Adding a person to an AI process sounds reassuring. But a person who is rushed, uninformed or unable to challenge the output is not much of a safeguard.
Make the review specific.
Decide what needs checking. Is it factual accuracy? Missing information? The effect on a customer? Different risks call for different expertise and evidence.
Give the reviewer useful context.
They should be able to see the original request, relevant source material and known limitations. A polished final answer on its own can hide the assumptions that produced it.
Give them permission to say no.
Oversight must include the authority to reject an output or pause the process. If the system carries on regardless, the review is a ceremony rather than a control.
Match the effort to the consequences.
A rough list of brainstorming ideas does not need the same review as a decision affecting someone’s money, employment or access to a service. Higher-stakes uses need appropriately qualified oversight.
Responsibility remains with the people and organisations using the result. “The AI said so” is an explanation of the workflow, not a defence of the decision.