AI & Technical question
The team is considering an AI-powered internal tool for Finance or User Operations. Pick one high-value workflow and explain how you would determine whether it is a good AI use case, define the product requirements, and design an evaluation plan. What failure modes would you expect, and what guardrails, human-review steps, or fallback mechanisms would you put in place before launch?
- OpenAI
- AI & Technical
- Hard
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What this question tests
Whether you can scope an internal AI use case rigorously (not just enthusiasm for AI) and build the eval and guardrail plan that makes it safe to ship.
How to approach it
- Pick one workflow and say why, for example Finance's vendor-invoice anomaly review: high volume, repetitive, rule-plus-judgment, and past decisions are logged.
- Check AI fit: enough labeled history to eval against, errors are correctable (route to a human, not a one-way action), and latency tolerance allows a review step.
- Define requirements: inputs (invoice, PO, vendor history), output (flag plus rationale and confidence), a reviewer queue, and an audit trail for Finance compliance.
- Build the eval set from real past decisions and measure precision and recall on flags, tuned separately by risk tier.
- Launch human-in-the-loop only: every AI flag goes to a reviewer, and track override rate as a live accuracy signal.
- Expand autonomy in stages, gated by sustained precision on the highest-risk tier, not by a single offline score.
What a strong answer includes
- Names a specific workflow and volume, for example assume 500 invoices a week reviewed manually today.
- Treats false negatives (missed fraud or duplicate payment) as more costly than false positives, so sets the auto-approve threshold conservatively, for example 98% precision assumption before removing review.
- Separates 'flag for review' (low risk to automate) from 'auto-resolve' (high bar) as two different launch gates.
- Includes an explicit fallback: low-confidence cases always route to a human, never guessed.
Common mistakes
- Jumping to 'use AI' without checking there is enough labeled history to build a real eval set.
- No fallback path when the model is uncertain, so it silently guesses on edge cases.
- Skipping the audit trail, which Finance and User Ops both need for compliance and dispute resolution.
Likely follow-up questions
- How would you build the labeled dataset if none exists today?
- What would change your mind about giving the model more autonomy?
- How do you handle a case where the model is confident but wrong?
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More questions from OpenAI
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture