When a process moves from a person reading a document to a system surfacing an answer, oversight can quietly erode — not because anyone decided to remove it, but because the workflow no longer asks for it.
Design the checkpoint into the workflow, not around it. If reviewing an AI-assisted answer requires opening a separate tool or searching for the underlying document, most people will skip it under time pressure. Oversight that depends on extra effort tends to disappear.
Make responsibility explicit. Decide, in writing, which decisions a system can surface and which a licensed professional must approve — and who is accountable for the outcome either way. Ambiguity about who is responsible is where oversight actually breaks down.
Sample and audit, even when things seem to be working. Spot-check a percentage of AI-assisted work on a regular schedule, not only when something goes wrong. Problems that would show up at full scale are often visible earlier in a small, regular sample.
Watch for automation complacency. The better a system performs, the more tempting it is to stop checking its work carefully. Build review habits that don't depend on people staying suspicious of a tool that has been reliable so far.
Keep an escalation path that's actually used. Employees need a fast, low-friction way to flag a case that doesn't look right, and evidence that flagging it leads to a real review — otherwise the option exists on paper only.
Oversight is not a constraint on using AI well in insurance. It's what allows a team to use it with confidence, on higher-stakes work, over time.