Where human approval belongs in an AI workflow

Place a decision in front of a person when an action changes a commitment, exposes sensitive information, spends money or is difficult to undo. The review should happen while the action can still be stopped. An email telling a manager that a customer has already received a concession is an audit notice, not an approval.

Design the screen around the decision

Imagine an agent that drafts replacement orders for damaged goods. Show the reviewer the original claim, order number, proposed replacement, destination address and reason the case qualifies. Highlight differences between the original and proposed shipping address. The reviewer needs enough evidence to say yes, correct the draft or decline without opening six applications.

Tie approval to that specific version. If the customer changes the address after the supervisor approves the replacement, ask for a new approval before dispatch. Save who approved, what was approved and when. A stored checkbox without a version can leave a later reviewer uncertain about which action was authorized.

Decide who owns silence

Give the approval request an owner, a backup and an expiry. During an ordinary shift, a branch supervisor might own requests. Outside that shift, cases could remain queued until the next business day. Define urgent exceptions with the people responsible. Silence should leave the case in a known state.

Try three cases during the pilot: approval, refusal and no response. Check that refusal leads to a clear next action and a late click cannot approve a superseded request. Measure how long reviewers spend and how often they correct drafts. If people routinely approve without reading because the queue is too large, narrow the work assigned to the agent or give reviewers better evidence before expanding the rollout.

Put this into practice

Bring a sample of the work and the tools your team uses. We can help define a first project, who reviews it, and how to tell if it works.

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