Martin Henley, chief executive of mea Platform, argues that as re/insurance pricing softens, the biggest lever for growth is not rate but the operating model. He sees AI creating underwriting capacity by cutting processing costs and cycle times, allowing carriers to quote faster and handle more submissions while keeping underwriters focused on risk selection and relationships.
"In my view, growth in this market is really going to come from the operating model, not so much the rate," Henley told Monte Carlo Today.
His company, an AI-native insurance technology firm founded in 2021, automates workflows for carriers, brokers and MGAs. After bootstrapping initially, mea secured a $50 million minority growth investment from SEP in February and is scaling across the US, London, Bermuda and Europe, as well as moving into Asia.
Early adopters pulling ahead
Henley sees much of the industry's AI activity concentrated on experimentation and back-office efficiency. Only a "pretty small group" is running AI in production at scale, with some having done so for two years or more. The gap between those operators and slower adopters will become increasingly visible, he said.
"We literally have clients saying to us, 'I'm going to win this market and this product line because of this tech,'" he said. After 30 years in insurance, he finds that striking. "It is unusual to hear underwriting teams making statements like that."
For executives mapping their own AI for Executives & Strategy, the lesson is that production deployment, not pilot count, separates leaders from the pack.
Changing the economics of the book
The more consequential question is what insurers do with the productivity AI creates. Henley frames it as a choice between headcount reduction and redeploying expert staff toward understanding risk.
"If you get those things right and the cost of processing a risk comes down sharply, then the economics of your whole book start to change," he said. "The overall prize here is capacity. You're able to handle more new business and more submissions coming in. You're able to quote faster and convert better."
Some clients are using that capacity to expand appetite or enter lines they did not previously write. Underwriters stay in the decision, with AI taking on repeatable work while specialists spend more time on pricing and relationships.
Performance is the test
Henley is sceptical of judging AI programmes by the number of pilots or proofs of concept. The tougher test is whether the technology is running on live business and producing measurable results.
"If you get it right, AI is able to put better risk information in front of underwriters - or brokers - faster, at the point of decision," he said. Cleaner, more complete information should show up in the measures insurers already use: hit ratio, loss ratio, combined ratio.
He also offered a sharp critique of what insurance technology has delivered before AI. Much of the transformation during his career amounted to digitising existing processes without fundamentally changing the work. Core-system replacements, he said, have sometimes consumed tens or hundreds of millions over several years for "questionable benefit."
"It's quite easy to run a proof of concept and also quite easy to run a pilot," he said. "Turning that into something real, something we are now relying on for the business - whatever the AI is doing - is quite tough."
That distinction becomes more important as market conditions soften. Rate may be outside an individual company's control; the efficiency of its operating model is not. "The soft markets will reward operators," Henley said. "While the whole market is debating rate, there's an opportunity right now to take real cost and cycle time out of your operating model."
Why this matters for insurance professionals
The companies that move AI from pilots to production will gain capacity to write more business and respond faster to submissions, even as pricing pressure intensifies. For underwriters and brokers, the shift is not about replacement but about spending less time on administrative tasks and more on the work that drives profitability: risk selection, pricing and client relationships. The technology exists now. The differentiator is execution.
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