Scope — What May This Touch?
The first discipline: what an AI-augmented workflow may touch, may not touch, and must escalate. Five chapters.
5. What should not be delegated
The skill most AI training omits entirely.
Some problems resist definitive formulation: the problem statement changes as you work on it, there is no stopping rule, and solutions are better-or-worse rather than true-or-false. Rittel and Webber called these wicked problems, and the point for an operator is blunt — a problem that cannot be specified cannot be delegated to anyone, human or machine.
| Category | The test |
|---|---|
| Ambiguity | The problem statement changes as you work on it — wicked, not merely hard |
| Ethical risk | The decision requires accountability that cannot be delegated |
| Regulatory constraint | A human decision-maker is legally required |
| Insufficient context quality | The inputs cannot support a reliable output, however capable the model |
The fourth is the one practitioners get wrong most often, because it is the only one that looks like a model problem and is not. Bad output from thin context is not a capability failure. Adding a better model to insufficient context produces a more fluent wrong answer.
The Four Questions
Before asking AI to do anything, you should be able to answer:
- What is the intended outcome?
- What constraints must be respected?
- What does success actually look like?
- How will the results be validated?
If these cannot be answered first, the issue is not AI risk. It is lack of governance — and no scope statement written afterward will recover it.
Least privilege limits the blast radius of ordinary error — not just malice.