Strategy question
You are evaluating several AI application opportunities across government-backed entities in one region, but the team can only pursue one first. What framework would you use to compare them and make a recommendation, balancing user pain, deployment complexity, data readiness, likelihood of measurable impact, and revenue potential?
- Scale AI
- Strategy
- Hard
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What this question tests
Whether you can build a repeatable comparison framework across multiple government AI opportunities and use it to make a clear, defensible recommendation under real resource constraints.
How to approach it
- List each opportunity against five explicit dimensions: user pain severity, deployment complexity, data readiness, likelihood of measurable impact within a reasonable timeframe, and revenue potential.
- Weight data readiness and likelihood of measurable impact most heavily early on, since a technically difficult but readable opportunity produces a reference case faster than a high-revenue but data-poor one.
- Score each opportunity on the five dimensions using available evidence, being explicit about which scores are confident versus assumptions that need validation.
- Recommend the opportunity with the strongest combination of readiness and measurable impact, even if it is not the highest revenue option, and explain the tradeoff clearly to stakeholders who may be revenue-focused.
- Propose a follow-up validation step for the top pick's shakiest assumption before fully committing resources, rather than treating the recommendation as final.
What a strong answer includes
- Uses named, weighted dimensions instead of an unweighted checklist, and explains why readiness and measurable impact are weighted heavily for a first engagement.
- Distinguishes confident scores from assumptions, which shows honest reasoning under real uncertainty rather than false precision.
- Recommends validating the riskiest assumption before full commitment, treating the recommendation as a starting point, not a final answer.
Common mistakes
- Picks the highest-revenue opportunity by default without weighing deployment complexity or data readiness.
- Presents scores with false confidence, with no acknowledgment of which inputs are assumptions.
Likely follow-up questions
- How would you validate the riskiest assumption in your top recommendation.
- What would change your recommendation if the region's political priorities shifted.
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More questions from Scale AI
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