Strategy question
A combatant-command planning team asks for custom workflow support in Scale’s military planning product for part of the Joint Planning Process, but platform engineering believes it is a one-off. How would you determine whether the underlying issue is a durable product gap, an integration or data-model problem, or a local change-management issue? What evidence would you gather, who would you involve, and how would you decide whether to build, configure, or say no?
- Scale AI
- Strategy
- Hard
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What this question tests
Whether you can distinguish a durable product gap from a one-off request using real evidence and cross-functional judgment.
How to approach it
- Gather evidence beyond the one team's ask: check if other planning teams have raised similar needs, and whether the request maps to a documented step in the Joint Planning Process.
- Separate three hypotheses: a durable product gap, an integration or data-model limitation, or local change management (the team has not adopted existing functionality).
- Involve the right people per hypothesis: other forward-deployed PMs for demand evidence, platform engineering for feasibility, and the requesting team's leadership for adoption context.
- Check usage data if available, for example whether the team tried and abandoned an existing feature, pointing to change management over a true gap.
- Decide with the evidence: build only if multiple teams show the same durable, technical need; configure if the data model supports it; say no or redirect to training for a local adoption issue.
- Communicate the decision and evidence back to both the requesting team and platform engineering.
What a strong answer includes
- Names the three hypotheses explicitly and uses a different investigative method for each, not one undifferentiated question.
- Uses cross-team demand signals as the deciding evidence, the right lens for a platform serving multiple commands.
- Considers change management and training as a real outcome, not just build or configure, showing balanced judgment.
- Closes with transparent communication of evidence and decision to both sides, important for military and engineering trust.
Common mistakes
- Deciding based only on the one team's insistence without checking for a broader pattern.
- Treating build versus no as the only two options, ignoring configuration or training.
- Not naming who would gather evidence for each hypothesis.
Likely follow-up questions
- How would you validate demand without over-indexing on a self-selected, more vocal command?
- What would you do if platform engineering strongly disagrees the request is durable?
- How would you handle a command that pushes back on a no decision?
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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