Insurance professionals are increasingly asked to review, or rely on, an answer an AI system produced — about a coverage question, an eligibility check, a claim status. The question that matters is not "does this sound right," but "how do I know it's right."
Start with the source. A useful answer should point back to where it came from — the specific document, the specific clause, the specific record — so a professional can check it in seconds rather than search for it from scratch. An answer with no way to trace it back is not something to build a decision on.
Look at specificity. A vague, general answer ("this is usually covered") is easier to produce than a precise one tied to the actual terms in front of you. The more specific and checkable an answer is, the more useful it is — and the easier it is to catch when something is wrong.
Ask what happens when the system is unsure. A well-built system should be able to say it doesn't have enough information, or flag a case as needing a person, rather than guessing with confidence. Confident-sounding wrong answers are the most dangerous kind, because they are the easiest to trust without checking.
Test it on cases you already know the answer to. Before trusting an AI answer on a new case, run it on a handful of past cases where your team already knows the correct outcome. Consistency across similar cases, and disagreement flagged rather than hidden, tell you more than a single impressive demo.
Keep a person in the loop for anything that affects money, coverage or a customer relationship. Evaluating an AI answer well doesn't mean fully automating the decision — it means giving your team a faster, better-sourced starting point, with a clear, easy path to verify or override it.