Strategy question
After several custom healthcare deployments, Decagon sees repeated patterns in benefits verification, scheduling, escalation rules, and compliance reviews. How would you decide which pieces should become reusable healthcare product or playbook assets versus remain customer-specific, and how should that decision feed the broader healthcare roadmap?
- Decagon
- Strategy
- Hard
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What this question tests
Whether you can turn recurring patterns from custom deployments into a deliberate build-versus-keep-custom decision, using a repeatable framework rather than generalizing prematurely from a small sample.
How to approach it
- Require evidence the pattern genuinely repeats across independent healthcare deployments, not just recurring within one large account, since a pattern specific to one customer's workflow is not yet a platform candidate.
- Check for structural similarity, not just topical similarity: benefits verification patterns across two hospital systems might look similar on the surface but differ enough in underlying data and payer rules to resist generalization.
- For patterns that repeat and are structurally similar, for example a common escalation-rules shape, build a reusable playbook asset, a configurable template rather than a fully rigid product feature, preserving some flexibility for healthcare-specific variation.
- For patterns still varying meaningfully by customer, like compliance review steps that differ by state or payer, keep customer-specific for now and track them for future reconsideration.
- Feed the decision back into the roadmap by prioritizing playbook-asset investment for the highest-repetition, highest-structural-similarity patterns first, since those generalize with the least risk of forcing a wrong abstraction.
What a strong answer includes
- Distinguishes topical similarity from structural similarity, a specific and important distinction for healthcare workflows that look alike on the surface but differ in underlying rules.
- Proposes a configurable playbook asset rather than a rigid feature, preserving needed flexibility for a domain with real regulatory variation.
- Ties the roadmap prioritization to repetition and structural similarity together, avoiding premature generalization from a small or superficially similar sample.
Common mistakes
- Generalizes into a reusable feature after seeing the pattern at just one or two customers, without checking structural similarity.
- Builds a rigid product feature instead of a flexible playbook asset, ignoring real regulatory variation across healthcare customers.
Likely follow-up questions
- How would you handle a playbook asset that works for most customers but fails badly for one state's regulations.
- What evidence would you need before generalizing a scheduling pattern specifically.
More strategy questions
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- Decagon can support enterprise deployments through native integrations, customer-built API/SDK integrations, or partner-built extensions. How would you prioritize which systems and deployment patterns to productize first? Walk through the decision framework you’d use, including customer demand, implementation cost, reusability across accounts, security/compliance blockers, and impact on time-to-launch.Decagon · Strategy · Hard
More questions from Decagon
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