Artificial intelligence is rapidly transforming business operations, but it is also creating a new and complex landscape of liability risks. What was once considered a future challenge has now become an immediate concern for organizations, insurers, and risk managers worldwide.
A recent study by WTW’s Willis Research Network indicates that AI-related incidents have been increasing at an alarming rate. Reported incidents grew by nearly 50 percent annually between 2022 and 2024, and early data suggests that the number of incidents in 2025 has already exceeded previous years. This trend reflects the growing dependence of businesses on AI-driven technologies and the corresponding rise in potential legal and financial exposures.
One of the most significant concerns highlighted by experts is the emergence of “silent AI” risk. Similar to the concept of silent cyber risk that challenged insurers in the past, silent AI refers to AI-related exposures that may exist within insurance policies without being specifically identified or addressed. This can create uncertainty regarding coverage when AI-related claims arise.
AI-related liabilities are no longer confined to a single insurance segment. Exposures are spreading across multiple lines, including professional indemnity, general liability, cyber insurance, employment practices liability, and directors and officers liability insurance. As organizations increasingly integrate AI into decision-making, customer service, automation, and operational processes, the likelihood of liability events continues to grow.
Physical injury and property damage caused by AI-enabled systems represent one area of concern. Autonomous vehicles, smart manufacturing systems, medical devices, and automated machinery may all expose organizations to legal claims if failures occur. In such situations, courts are expected to apply traditional negligence and product liability principles while evaluating whether organizations exercised reasonable care in the deployment and oversight of AI systems.
Financial losses resulting from AI errors present another challenge. Determining accountability can be difficult because AI ecosystems often involve multiple stakeholders, including technology providers, model developers, data suppliers, system integrators, and end users. This complexity can lead to disputes regarding responsibility and insurance coverage.
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