AI & Technical question
A Fortune 500 prospect wants a German-language support agent across chat and email, but the CFO and COO are skeptical about accuracy, brand risk, and time to value. Walk me through how you would scope a 6- to 8-week pilot: which workflows you would include or exclude, what data and integrations you would require, what offline and live evals you would run to prove German quality and brand safety, what launch guardrails and fallback paths you would set, what success criteria you would commit to, and how you would design the pilot so a win expands naturally into a broader CX transformation.
- Decagon
- AI & Technical
- Hard
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What this question tests
Ability to scope an enterprise pilot that proves value to skeptical executives while managing accuracy and brand risk.
How to approach it
- Scope to 6 to 8 weeks, including only high-volume, low-risk German workflows like order status, and excluding sensitive cases.
- Define required data: a German knowledge base, ticketing access, and enough historical tickets to build offline evals.
- Run offline evals on German accuracy and tone first, then live evals on a small traffic slice with human review sampling.
- Set guardrails: confidence thresholds for auto-resolution, escalation paths, and a kill switch if errors spike.
- Define success criteria upfront with the CFO and COO, such as accuracy, cost per ticket, and satisfaction versus the human baseline.
- Design workflow coverage to map onto a broader rollout so a win has an obvious next phase.
What a strong answer includes
- Picks scope based on risk and volume data, not convenience, so the CFO sees a deliberate tradeoff.
- Proposes concrete offline metrics, like factual accuracy and brand tone compliance, before live traffic.
- Builds a fallback path, such as human handoff below a confidence threshold, as a non-negotiable guardrail.
- Frames success criteria as numbers the CFO and COO already track, like cost per ticket.
Common mistakes
- Scoping too broadly to impress, which raises risk of a visible failure.
- Skipping offline evals and going straight to live traffic to save time.
Likely follow-up questions
- What would make you recommend against expanding after the pilot?
- How would you handle an idiom or edge case the model gets wrong publicly?
- What is your fallback if accuracy is close but not at the bar?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Making AI Product Decisions
- Chapter 8: Designing Evals for AI Products
- Chapter 6: Designing AI Agents and Tooling