evidence

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.

Models sometimes describe actions they never took. Let the system decide what is done from checks it runs itself, and show users the evidence.

Rank surviving signals by what they actually prove, decide when to pause exposure and reconcile missing events without inventing outcomes.

Synthetic personas are cheap and fast, and they tell you what a model thinks people might say. The interviewer wants a clear line between useful applications and evidence they cannot provide.

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.

A portfolio process question. Prototypes make AI ideas cheap to test; the challenge is turning a stream of demos into consistent, evidence-based funding decisions.

Six sessions find usability problems; they cannot estimate output quality. Fix the tasks, log every output, separate interface, quality and latency findings, and claim only what six people can show.

Turn conflicting pilot evidence into a recommendation an executive can approve. Show the decision, the rejected alternative, the workload cost and the evidence that would change your mind.

Make a past project inspectable: show the decision, your contribution and the limits of the result. Prepare for a reviewer who challenges both the metric and the design.

Bring one defensible workflow, show the review burden and explain a mistake that changed how you use the tool.