Insurance

Insurance executives are increasing AI spending as a share of P&C revenue threefold this year, according to BCG's 2026 analysis, yet only 38% of carriers are generating value from AI in core workflows. But the Center for Economic Justice warns that the real strategic misstep isn't about technology performance - it's about starting with the tool instead of the outcome.

Birny Birnbaum, executive director of the Center for Economic Justice and a longtime consumer representative at the National Association of Insurance Commissioners, sees genuine promise in AI and big data for underwriting, pricing, claims and risk prevention. His warning is that insurers must decide what outcome the technology is meant to serve before building it into the business.

"Before we choose our tools and our techniques, we must first choose our dreams and our values," Birnbaum said. "Some tools will serve them, while others will make them impossible to achieve."

The outcome question

The difference matters because AI investment is accelerating without clear alignment. The usual answer to "what is AI for" is efficiency: faster sales, faster underwriting, faster claims. Birnbaum does not object to that. "AI technologies hold tremendous promise for increasing the availability and the affordability of insurance," he said. "They hold great promise for making the sale of insurance and the claim settlement process more efficient."

But efficiency is not neutral. An AI system that identifies vulnerable building materials could direct mitigation dollars to homes most likely to suffer losses. The same insight could also be used to further segment the book, avoid certain risks and leave consumers with fewer affordable options. The data point is identical. The outcome is not.

For executives shaping AI strategy, this reframes governance as a strategic choice - not a technical one. The first decision is not whether a model performs. It is whether the model advances the carrier's stated purpose. That perspective is directly explored in AI for Executives & Strategy, which focuses on outcome-driven approaches for leadership.

"So the issue isn't what technologies are good or bad," Birnbaum said. "It's can we use the technology in a way that promotes the public policy goals of insurance, which are strengthening the risk pool, broadening the risk pool, creating more availability and affordability, and promoting loss prevention and risk mitigation."

From selection to prevention

A claims tool that gathers documents for an adjuster may improve service without transferring judgment to the model. A property analytics system that identifies where roof, drainage or vegetation improvements would reduce losses can support underwriting and resilience. A pricing model designed only to identify the most profitable customers may improve targeting while worsening the broader availability problem.

That availability problem is no longer theoretical. The US Treasury's Federal Insurance Office said in January 2025 that homeowners insurance is becoming more costly and harder to procure for millions of Americans as climate-related events intensify. Birnbaum's view is that AI and big data could help address that problem if directed toward loss prevention and mitigation, not merely risk selection. He pointed to climate vulnerabilities in property insurance and accident likelihood in auto as examples where analytics could guide prevention.

For carriers looking to apply AI in insurance markets responsibly, the focus on loss prevention rather than risk selection is a key principle covered in AI for Insurance training.

The boardroom test

The practical test is simple: when an AI use case reaches the executive table, ask who benefits if it works. If the answer is only the carrier - through better segmentation, lower expense or selective appetite - the project may still be lawful and attractive. But it is not the same as a project that improves service, directs mitigation, expands availability or helps insureds reduce loss.

Birnbaum argues that insurers have tended to respond to climate risk by cutting coverage, narrowing availability and shifting risk onto consumers and public programs. AI can reinforce that playbook or challenge it. The choice happens before implementation.

"The use of AI and big data can be instrumental in helping identify opportunities for investments in resilience," he said.

Why this matters for Executives and Strategy

The most important AI question for insurers is not what the model can predict. It is what the company is trying to make possible. A tool built to find profitable risks will do that. A tool built to prevent losses, improve affordability and preserve insurability must be designed, measured and governed differently from the start. For C-suite leaders, this means AI governance cannot be delegated to IT. It belongs in the strategy conversation - and the answer to "who benefits" determines whether the technology strengthens the risk pool or accelerates its segmentation.