Deciding Which AI Outputs Need a Second Human and Which Do Not

Someone has asked you which AI outputs need checking. The honest answer is "not all of them", but saying that without a method sounds like carelessness.

Score each output type on three things: how reversible it is, how visible it is outside the team, and how much money is attached. High on any one of the three means a second human. Low on all three means let it through and sample it later. That is the whole method, and it holds up better than a policy that says everything gets reviewed.

Why review everything fails

A blanket review rule feels safe and behaves badly. Reviewers get buried in low stakes items, approval becomes a reflex, and by the time something genuinely risky arrives the habit of clicking through is already set.

Worse, the team learns that review is a formality. Once that belief takes hold you cannot get it back by adding another approval step. You get it back by making review rare enough to be meaningful, and by writing the approval step so people actually read it.

So the goal is not more review. It is review landing on the right things.

The three scores

Rate each one low, medium or high. Do it for a category of output, not for individual items.

Reversibility. How hard is it to undo? A draft that sits in a folder is fully reversible. A message already read by a client is not. A deleted record with no backup is the worst case. Ask specifically: if this is wrong and we notice in an hour, what does the fix cost? If we notice in a week?

External visibility. Who sees it outside the team that produced it? Internal notes are low. A board summary is medium, because it shapes decisions by people who cannot check the source. Anything reaching a customer, a regulator or the public is high, because correcting it is itself an event.

Money at stake. Not the value of the whole process, the value of this single output being wrong. A mis categorised expense is small. A pricing line in a quote is not.

Reading the scores

One high on any dimension means a second human before it ships. Two mediums usually means the same. All low means release and check by sample. Stop at one extra reviewer, because two approvers are sometimes worse than one.

Resist the urge to average. Averaging is how an irreversible action with a small money value gets waved through. The point of scoring three separately is that any one of them can be decisive on its own.

Where the score sits on a boundary, look at frequency. Something borderline that happens twice a year can be reviewed every time at almost no cost. Something borderline that happens two hundred times a day cannot, and needs either a real reduction in scope or a sampling scheme with teeth.

Score the action, not the confidence

A tempting shortcut is to route outputs for review based on how confident the model appears to be. It is worth using as an extra signal, and it is a bad primary rule, even when you have gone to the trouble of setting the threshold from your own rejection data.

Confidence tells you about the model's internal state. It tells you nothing about what happens in the world if the output is wrong. A confidently wrong price is still a wrong price. Score the consequence first, then use confidence to decide which of the low risk items to sample.

Where the sheet lives

The scoring sheet is only useful if it is attached to the workflow rather than filed somewhere. In practice that means each automated workflow carries its three scores and its resulting rule, visible to the person who owns it.

This is easier when project work, people records and agents sit on the same set of records, which is how SicherOne is put together. A human approves agent output before it ships, and the question of which outputs need that approval becomes a setting on the workflow rather than an argument in a meeting.

Re score when the work changes

The most common failure is not a bad initial score. It is a good score that nobody revisited.

A workflow that started as internal drafts now feeds a customer facing page. A summary that went to one manager now goes to the board. Nothing about the AI changed, but external visibility moved from low to high and the review rule should have moved with it.

Put a re score on the calendar when a workflow changes audience, changes volume by a large factor, or changes the type of record it touches. Those three triggers catch most of the drift.

A short version to hand around

If you need one paragraph for a policy document: outputs that are hard to undo, visible outside the team, or attached to money get a named human approver before release. Everything else is released and sampled, with the sample weighted towards new and unusual cases. Scores are recorded on the workflow and reviewed whenever the audience, volume or record type changes.

That is defensible, it fits on a page, and it puts your reviewers' attention where the damage would actually be.