As AI moves from experimental to integral, insurers confront complex risks spanning cybersecurity, infrastructure, and physical safety, prompting a reevaluation of coverage frameworks amidst rapid technological integration.
Artificial intelligence is moving quickly from experimental promise to everyday operations in insurance, reshaping underwriting, claims handling, risk selection and customer service. But as the technology spreads, the industry is also being forced to reckon with a broader and more complicated risk profile, one that extends well beyond software bugs and into cybersecurity, liability, infrastructure and the physical world. McKinsey has said AI could transform distribution, pricing and claims by 2030, while also intensifying concerns around data privacy and governance, underlining the tension between efficiency gains and new exposure.
That tension is already visible in reported incidents. Verisk’s emerging issues team found that about 77% of AI incidents reported in 2025 had some possible insurance implication, suggesting that the technology’s risk footprint is widening alongside adoption. Deloitte has argued that generative AI can speed underwriting and improve decision-making, but it also warned that data quality and staffing remain persistent hurdles. The result is a market in which AI is no longer just a productivity tool; it is becoming a source of potential claims in its own right.
The concern is not confined to abstract model failures. Recent reports have pointed to security flaws in enterprise generative AI systems that allowed confidential material to be accessed and summarised, reinforcing fears that widely deployed large language models can create systemic exposure across cyber, professional indemnity and management liability lines. Insurance Business reported that Tokio Marine HCC’s review of major cyber losses in 2025 again highlighted ransomware, supply-chain compromise and concentration risk around major cloud and platform providers, themes that are likely to intensify as insurers and their clients rely more heavily on shared AI infrastructure. Tom’s Hardware has also reported that some insurers are responding by tightening exclusions or declining to cover certain AI-related losses altogether.
Much of that risk sits behind the scenes in the fast-growing buildout of data centres, the physical backbone of the AI economy. The insurance article notes projections that U.S. data centre construction spending could rise 23% this year and that the sector may produce as much as $10 billion in new premium in 2026. Yet the same expansion brings familiar property and casualty exposures: power interruption, fire, supply-chain disruption and contractor liability. The energy burden is especially stark. Industry forecasts cited in the piece suggest U.S. data centre electricity demand could reach 106GW by 2035, with the facilities accounting for as much as 12% of peak U.S. demand by 2028, while NERC has warned of mounting strain on the grid. Water use is also becoming a flashpoint, with some large sites drawing millions of gallons a day for cooling.
AI is also changing the risk profile on the road. Self-driving taxis and autonomous freight vehicles are becoming more visible, and the systems that control them depend heavily on machine learning, sensors and real-time data. Research cited in the article suggests more advanced AI models could make vehicles behave in increasingly human-like ways, but that does not eliminate danger; it may simply change the way accidents happen. A study from UC Santa Cruz found that embodied AI systems could be vulnerable to manipulation through misleading text in the physical environment, such as road signs. That raises difficult questions for insurers about whether future claims will be treated as driver fault, fleet liability or product liability.
For insurers, the central challenge is that AI does not create just one kind of exposure. It concentrates digital, physical and legal risks in the same ecosystem, often through a handful of dominant platforms and infrastructure providers. As McKinsey and Deloitte both suggest, the upside for productivity is real, but so are the governance, data and accountability gaps. The industry’s next test will be whether coverage frameworks can evolve quickly enough to keep pace with a technology that is now embedded not only in office software and underwriting rooms, but in the power grid, the cloud and the vehicles on the road.
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The draft above was created using the information available at the time the story first
emerged. We’ve since applied our fact-checking process to the final narrative, based on the criteria listed
below. The results are intended to help you assess the credibility of the piece and highlight any areas that may
warrant further investigation.
Freshness check
Score:
8
Notes:
The article was published on May 4, 2026, making it current. However, similar themes have been discussed in previous articles, such as ‘Emerging Risks to Watch: Agentic AI, Electric Motorcycles, and Hydrogen Fuel Cells’ from February 23, 2026 ([insurancejournal.com](https://www.insurancejournal.com/magazines/mag-features/2026/02/23/858363.htm?utm_source=openai)). This suggests some overlap in content, which may affect originality.
Quotes check
Score:
7
Notes:
The article includes direct quotes from McKinsey and Deloitte reports. While these sources are reputable, the quotes cannot be independently verified without access to the original reports. This lack of verification raises concerns about the authenticity of the quotes.
Source reliability
Score:
8
Notes:
The article is published by Insurance Journal, a reputable industry publication. However, the reliance on secondary sources like McKinsey and Deloitte reports, which are not directly accessible, may limit the ability to verify the information independently.
Plausibility check
Score:
7
Notes:
The claims about AI’s impact on the insurance industry are plausible and align with industry trends. However, the lack of direct access to the cited reports makes it difficult to fully assess the accuracy of these claims.
Overall assessment
Verdict (FAIL, OPEN, PASS): OPEN
Confidence (LOW, MEDIUM, HIGH): MEDIUM
Summary:
The article presents current and plausible information on AI’s impact on the insurance industry. However, the reliance on secondary sources with unverifiable quotes and limited access to original reports raises concerns about the accuracy and independence of the content. Further verification is recommended before publication.
