Responsible AI in Insurance Requires More Than a Policy Statement
Most firms have a policy. Very few have a governance model that actually tells you what to do when you're deploying something.
A lot of insurance firms now have an AI policy. Far fewer have a real governance model. I've seen this gap firsthand, and it's a bigger deal than most people realize.
Policy language tends to be abstract. Deployment decisions are painfully specific. The hard questions aren't "Do we believe in responsible AI?" They're more like: Which use cases are low risk? Which require human review? What needs logging? What happens when the model is uncertain? Who owns the escalation path when something breaks?
If those questions don't have operational answers, governance is mostly ceremonial.
Insurance has particular reasons to care about this. It's a regulated industry. It handles sensitive information. It supports decisions that can influence client outcomes, claims handling, underwriting interpretation, and risk transfer. Even when an AI tool sits one layer removed from the formal decision, it can still shape judgment in ways that matter. People overtrust fluent systems - that's just human nature.
A useful governance model starts with use cases, not models. The approach I've found most useful is classifying workflows by risk tier. Low risk might be internal meeting note cleanup or basic summarization. Moderate risk might be renewal prep support or document extraction feeding a human-reviewed process. Higher risk is anything that materially shapes coverage interpretation, claims guidance, or external advice. And some things just shouldn't be automated.
Once the use case is classified, the operating requirements get clearer. What review level is needed? Does the user need to see sources? Can the output be client-facing? What data is allowed? What logging has to exist? What are the most important failure modes?
This sounds procedural, and it is. But it's also strategic. Good governance lets you move faster where risk is low and more carefully where downside is real. Without that structure, firms either freeze useful experimentation or adopt tools with weak controls. Neither outcome is good.
I think most insurance teams need four things.
Human accountability. Someone has to own the outcome, not just the tool.
Traceability. If the output influenced a decision, you should be able to reconstruct the inputs, the model's behavior, and the review path at a practical level.
Role clarity. Users need to know whether the system is summarizing, drafting, flagging, or recommending. Ambiguity here is genuinely dangerous.
Escalation. Any governance model that doesn't specify what happens when the workflow fails is incomplete.
The mistake I see most often is treating governance as a compliance layer you add after the use case is built. In practice, governance should shape the workflow itself - defining how the task is bounded, how review works, and where the system isn't allowed to operate.
Bottom line: if the controls are vague, the deployment isn't ready. Real credibility comes from operating discipline, not from having a policy on the intranet.