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AI evaluation and effectiveness

Evaluate AI before release and keep learning after release

Good quality is sustained when AI governance evaluation criteria are defined before use, effectiveness is reviewed after release, and findings lead to action.

Discuss your GMP AI pathway

Evaluation is part of the operating model

A one-time launch check is not enough when an AI-assisted workflow, its data, its prompts, or its surrounding process can change. Evaluation should connect intended use, quality expectations, review evidence, and improvement.

  • Define success before release

    Set evaluation criteria, acceptance thresholds, review scope, and failure handling before the workflow is used.

  • Monitor the operating reality

    Watch output quality, review outcomes, exceptions, missed reviews, and escalation signals after release.

  • Turn findings into improvement

    Use deviations, review findings, and effectiveness checks to improve the workflow under control.

Questions for pre-release evaluation

  • Is intended use clear?

    The process, decision, data, output, reviewer, and evidence expectations should be explicit.

  • Are acceptance thresholds defined?

    Decide what the team must demonstrate before the use case is allowed to proceed.

  • Are failure paths testable?

    Define what happens when output is uncertain, incomplete, incorrect, or unavailable.

Questions for post-release review

  • What signals matter?

    Monitor quality, review, exception, escalation, and process signals rather than infrastructure health alone.

  • Who owns the response?

    Name the decision owner when a threshold is breached or a control is missed.

  • When is re-evaluation needed?

    Connect material changes and observed drift to a documented re-evaluation decision.

Next steps

Continue the decision

Need an evaluation path for a GMP AI use case?

Bring the intended use, quality criteria, and review signals your team needs to define.

Discuss your pathway