prioritisation

Standard prioritisation frameworks miss the thing that makes AI ideas risky: nobody knows yet whether the model can do it reliably. The strong answer prices that uncertainty in.

A portfolio question. Spreading money evenly guarantees mediocre results; betting everything on one idea risks it all. The interviewer wants a portfolio with evidence gates, staged funding and kill criteria.

The strongest first move costs nothing and involves no tooling: read the traces. What you do with the notes, and in what order, is the rest of the answer.

A portfolio and stakeholder question. Across-the-board cuts are the easy answer and the wrong one. The strong answer finds where spend creates value, cuts waste first, and gives the CFO a number they can hold you to.

A resource allocation question with no painless answer. The interviewer wants a transparent framework, efficiency gains before rationing, and decisions leadership can stand behind.

A working prototype is the start of the decision, not the end. The interviewer wants evidence on value, feasibility at real quality, cost, risk and strategic fit, and a decision rule set before the evidence arrives.

Stopping work is one of the hardest PM decisions, and AI projects need it often because feasibility is uncertain. Panels want evidence, criteria, timing and care for the team.

Matching fast can cost more than being second. Show how you found out whether users would switch, what you shipped and on what quality bar, and how you held the line on the eval.