Strategy Questions Scoring Rubric

Strategy questions are scored on four dimensions: Business Acumen, AI Realism, Quantitative Reasoning, and Strategic Communication. The weighting reflects that strategy questions are fundamentally about business judgment applied to AI, not about AI applied to business. The most common mistake is treating strategy questions like technical questions. An answer that is technically sophisticated but commercially naive will score poorly.

These questions are most common at Senior PM+ levels. At the Director level, strategy questions often account for 35% of the total evaluation weight.

This is the most heavily weighted dimension. It measures whether you understand the business context, competitive dynamics, and how AI investments connect to business outcomes.

Score 1: No business context. Discusses AI in a vacuum. Score 2: Mentions business context but superficially. 'This would help the company grow.' Score 3: Demonstrates clear understanding of the business model, competitive landscape, and how the AI investment creates value. Score 4: Same as 3, plus makes connections between AI capabilities and specific business outcomes (revenue growth, cost reduction, competitive moat). References real companies or market data to support reasoning. Score 5: All of the above, plus identifies non-obvious business implications. For example, how an AI investment changes the company's pricing power, affects talent acquisition, or shifts the competitive landscape.

Phrases that signal strong Business Acumen: 'At this company's stage, the priority is...' 'The competitive dynamics in this market mean...' 'This AI investment pays back through [specific mechanism].' Phrases that signal weakness: 'AI is the future.' 'Every company needs AI.' 'This would be good for the business.'

This dimension evaluates whether your AI strategy is grounded in reality. Can the proposed AI approach actually work? Is the timeline realistic? Are the data requirements feasible? Interviewers who have shipped AI products can immediately spot strategies that sound good in a slide deck but would fail in execution.

Score 1: Proposes AI capabilities that do not exist or wildly overstates what AI can do. Score 2: Proposes AI approaches that are theoretically possible but ignores practical constraints (data, talent, infrastructure). Score 3: Proposes realistic AI approaches with awareness of constraints. Discusses what is feasible with current technology vs. what requires research. Score 4: Same as 3, plus calibrates the AI approach to the organization's capabilities. A strategy for a 200-person company with no ML team should look very different from a strategy for Google. Score 5: Demonstrates deep practical knowledge. Discusses specific implementation challenges, realistic timelines, and tradeoffs between different AI approaches based on organizational context.

The most common failure on AI Realism is overpromising. Candidates who say 'we will build a custom LLM' for a company with 3 engineers lose credibility immediately. AI Realism means matching ambition to capability.

Dimension 3: Quantitative Reasoning (Weight: 25%). Strategy questions require numbers. Revenue impact, cost estimates, market sizes, ROI calculations. Score 3 requires reasonable estimates with stated assumptions. Score 4 requires a clear ROI or cost-benefit analysis. Score 5 requires sensitivity analysis: 'If our assumption about X is wrong by 2x, the conclusion still holds because...' The bar is not precision. It is the ability to quantify tradeoffs and make decisions based on rough but reasonable calculations.

Dimension 4: Strategic Communication (Weight: 15%). Strategy answers need to be communicated as a clear recommendation with supporting rationale, not as an analysis with no conclusion. Score 3 requires a clear recommendation. Score 4 requires a recommendation structured as a CEO/board-level pitch: problem, analysis, recommendation, risks, next steps. Score 5 requires the interviewer to think 'I would hire this person to present this to my board.'

The pass criteria for strategy questions: average score of 3.5+ with Business Acumen at least 3. A candidate who scores 5 on AI Realism but 2 on Business Acumen is flagged as 'technical but not strategic.' A candidate who scores 5 on Business Acumen but 2 on AI Realism is flagged as 'strategic but disconnected from AI reality.' Both profiles face an uphill battle in the hiring committee.