Evaluation · 9 minute read
Evaluate the workflow, not only the model
A repeatable evaluation approach spanning business, technical, trust and operating acceptance.
- Risk & Assurance
- Trust
- Global
- Build
Decision lens
What evidence should support release, restriction or rejection of an enterprise AI capability?
Model benchmarks rarely answer the operational question. Enterprise evaluation must test the complete path from request and context through action, oversight and recorded outcome.
Translate outcomes into tests
Start from material workflow decisions and failure modes. Define observable quality, authority, safety, reliability, latency and cost thresholds.
- Use representative business cases
- Include edge and adversarial cases
- Define pass, review and stop
Preserve repeatability
Version test data, instructions, models, knowledge and tools. Record failures and accepted limitations so later releases can be compared.
- Separate test and tuning sets
- Retain failure examples
- Track evaluation coverage
Connect evaluation to authority
A release decision needs an accountable owner, accepted residual risk, monitoring, incident readiness and a defined response when observed performance changes.
- Name threshold owners
- Require re-evaluation after material change
- Link failures to remediation
Practical checklist
Evidence to bring into the decision.
- 01Evaluation plan
- 02Versioned test set
- 03Acceptance thresholds
- 04Failure register
- 05Release decision record
Continue with evidence
Connect the perspective to an operating method.
Evaluation reduces uncertainty for a defined use and test scope. It does not eliminate risk or guarantee future behaviour.