Insurance organizations evaluating AI often start with the most ambitious use case — a fully automated claim decision, an end-to-end underwriting flow. That is usually the wrong place to start.
Choose a process with a clear, existing right answer. Pick a task where your team can already tell, quickly, whether an AI-generated answer is correct — a renewal summary, a coverage question, a policy lookup — rather than a judgment call with no clean benchmark.
Choose a process where a person stays in the loop by default. The best first pilot augments a task a professional already performs, rather than replacing a decision outright. This keeps risk contained while you learn how the system actually behaves.
Choose a process with enough volume to learn from. A task that happens twice a month won't generate enough cases to tell you much in a reasonable pilot window. Look for something that happens often enough to produce a real sample within weeks, not months.
Choose a process where the cost of a wrong answer is contained. Early pilots should sit somewhere a mistake gets caught and corrected before it reaches a customer or affects a payment — not on the highest-stakes step in the workflow.
Involve the team who will actually use it, from day one. A pilot chosen by leadership but never adopted by the frontline team that would use it daily tells you little about whether the approach works in practice.
Treat the first pilot as a way to build an evaluation habit, not just to test one tool. The metrics, review routines and oversight you put in place for pilot one are what make pilot two, three and four faster and safer to run.