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Is AI Spying on Your Roof? How Insurers Use Tech to Raise Rates

It is common for insurance companies to inspect properties for damage in an attempt to mitigate future losses. But over the last few years, there has been a significant shift in exactly how those inspections are done.

In the past, a human inspector would look at the property in person. Today, insurers are replacing inspection agents with drones, satellites and artificial intelligence systems to review footage and determine whether a policy should be canceled.

The consequence for homeowners? Higher premiums, nonrenewals and coverage issues based on remote evaluations have increased.

How Are Insurers Using AI?

Insurers are currently using AI in a variety of ways, including claims settlement, risk assessment, risk mitigation and investigating covered homeowners insurance claims following a loss, according to the American Property Casualty Insurance Association’s response to a request for drafting an AI model law. Insurance companies are also using AI to model the potential for catastrophic weather events and assess the likelihood of a policyholder sustaining losses from catastrophes, such as hurricanes, wildfires and earthquakes.

Insurers are using these AI-driven underwriting models to save time and reduce expenses, according to Chip Merlin, founder and president of Merlin Law Group, a national insurance claim litigation firm that handles property insurance claims. But some companies are using different terminology to sidestep regulations, he wrote in an email. “Many insurers are intentionally avoiding the term ‘AI’ to avoid regulatory scrutiny, relying on external vendors rather than building their own software. Regulators are starting to make strides toward transparency and accountability … but the laws are still behind on this technology.”

AI Liability Is No Longer a Future Problem for Risk Managers

AI-related incidents rose approximately 50% year over year from 2022 to 2024, and exposure is building across multiple insurance lines, according to WTW's Willis Research Network.

Artificial intelligence has moved from operational experiment to embedded infrastructure across insurance and corporate risk functions, but governance, liability frameworks, and coverage structures have not kept pace, according to a new report from WTW’s Willis Research Network.

The report documents a surge in AI-related incidents, up roughly 50% year over year from 2022 to 2024, with 2025 incidents having already exceeded 2024’s total before that year concluded. The report warns that organizations are accumulating exposure across general liability, professional indemnity, cyber, EPLI, and D&O, often without policy language that explicitly addresses AI risks.

Silent AI Risk Spreading Across Lines

The report frames AI less as a standalone peril and more as a risk amplifier across existing coverage lines, a dynamic it compares explicitly to silent cyber exposure before 2019, when ambiguity in policy language narrowed quickly once claims and litigation materialized. WTW describes this as the “silent AI” problem: AI-related losses can sit embedded across multiple lines and many insureds simultaneously, invisible until a claim forces interpretation.

AI-related losses are already reaching insurers through multiple pathways, the report said. When AI causes bodily injury or property damage, in autonomous vehicles, industrial systems, or medical devices, courts typically analyze traditional negligence and product liability frameworks, focusing less on whether AI was involved than on whether reasonable care was exercised in deployment and supervision.

Insurers Must Plan for Tech’s Unintended Consequences

Insurance carriers are facing a familiar problem with increasingly significant implications: technology decisions made to solve one problem often create others.

Cloud adoption offers a good example. Many insurers moved workloads to cloud providers because the cloud was perceived to be cheaper. In many cases, it wasn’t.

That does not mean the move was wrong. Cloud environments can deliver important advantages in disaster recovery, business continuity, flexibility and scalability. But those benefits were not always the original goal. When the expected result is cost reduction and the actual result is a different operating model—with new dependencies, new governance requirements and new cost dynamics—the decision needs to be understood in a different light.

Every decision has consequences, which is clearly the point. A problem or opportunity is recognized, a plan is developed, resources are deployed and action is taken. In the postmortem phase, actual results can be compared with expected results, and a new set of decisions can be framed.

Simple enough in theory. In practice, however, the unexpected consequences can be highly consequential. In a worst-case scenario, the cure can be worse than the disease. A mentor once shared with me that surprise outcomes are generally bad because they reflect a failure to plan. Clearly there are surprise endings that turn out well—Teflon and Sticky Notes come to mind—but only after accepting that the original goal of those science projects had failed.

Rob McIsaac is the President and CEO of RPM Ventures

Exclude It, Harness It, Get Greedy: McGavick’s Take on Insurers’ AI Playbook

Executive Summary

Mike McGavick, the former CEO of XL, sees exciting possibilities ahead for AI to refocus the insurance industry on problems it is designed to solve. But today, the industry is falling short, he says.

During a keynote speech at the Casualty Actuarial Society Seminar on Reinsurance this week, McGavick was enthusiastic, not just about AI but also about the role of actuaries at the center of change, influencing trust in AI models. Still, he believes insurers aren’t living up to pronouncements they are making about their ability to harness AI today, nor are they fulfilling their central promises to efficiently transfer risks from society as they emerge. MORE