Before actuarial tables, satellite imagery and machine learning, insurance was fundamentally an act of collective confidence–the willingness of many to absorb what anyone could not bear alone. That social compact has always required two things in roughly equal measure: courage to take risk and discipline to price it appropriately.
A soft market tests both.
We're beginning to hear more about a soft market on the horizon, characterized by lower premiums and broader coverage. When premium rates compress, and when competition and the temptation to grow the market intensify, that is precisely when the industry's long-cultivated analytical instincts matter most. That makes this a critical time to deploy AI that delivers results.
Here is a view neither breathlessly optimistic, nor reflexively cautious: AI represents a genuine and durable shift in what is possible for insurers. But in a soft market, the question is not whether to embrace it. The question is how—and with whom.
The insurance industry has navigated technology transformations before. The introduction of computerized rating systems in the 1960s and 1970s radically accelerated underwriting workflows. The industry saw the proliferation of catastrophe modeling in the 1980s and 1990s. In each case, the technology itself was neutral. What differentiated the carriers that thrived from those that struggled was not access to the tool; it was the rigor with which they applied it.
AI is no different. The current generation of generative and agentic AI tools is genuinely powerful—capable of synthesizing complex data, accelerating workflows and surfacing insights that would take human analysts hours or days to prepare. These are real capabilities with real commercial value. But in a regulated industry, capability deployed without governance, explainability and accountability is not innovation. It's exposure.
In a hardening market, insurers have natural incentives to be selective. In a soft market, the calculus shifts. The pressure to compete on speed and cost is real. And AI, marketed as a path to radical efficiency, can seem like exactly the right tool at exactly the right moment.
But efficiency and accuracy are not synonyms. An AI system that processes submissions faster does not necessarily process them better. The promise of AI in insurance is not speed alone. It is speed grounded in the kind of authoritative, regulatory-grade and use-case-specific data and insights that the industry has spent decades building.
When pricing competes on thin margins, even modest inaccuracies in loss cost assumptions, claims estimates or exposure assessments compound quietly.
The result worth seeking is not AI that replaces the judgment of underwriters and adjusters. It is AI that puts authoritative insight closer to where those professionals work. It's about creating speed without sacrificing explainability and defensibility. A restoration contractor can develop an estimate in a fraction of the time. An actuary can explore loss indications across multiple dimensions in a single workflow. In both cases, the human navigates thousands of data points through conversation, more easily than ever, and remains accountable—and the data underlying their decisions remains trustworthy.
I am genuinely optimistic about what AI can do for insurance—and for consumers and policyholders that insurance exists to serve. The ability to put better information in the hands of underwriters faster, to help restoration professionals deliver more accurate and timely estimates and surface emerging risk patterns before they become losses are meaningful improvements in how the industry fulfills its fundamental purpose.
The need for urgency is not theoretical.
But the insurers that will benefit most from AI are not those who deploy it fastest but those who deploy it on a foundation of trusted data, within a governance framework that regulators and policyholders can rely upon.
The most important thing AI can do for insurance right now is to make the humans making those decisions better informed, more efficient, and more confident that the data beneath their judgment is worthy of the trust the industry has always asked of the world.
