A useful AI project starts with the workflow
Before choosing a model, understand the task, the person doing it, and the decision that follows.
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A review button is a start. A useful review experience gives people evidence, context, and real control.
Put review at the consequential moment
Human oversight works best when it appears before an action that needs judgment. Drafting a reply and sending it are different steps. Extracting an invoice value and initiating a payment are different steps. Mapping these boundaries helps a team place review where it can meaningfully affect the outcome.
Give reviewers something they can verify
Asking someone to approve an unexplained output transfers the uncertainty to that person. Show the original input, the proposed result, the relevant evidence, and any unresolved questions. For extracted data, a link to the source passage is often more useful than a confidence score by itself.
Make correction straightforward
Reviewers need to reject, edit, or route a task without fighting the interface. Corrections should preserve the original proposal and record what changed. When a task cannot be completed, a clear escalation route prevents the review queue from becoming another place where work waits indefinitely.
Measure the quality of the whole process
Approval rates alone do not show whether oversight is effective. Look at the errors reviewers catch, the context they ask for, the time required to make a decision, and the issues discovered after approval. These signals help improve both the AI component and the workflow around it.
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