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Enterprise10 minUpdated 2026-06-04

Enterprise AI Evaluation Scorecard

Enterprise AI selection is not only a model contest. The winning provider must satisfy engineering, security, legal, finance, support, and product teams at the same time.

Score product fit and operational fit separately

Product fit asks whether the model solves the user task. Operational fit asks whether your team can monitor, secure, pay for, and maintain the system. A model can be excellent on product fit and still be a poor enterprise choice.

Use a weighted scorecard instead of a single rank. For example, customer support may weight latency and groundedness higher than creative reasoning, while research workflows may weight context length and analysis quality higher.

Include non-model requirements

Track data retention, regional hosting, audit logs, admin controls, access management, contractual terms, rate limits, SDK maturity, incident communication, and support response quality.

Ask whether the provider can support the boring work: billing exports, uptime notices, model deprecation timelines, and predictable migration paths.

Run a pilot with a decision memo

Every pilot should end with a written decision memo: what was tested, what failed, what the model is allowed to do, what it must not do, and what would trigger reconsideration.

The memo becomes a reusable artifact for security review, procurement, and future model upgrades.

Practical checklist

  1. 1Separate product fit from operational fit.
  2. 2Weight criteria by workflow.
  3. 3Include legal, security, and finance requirements.
  4. 4Run a real pilot before vendor lock-in.
  5. 5Write a decision memo after evaluation.

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