Strategy question
A Fortune 500 prospect believes in Decagon’s vision but is skeptical that an AI agent can safely automate complex support journeys across chat, voice, email, and SMS. How would you structure the pre-sales process from discovery through pilot to secure the technical win, convince the C-suite the rollout is worth the risk, and choose the narrow initial deployment scope that maximizes proof of value while minimizing implementation risk?
- Decagon
- Strategy
- Hard
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What this question tests
Tests structuring an enterprise pre-sales motion for a risky AI automation product, from technical proof to C-suite buy-in, and scoping a pilot that proves value with minimal risk.
How to approach it
- Start discovery by mapping current support volume and complexity across chat, voice, email, and SMS, and identify the channel and ticket type with the highest volume and lowest ambiguity.
- Propose a narrow pilot scope, for example fully automating one high-volume, low-ambiguity chat ticket type with a hard handoff to a human for anything uncertain.
- Define pilot success metrics upfront with the customer: resolution rate without escalation, customer satisfaction versus the human baseline, and containment rate, agreed before the pilot starts.
- Build the technical win by showing eval results and live pilot data to the engineering and operations stakeholders, not just a demo.
- Win the C-suite by translating pilot results into business terms, cost per resolved ticket and projected annual savings, tied to their existing support cost baseline.
- Propose a phased expansion plan, additional ticket types then additional channels, contingent on the pilot metrics holding.
What a strong answer includes
- Chooses the narrowest, lowest-ambiguity workflow first, explicitly trading initial scope for proof of safety and reliability.
- Sets pilot success metrics jointly with the customer before starting, so results are not disputed after the fact.
- Separates the technical win, evals and operations sign-off, from the executive win, cost savings translated to dollars.
- Proposes a concrete phased expansion tied to metrics, not a vague we'll expand later promise.
Common mistakes
- Pitching full multi-channel automation before proving safety on one narrow, low-risk workflow.
- Skipping agreement on pilot success metrics upfront, leading to disputes over whether it worked.
- Talking to the C-suite only in technical terms instead of cost and risk they can act on.
Likely follow-up questions
- What would you do if the pilot metrics are good but the C-suite is still hesitant?
- How would you choose the ticket type if volume and ambiguity point to different channels?
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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