How would you design AI for customer support automation?

Signal to interviewer

I can optimize support automation by explicitly balancing economics and customer experience, rather than maximizing automation rate in isolation.

Clarify

I would clarify support scope: channels, ticket taxonomy, service levels, and regulatory obligations. I would identify which interactions are deterministic, policy-bound, or emotionally sensitive. I would align on acceptable automation boundaries and escalation promises.

Approach

Use a cost–quality frontier framework. For each ticket class, map expected quality at different automation depths and find the efficient frontier. Deploy full automation only where confidence and policy adherence are stable. Use agent-assist for mixed-complexity tickets and guaranteed human handling for high-risk categories.

Metrics & instrumentation

Primary metric: cost per resolved ticket by queue. Secondary metrics: first-contact resolution, handle-time reduction, and escalation transfer quality. Guardrails: customer satisfaction decline, repeat-contact rate, and incorrect-policy response incidents. Instrumentation: intent classification confidence, retrieval quality signals, handoff reason codes, and downstream resolution outcomes.

Tradeoffs

Maximizing automation lowers cost but can increase quality volatility on edge cases. Stronger safeguards reduce bad outcomes but can push more tickets to human queues. Fast response speed can conflict with deeper evidence retrieval.

Risks & mitigations

Risk: wrong auto-resolve for nuanced cases; mitigate with confidence thresholds and forced escalation bands. Risk: agent distrust in AI drafts; mitigate with editable rationale and feedback loops. Risk: queue gaming to hit efficiency targets; mitigate with shared quality-plus-cost KPIs.

Example

For an ecommerce support stack, order-status and return-policy tickets get full automation, billing disputes use agent-assist with evidence packets, and fraud-related cases always route to specialists.

90-second version

Design support AI on a cost–quality frontier. Automate where quality is stable, assist where judgment is needed, and preserve human ownership for sensitive flows. Measure economics with strong trust guardrails so efficiency gains are durable.