Product design question

A new German enterprise customer wants Sierra to automate support with an AI agent. Walk me through how you would discover requirements across business, operations, and compliance stakeholders; break down their conversation volume into candidate use cases; and decide which 2-3 workflows the first release should handle versus route to human agents. What criteria would you use for scope, risk, and handoff design?

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What this question tests

Discovery and scoping skill for an enterprise AI agent deployment, including risk based prioritization and handoff design.

Run discovery across three groups: business stakeholders for goals and KPIs, operations or support leads for actual conversation volume and pain points, and compliance for German and EU specific requirements like GDPR and works council involvement.
Break down conversation volume into candidate use cases by category, for example order status, returns, billing disputes, and technical troubleshooting, each with an estimated share of total volume.
Score each candidate on frequency, resolution complexity, and risk if the agent gets it wrong, prioritizing high frequency, low risk, clearly bounded workflows like order status over high risk ones like billing disputes.
Choose the two to three workflows with the best frequency to risk ratio, and design explicit handoff triggers, such as any request involving a refund above a threshold or expressed frustration, routing to a human.
Address German specific compliance needs directly, for example data residency and any works council requirements around automated decision making affecting employees or customers.
Define the success metric for the pilot, such as resolution rate on in scope workflows and false handoff rate, before expanding scope.

Names concrete candidate workflows by category and ranks them on a frequency versus risk basis, not just picks arbitrarily.
Designs explicit, rule based handoff triggers rather than a vague escalate when needed.
Addresses Germany specific compliance considerations directly, showing the candidate did real discovery rather than generic answers.
Defines a pilot success metric before recommending scope expansion.

Choosing workflows based on customer excitement rather than actual volume and risk data.
Ignoring GDPR or local compliance nuances specific to a German enterprise customer.

How would you handle a workflow that looked low risk in discovery but turns out high risk in production?
What would you do if the customer insists on including a high risk workflow in the first release?