Strategy question
OpenAI teams have conflicting asks: ChatGPT wants faster feature rollout, Monetization wants precise experiment reads, and Developer Platform wants stable integrations. How would you create a prioritization framework for Statsig that decides which requests become shared platform investments versus team-specific support? What criteria and decision process would you use?
- OpenAI
- Strategy
- Hard
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What this question tests
Whether you can design a prioritization framework for a shared platform under conflicting stakeholder demands, and articulate criteria rather than case-by-case judgment calls.
How to approach it
- Name the three conflicting needs concretely: ChatGPT wants speed, Monetization wants precise reads, Developer Platform wants stability, and note these pull the roadmap in different directions.
- Define criteria: reuse potential across teams, risk if unaddressed, and whether the request requires a platform capability versus a one-off configuration.
- Score each incoming request against those criteria in a lightweight rubric reviewed with the requesting team, so decisions are explainable, not ad hoc.
- Route high-reuse, high-risk requests to shared platform investment; route low-reuse requests to team-specific support with a documented workaround.
- Revisit the rubric quarterly with input from all three teams so the framework stays legitimate, not just Statsig's own priority list.
What a strong answer includes
- Gives concrete criteria (reuse, risk, platform versus config) instead of vague values like impact and effort.
- Shows how the framework resolves the specific three-way conflict named in the question, not a generic prioritization matrix.
- Builds in a review cycle so stakeholders trust the framework over time rather than seeing it as arbitrary.
Common mistakes
- Proposes a generic RICE or impact-effort matrix with no tie to the specific conflicting asks.
- No mechanism for stakeholders to see or contest how requests were scored.
Likely follow-up questions
- How would you handle a request that scores high on reuse but comes from a low-priority team.
- What happens when two teams disagree with how their request was scored.
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 14: Get the job: the AI PM interview loop