Who should do the human review, the rep or someone else?

For most outputs, the rep who owns the account. They have context to catch subtle errors. For compliance-heavy outputs, RevOps or legal. For brand content, marketing. Match reviewer to risk.

How much time does review add per message?

15 to 60 seconds per message for per-item review. Batch review drops this to 5 to 10 seconds per message average because the reviewer scans for patterns.

Can AI review its own output?

AI self-review catches some errors but is not a substitute for human review on high-stakes outputs. Useful as a pre-filter layer, flagging likely problems for human prioritization.

What happens when an AI output is rejected by the reviewer?

Message gets edited and sent, or rejected with a note that feeds future prompt iteration. Rejection rate is a leading indicator of model performance.

Human-in-the-Loop Controls: What Must Be Reviewed by a Human

Human-in-the-loop is a review step where a human approves, edits, or rejects an AI output before it reaches its destination. Review intensity should match output stakes.

Always review

Proposals, contract language, pricing communication, public social content, enterprise first-touch messages, and any output that cites specific data.

Can skip with monitoring

Second and third follow-ups in approved cadences, LinkedIn engagement on existing connections, internal research summaries, meeting briefs.

Scale

Batch review (50 to 100 per session), sampling for high volumes, rule-based pre-filters (profanity, price check, banned phrases) before human review.

Mistakes

Reviewing everything (fatigue, rubber stamp), reviewing nothing (incidents mount), reviewing inconsistently (false safety signal).