Strategy question
After several flagship enterprise deployments, you notice each customer asks for different custom logic, integrations, and operating processes. How would you distinguish among (a) one-off account work, (b) reusable deployment playbooks, and (c) core product investments, and how would you feed those decisions into Product and Engineering so future deployments get faster without overfitting to a single customer?
- Decagon
- Strategy
- Hard
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What this question tests
Tests distinguishing one-off account work, reusable playbooks, and core product investments across flagship deployments, and building a feedback loop into Product and Engineering.
How to approach it
- Classify each customer request by asking: is this integration or logic tied to a unique legacy system (account-specific), a repeatable pattern of how deployments get configured (playbook), or a genuine product capability gap (core investment).
- Tag every account-specific solution during delivery, and review the tags quarterly to spot patterns of requests that keep recurring across different customers.
- Convert recurring account-specific patterns into documented playbooks, standard configuration templates and rollout sequences, so future deployments move faster without needing new product work.
- Escalate patterns that appear across three or more accounts, or that a playbook can't sufficiently solve, into Product and Engineering as core roadmap candidates.
- Build a lightweight recurring forum, for example a monthly deployment retro, where delivery teams surface patterns directly to Product rather than relying on ad hoc escalation.
- Track a metric, like average time-to-go-live per new deployment, to prove the playbook and productization loop is actually making deployments faster over time.
What a strong answer includes
- Defines a clear three-way test, unique legacy system, repeatable pattern, or genuine capability gap, to classify every request instead of judgment calls case by case.
- Builds an explicit feedback loop, tagging and quarterly review, so account-specific work systematically surfaces candidates for playbooks or product investment.
- Sets a concrete escalation trigger, three or more accounts requesting something similar, to avoid over-productizing single-customer quirks.
- Proposes a measurable outcome, faster time-to-go-live, to prove the classification system is actually reducing overfitting to individual customers.
Common mistakes
- Treating every custom request as one-off without ever revisiting patterns that recur across accounts.
- Escalating every recurring request straight to core product without first trying a lighter-weight playbook.
- Building the classification system with no forum or process for delivery teams to actually feed insights back.
Likely follow-up questions
- How would you decide between a playbook and a full product investment for the same pattern?
- What would you do if delivery teams stop surfacing patterns because the forum feels like overhead?
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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