One Concept, One Example, One Question

The concept

A handoff is risky when it does not say what has been assumed, checked, and still needs deciding.

That might be an AI-generated draft that assumes the sources are strong enough, a reading queue that assumes saved items will later become useful or a technical job that assumes the production environment has the same capacity as the place where it was tested.

The problem is not always that the assumption is wrong. The problem is that the next person cannot see it clearly enough at the moment they need to decide what to do next.

The principle is simple:

Make the assumption visible where the next person, system, or future version of you can inspect it.

If the assumption is weak, the work may need a checkpoint. If it is strong, it can usually move with more confidence. Either way, the handoff becomes more honest.

The example

Imagine a team uses AI to draft a short options paper. The first version looks plausible, and the structure is helpful, but it quietly assumes three things:

• the source material is complete enough;
• the recommendation criteria are agreed;
• and the reviewer has time to check the evidence before the paper is shared.

If those assumptions stay hidden, the draft can reach the next stage as though the underlying work had already been checked. A senior reviewer may then inherit the uncertainty late, when the work is harder to reshape and the cost of correction is higher.

A better handoff does not need a heavy governance process. It could simply say: "This draft assumes the source pack is complete, the decision criteria are cost, delivery risk, and operational impact, and the unresolved point is whether the proposed option creates extra support burden."

That short note changes the work. The reviewer is no longer only judging whether the prose sounds credible. They can test the assumptions that hold the recommendation up.

The question

Ask yourself:

Which assumption would be most expensive if it turned out to be wrong?

That question is useful because it moves review away from general confidence and toward the specific point where the work might fail. Sometimes the answer will justify a deeper check. Sometimes it will show that the task is safe enough to keep moving. The value is in making that judgement before the assumption has been hidden inside a polished output.

Related

• I explored the issue of what happens after an issue becomes visible in my article Data Quality Is an Ownership Problem: visibility helps, but someone still needs the authority and responsibility to act on it.

Elsewhere this week…

• I explore why a useful AI collaborator needs a stop button: it should be able to challenge, abstain, expose uncertainty, and finish cleanly rather than always producing the next action.

  A Good AI Collaborator Needs a Stop Button

• I also published Applying the Practical Delegation Test, a shorter practitioner guide for deciding what to delegate to AI, what still needs human judgement, and where residual uncertainty should stay visible.

• A SecondBrain Framework update adds Markdown-first Marp slide-deck support, including validation and managed PDF, PowerPoint, and speaker-note exports. It also separates public-writing and internal-document voice guidance, improves DOCX export, and makes Daily Note rollover links more reliable.

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