Where human review belongs in AI workflows: designing review points in advance

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Human review works best when it is built into an AI workflow from the start rather than added after a system error occurs. The workflow should specify which cases require review, what the reviewer sees, and how processing resumes after a decision. Review triggers can include missing information, unsupported languages, ambiguous categories, conflicting validation rules, or actions with significant consequences. The article advises against relying solely on a model's confidence score unless it has been calibrated for the task, and recommends showing the reviewer the original input, the proposed result, the relevant rule, and the reason for the pause. Recording reviewer decisions, using stable identifiers to avoid duplicates, and tracking review volume and unresolved cases are also recommended.

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