AI tools in insurance workflows are fabricating policy exclusions, inventing coverage limits, and generating claims summaries that assign causes of loss not in the file—and someone is always responsible when the client finds out
Insurance is a domain where the gap between what AI says and what is actually true carries immediate financial and legal consequences. Policy language is precise. Coverage determinations are binding. Exclusions are enforceable. When an AI tool working through a claims workflow invents an exclusion that does not appear in the policy document, the result is a claim denial that may be legally indefensible. When a customer-facing chatbot generates a coverage limit because the actual policy language is ambiguous and the AI resolves the ambiguity on its own, the insurer has made a representation it may be required to honor.
These are not edge cases from academic papers. Roots AI and Owl.co, both insurance technology firms, have documented specific examples: a claims assistant referencing a non-existent exclusion; a claim summary attributing a cause of loss that does not appear in the claim file; a customer chatbot inventing a coverage figure rather than acknowledging uncertainty. The common thread is AI systems filling in gaps with confident, plausible output rather than flagging what they do not know.
Insurance producers occupy a legally specific position. Their role is to understand client needs, explain coverage accurately, and ensure the policies they place actually protect the clients they serve. When an AI tool assists a producer by drafting a policy summary, explaining coverage options, or generating a renewal review document, the producer’s name and license are attached to that output. If the AI summary misrepresents what is covered—by omission, by imprecision, or by outright invention—the producer bears the E&O consequence.
Courts have already established that companies are responsible for what their AI systems communicate to clients (the Air Canada ruling being the most cited precedent). In insurance, the regulatory layer adds additional teeth: producers have affirmative duties of disclosure and accuracy that do not diminish because an AI tool made a mistake. The question for insurance sales organizations is whether their use of AI in client-facing communication is producing outputs that their human producers have actually reviewed and can stand behind—or whether the AI is operating as a de facto spokesperson with no human accountability in the loop.
Verisk’s January 2026 general liability endorsements are a signal worth paying attention to. A major insurance data and analytics company providing traditional carriers with the option to exclude generative AI from their own liability policies is, in effect, communicating that AI-generated outputs carry meaningful, documented risk—risk significant enough to warrant formal exclusion language in standard policy forms.
That response from within the insurance industry itself validates what insurance producers already know intuitively: the client relationship depends on accuracy, and accuracy in complex, policy-specific conversations is something AI tools consistently struggle to deliver without human oversight. The producers who will maintain their clients’ trust over the next decade are not the ones who automate client communication—they are the ones who use technology to make themselves more efficient while keeping human judgment in the loop for everything that reaches a client.
