Brief

How to Evaluate an Enterprise AI System Before You Ship

How to Evaluate an Enterprise AI System Before You Ship

If you cannot name the failure modes, the acceptance cases, and who can pause the system, you are not ready for production — no matter how good the demo looks.

  • 8 min read

Brief

How to Evaluate an Enterprise AI System Before You Ship
At a Glance
  • Evaluation is a release gate, not a research side quest.
  • Build a set from real cases — including failures operators already fear.
  • Track quality and latency together; either can kill trust.
  • Require a pause switch and a named owner before wide rollout.

Start with cases, not vibes

Write down the decisions the system will influence. For each, list acceptance examples and known failure modes. Pull them from production logs and operator interviews — not from marketing prompts.

If you cannot assemble that set, you are not evaluating a system. You are admiring a demo.

Make regression automatic

Every material change to prompts, models, retrieval, or tools should re-run the set. Store versions. Record who approved the release. When quality drops, you need a story that is not ‘we’ll look into it.’

Keep human authority visible

For consequential writes — money, clinical status, identity, safety — require a reviewer and show them enough context to disagree. Automation without authority is how you earn incidents.

Operate quality after go-live

Monitor not only uptime but also score distributions, abstention rates, and user overrides. Name who can pause the system. Write the runbook before the first fire drill.

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