Strategy question
Three strategic accounts, two Fortune 100 enterprises and one government customer, are independently requesting similar workflow capabilities, but each needs different approval logic, security constraints, and reporting. How would you decide whether to keep solving these as account-specific implementations or invest in a generalized core product capability? Walk through the criteria, the data you’d gather, and how you’d make the call under roadmap and capacity constraints.
- Scale AI
- Strategy
- Hard
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
Start a mock interview on this question · Mock interview from a job description
What this question tests
Whether you can decide between account-specific implementations and a generalized core capability using real evidence under genuine capacity constraints.
How to approach it
- Confirm the underlying workflow need is genuinely shared across the three accounts, separate from their different approval logic, security constraints, and reporting needs, which may just be configuration variance on top of a common core.
- Design the core capability to separate the shared workflow logic from the account-specific configuration layer, for example a common approval-workflow engine with configurable rules per account.
- Gather the data needed to decide: how many other current or prospective accounts (beyond these three) would likely need similar configurable capability, since that changes the reach calculation significantly.
- Weigh the criteria: three strategic accounts (two Fortune 100, one government) represent high-value reach, but building a generalized capability takes real capacity away from other roadmap commitments.
- Decide with capacity constraints named explicitly: if capacity allows, build the configurable core capability now, since the reach and strategic value justify it; if not, build one clean implementation first as a reference architecture, and generalize once capacity frees up.
- Communicate the sequencing decision, and its reasoning, to all three accounts so they understand the plan even if their specific solution ships in stages.
What a strong answer includes
- Separates the shared workflow core from account-specific configuration, the actual architectural insight that resolves the false binary between account-specific and generalized.
- Uses reach beyond the three named accounts as a key data point, avoiding a decision based only on the visible three.
- Names capacity constraints explicitly as part of the decision, rather than assuming unlimited resources.
- Proposes a staged path, one clean implementation as a reference architecture first, if capacity does not allow full generalization immediately.
Common mistakes
- Treating account-specific and generalized as a strict either-or without considering a shared-core-plus-configuration architecture.
- Ignoring capacity constraints, proposing a full generalized build as if resources were unlimited.
- No data on reach beyond the three named accounts to justify generalization.
Likely follow-up questions
- How would you validate reach beyond these three accounts before committing to a generalized build?
- What would you do if the government account's security constraints cannot fit the shared architecture at all?
- How would you sequence which account gets the first implementation?
More strategy questions
- A Fortune 500 customer asks Scale to build a GenAI copilot on proprietary data, but the executive sponsor is split between sales enablement, advisor workflow, and business intelligence. In your first 2-3 weeks, how would you identify the highest-value wedge, quantify the opportunity, and turn that into a product strategy and phased roadmap both the customer and Scale can commit to?Scale AI · Strategy · Hard
- You've shipped a bespoke Text2SQL workflow for one large customer, and leadership wants to know whether it should become a repeatable product. What criteria would you use to decide which components should be standardized into reusable software, which should stay configurable, and which should remain fully custom?Scale AI · Strategy · Hard
- You own pay and incentives for Scale's global contributor marketplace. How would you design a compensation and incentive system that improves fill rates for scarce skills while protecting gross margin and data quality? Include how you'd segment contributors, set base pay versus bonuses, and guard against gaming or unintended quality regressions.Scale AI · Strategy · Hard
- During task construction, Scale may uncover live vulnerabilities or handle sensitive offensive artifacts. How would you design the responsible-development and release process for this portfolio, including containment, coordinated disclosure, access controls, artifact handling, and customer vetting? Where would you set hard launch gates versus case-by-case exceptions?Scale AI · Strategy · Hard
- Scale is standing up a net-new cybersecurity portfolio. How would you choose the first 2-3 capabilities to launch across vulnerability discovery, exploit reproduction, patch validation, secure code review, malware analysis, and incident triage? Walk through the prioritization framework you would use, including customer value, execution difficulty, benchmark credibility, and dual-use risk, and explain what you would explicitly defer from v1.Scale AI · Strategy · Hard
- Two near-term customer commitments pull the platform in different directions: one requires stronger auth and secure-by-default deployment into a constrained environment, while another needs better agent runtime primitives to improve forward-deployed team velocity. Engineering capacity is fixed and both asks are only partially specified. How would you sequence the work, what framework would you use to make the call, and how would you explain that decision differently to platform engineers, FD PMs, and executives?Scale AI · Strategy · Hard
More questions from Scale AI
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovering and Sizing AI Opportunities
- Chapter 9: Measuring AI Product Economics
- Chapter 14: Interviewing for AI PM Roles