“Human in the loop” appears in almost every AI proposal. Too often it means nothing more than “someone will keep an eye on it”. Real oversight is designed, staffed and measured like any other part of the process.
Decide the level of oversight
Not every task needs the same control. Three common levels work well:
- Suggest: the system recommends, a person decides every case. Right for consequential decisions such as approvals, clinical or financial judgements.
- Act and sample: the system acts, people review a regular sample and all flagged cases. Right for high-volume, moderate-risk work.
- Act and escalate: the system acts, people handle only the exceptions it cannot resolve. Right for low-risk, well-understood tasks.
Name the people
For each workflow, write down who reviews, who can override and who owns the outcome. If that list is empty, the oversight is imaginary.
Make overriding easy and visible
Reviewers need to see why the system reached its answer and correct it in one step. Every override should be recorded, because overrides are the best signal of where the system is weak.
Keep a manual path
Every critical automation needs a documented fallback: how the work gets done if the system is unavailable or produces doubtful results. Test it, just as you would test a backup.
Measure oversight itself
Track review backlog, override rate, time to resolve exceptions and incidents. Rising overrides suggest the system needs attention; falling reviews may mean people have stopped looking.
Oversight is not a brake on automation. It is what allows an organisation to trust automation with more over time, because the evidence shows it deserves that trust.