Closing the AI value gap: Why ROI and governance define the next chapter of insurance AI

But Everett's real target is a gap he sees industry-wide between AI enthusiasm and AI results. Too many organizations, he writes, are counting how many AI projects they've launched instead of measuring what those projects actually changed — claims leakage, time-to-policy, persistency, servicing cost. He's equally blunt about governance: rather than a compliance drag, he frames explainability, audit trails, and bias testing built in from the architecture stage as what actually lets insurers move fast and earn the trust of regulators and reinsurers.

The piece also makes a case for talent as India's most underrated AI advantage — engineers who understand actuarial concepts, data scientists fluent in underwriting logic — and warns against the common failure mode of AI succeeding in one pilot market and stalling for a year and a half before anyone can scale it elsewhere. His closing argument is that closing the AI value gap is a leadership problem before it's a technology one: the tools and data are already available, what's scarce is the discipline to hold AI initiatives accountable to outcomes rather than hype.