Where human approval belongs in an AI workflow

Review consequences, not just confidence

An AI system can sound certain while being wrong. Approval decisions should therefore depend on what an action changes, who is affected and how easily it can be reversed. A draft internal summary and a payment instruction deserve different controls even if both are produced by the same model.

List the proposed actions before building the interface. Separate reading information, preparing a recommendation, changing an internal record and communicating outside the organisation. Give each action a named owner and a clear permission rule.

Make approval an informed decision

A useful approval screen shows the proposed action, supporting evidence, affected records and any missing information. Reviewers need enough context to challenge the recommendation. A button labelled “Approve” is not meaningful oversight if the person cannot see what will happen next.

Avoid presenting every decision as urgent. If the queue constantly interrupts people with low-value requests, they may approve without checking. Reserve mandatory review for actions that need it, and use sampling or routine checks where the consequences justify that approach.

Test the difficult cases

Try a duplicate request, contradictory documents, a changed bank detail, an unavailable system and a user asking the assistant to ignore its normal process. Check that the workflow stops or escalates correctly. Treat text supplied by customers and retrieved documents as untrusted inputs, not as permission to perform actions.

Design a workable queue

• Show the reviewer what changed since the previous step.
• Let them reject with a reason and correct the request.
• Set an escalation route for unattended requests.
• Record approval alongside the eventual action.
• Ensure rejecting a request does not quietly trigger it elsewhere.

The aim is a system in which responsibility remains clear. Vida can help map approval boundaries during discovery and include failure cases in acceptance testing. Begin with a small workflow and adjust the review process using observed errors, overrides and handling time rather than the apparent fluency of generated responses.