Assign human review roles before automation

What this solves

AI-assisted work stalls or becomes risky because nobody knows who reviews outputs, handles exceptions, fixes mistakes, and maintains the source material.

• Review happens informally or only when someone is nervous.
• People assume someone else is checking AI outputs before they reach customers, staff, or systems.
• Exceptions bounce between roles because approver, escalation owner, and maintainer are not named.

What AI is good at here

• Mapping review points from workflow steps and identifying where errors would matter.
• Drafting role descriptions for output approver, exception owner, source owner, workflow maintainer, and incident owner.
• Summarizing what each reviewer should check: source, facts, tone, policy, risk, system destination, and customer promise.

What AI should not own

• Humans retain final accountability for approvals, exceptions, source truth, and customer impact.
• AI should not approve its own output, resolve review disputes, or decide when a workflow is safe to loosen.

Best first implementation

• List the points where an AI output could affect a customer, patient, guest, learner, job, quote, refund, report, team member, or financial result.
• Assign one accountable reviewer to each point and state what they must check before approval.
• Define how corrections feed back into prompt rules, source material, templates, and training examples.

What good looks like

• Outputs move faster because approval ownership and review standards are clear.
• Exceptions have a known path instead of escalating to the owner by default.
• The team trusts AI assistance because accountability is designed, not implied.

Common mistakes

• Adding human review without saying what the human checks or what a failed review looks like.
• Making the owner review everything and creating a new bottleneck instead of assigning role-based review.