view of how AI actually delivers value, moving the discussion beyond hype into measurable operational change.

From data overload to underwriting clarity

The conversation begins with the origin story behind Concirrus and its focus on specialty insurance. Andrew traces the company’s early roots in telematics, where vast amounts of data were available but largely unusable by insurers due to limitations in systems, analytics, and workflow integration.

That experience revealed a core industry problem: insurers often have access to more data than they can effectively use. Early efforts to introduce richer datasets like thousands of parameters in marine underwriting sometimes made decisions harder rather than easier.

The breakthrough came not from adding more data alone, but from combining data with process automation. Concirrus shifted toward building platforms that simplify underwriting workflows while embedding intelligence directly into decision making, helping underwriters act faster without increasing complexity.

Automating the work underwriters don’t want to do

A major theme throughout the episode is the distinction between the work underwriters need to do versus what they want to do.

Administrative tasks such as data entry, lookup, and manual processing can consume roughly 40 percent of an underwriter’s time. Concirrus’ approach is to automate those tasks completely, freeing underwriters to focus on higher value activities like structuring deals and managing risk.

The results, as Andrew describes, can be dramatic:

• Significant reductions in manual system usage

• Faster quoting cycles

• Increased job satisfaction among underwriting teams

• Material improvements in expense and loss ratios

In one example discussed, time to prepare a quote dropped from 48 hours to just 90 seconds, illustrating how AI driven workflows can compress entire operational timelines.

Why specialization matters in AI

Unlike many platforms that aim to be broadly applicable, Concirrus has taken a different approach by building AI systems tailored to specific lines of business.

Andrew explains that an aviation underwriter, a marine underwriter, and a crisis risk underwriter all think differently about risk. As a result, AI systems designed to support them must reflect those differences rather than assume a one size fits all model.

This specialization allows the platform to deliver more relevant insights and recommendations, aligning closely with how real underwriting decisions are made. It also reinforces a broader point: domain expertise, not just technology, remains a critical differentiator.

The myth of AI native and what matters instead

One of the more thought provoking moments comes when Andrew challenges the idea of being an AI native company. In his view, labeling a business as AI native is increasingly meaningless as AI becomes ubiquitous, much like claiming to be internet native today.

Instead, he introduces a more useful framework for thinking about technology adoption:

• Ignorers which are organizations that resist new technology

• Adopters which are those that incorporate it into existing models

• But fors which are businesses that would not exist without the technology

The most transformative companies, he argues, fall into the third category. These but for AI organizations are not just improving workflows, they are building entirely new operating models around AI capabilities.

A glimpse of the AI driven insurance enterprise

Looking ahead, the conversation explores what those new models might look like. One idea that stands out is the concept of a solopreneur insurance entity, an MGA run by a single individual, with AI agents handling nearly all operational functions.

While still aspirational, Andrew believes this is a question of when, not if. As AI systems become more capable, the traditional relationship between headcount and scale could fundamentally change.

The discussion also touches on how AI may enable entirely new insurance products. For example, dynamically adjusting coverage across home, auto, and travel policies based on real time behavior and context, something that would be impractical without automation.

The evolving structure of the market

Another key thread is how innovation will play out across different parts of the insurance ecosystem. Andrew points to MGAs as the primary drivers of rapid experimentation, given their agility and ability to deploy capital quickly.

Meanwhile, traditional carriers with stronger balance sheets are likely to act as consolidators, scaling successful models once they are proven. This dynamic creates a feedback loop where innovation and scale reinforce each other over time.

A golden age moment for insurance

As the episode closes, Andrew offers an optimistic perspective on the broader trajectory of the industry. He describes the current moment as a golden age for insurance, where advances in AI are enabling capabilities that were previously unimaginable.

Rather than focusing solely on risks or disruptions, he emphasizes the opportunity: empowering individuals and organizations to operate beyond traditional constraints, rethink business models, and create entirely new forms of value.

Rob and Lee echo this sentiment, highlighting both the pace of innovation and the shift in mindset required to fully embrace it. Episode 305 ultimately becomes less about any single technology and more about how AI is expanding what is possible, from underwriting workflows to the very structure of insurance organizations themselves.