Define success metrics for support ticket summarizer serving enterprise admins

Problem Statement Description

You are evaluating a support ticket summarizer used by enterprise admins who manage high-volume customer or employee support queues. The summarizer turns long ticket threads, attachments, prior agent notes, system events, and customer history into concise summaries that help admins and support teams understand the issue, route it correctly, and drive a high-quality resolution.

The main business goal is quality resolution, not simply faster handling. In this context, “success” should capture whether summaries help teams resolve the right problem accurately, completely, and with customer trust, while avoiding hallucinations, missing context, privacy issues, or over-reliance on AI-generated content.

Your task is to define a metrics framework for evaluating this product experience. The framework should make clear what outcomes matter, how they would be measured, which users and workflows are included, and how the metrics would guide product decisions before and after launch.

The experience should consider:

- How to define “quality resolution” for enterprise support workflows, including resolution correctness, completeness, durability, and customer/admin satisfaction.

- The right denominators, such as tickets summarized, tickets viewed by admins, tickets resolved after summary use, eligible tickets, or enterprise accounts using the feature.

- Instrumentation needed to connect summary generation, admin consumption, ticket actions, escalations, reopens, SLA performance, and final resolution outcomes.

- Cohorts and segments, including ticket complexity, channel, priority, language, product area, customer tier, admin role, and new versus experienced support teams.

- Summary quality signals, including accuracy, relevance, missing critical details, hallucinated information, freshness, and usefulness in handoff or escalation.

- Guardrail metrics for responsible AI and enterprise trust, such as privacy leakage, unsafe content, compliance violations, bias across languages or regions, and admin override behavior.

- Operational and product health metrics, including latency, availability, coverage, cost per summary, adoption, repeat usage, and impact on support team workload.

- Decision usefulness, including how the metrics would indicate whether to launch, expand, tune the model, restrict use cases, or require human review.

The goal is to propose a clear, decision-oriented measurement approach that balances business impact, user value, AI quality, and enterprise risk. Your answer should show how you would know whether the summarizer is genuinely improving support outcomes rather than only making the workflow appear faster or more automated.