In most industries, "move fast and iterate" is reasonable advice for a first AI pilot. In banking, insurance, and healthcare, it's a good way to end up explaining yourself to a regulator. The difference isn't that these industries are more cautious by temperament — it's that the cost of an unreviewed model decision is measured in more than embarrassment.
Good governance in these settings has a few consistent features. Every model recommendation is explainable in terms a non-technical reviewer can evaluate, not just accurate. Every decision that affects a customer has a human checkpoint before it ships, not after a complaint arrives. And every change to a model or its inputs is logged in a way that survives an audit, not just a Slack thread someone remembers writing.
None of this is exotic. It's the same discipline regulated industries already apply to underwriting decisions or clinical protocols — it just hasn't caught up to how AI projects usually get built, which tends to start with a prototype and add governance later, if at all.
The organizations that move fastest on AI in regulated industries aren't the ones who ignore this. They're the ones who build the review checkpoint into the workflow from the first pilot, so it's not a retrofit six months in when legal finally asks to see the model.