How to Review AI-Generated Business Work Before It Goes Out

Use a practical review rubric for AI drafts: check facts, completeness, permissions, usefulness, and the decision to approve, revise, or stop.

Review AI-generated business work against the original request, approved sources, and the consequences of getting it wrong. Check facts, completeness, authority, and usefulness before approving the output. A draft can read smoothly and still contain the wrong commitment, omit an exception, or answer a different question from the one asked.

At Parker Miami, I described AI as useful in the middle: a person defines what needs to happen, AI helps do the work, and a person checks the result. The practical application is to design both human handoffs. A vague request followed by a quick glance at the answer is not meaningful oversight.

Define the acceptance standard before drafting

Suppose a team needs a response to a customer's scheduling request. The brief should identify the actual question, available options, current policy, and who can approve a change. The reviewer needs that same information. Otherwise, they may judge the wording without noticing that the proposed option is unavailable.

Write down what must be true for the work to pass. For this hypothetical response, that might mean the date is correct, the option is allowed, the next step is clear, and no unsupported promise is made. Different work needs different criteria. Do not use one generic checklist to approve every business decision.

Use four review passes

• Facts: compare names, dates, numbers, and claims with the approved source. Treat a citation as something to inspect, not automatic proof.
• Completeness: check whether the output addresses the whole request and includes the relevant conditions or unresolved questions.
• Authority: confirm that the draft does not promise, disclose, approve, or change something outside the assigned role.
• Usefulness: ask whether the recipient can understand the answer and take the intended next step.

Those passes help separate different kinds of problems. A typo may need a quick correction. An incorrect policy or unauthorized commitment may require stopping the workflow and investigating the cause. Do not average a serious failure away because the other sections look good.

Choose approve, revise, or stop

Approve only when the result meets the defined standard. Revise when the work is recoverable and the reviewer can explain the correction. Stop when the inputs are unreliable, the request is outside scope, or the reviewer lacks the information or competence to evaluate the result. Escalation is a valid outcome, not a failure to use the tool.

In the scheduling example, a missing greeting is a revision. Two conflicting records about availability require clarification before sending. A request to disclose someone else's booking details should follow the organization's escalation policy. The choice depends on the consequence, not on how confident the text sounds.

Give feedback that can improve the process

Replace make this better with a specific observation: the draft offers Friday, but the approved calendar shows no Friday availability. Record the error type and the relevant source. Then ask whether the problem began with the brief, outdated context, retrieval, or the review process itself.

Keep a small set of permitted examples that represent ordinary cases and important exceptions. Recheck them when the process changes. Anthropic's effective-agents guidance emphasizes evaluating outcomes, human checkpoints, and stopping conditions. A review routine makes those ideas concrete for the people responsible for the finished work.

Technical reference: Anthropic on effective agents and checkpoints

Measure the whole job

Track acceptable outputs, review time, correction effort, and escalations together. If drafting becomes faster but reviewing becomes slower, the business may not have improved. If reviewers routinely approve without opening the source, reduce their workload or redesign the handoff so the necessary evidence is visible.

The aim is dependable work with accountable people. This guide applies the human-at-the-beginning-and-end principle from my Parker Miami presentation. The rubric and scheduling examples are practical extensions, not evidence that a particular tool is safe for every task.

Set broader ownership with the AI governance checklist

Try a bounded client meeting brief