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Evidence and traceability

Make AI-assisted quality work understandable

Trust grows when a team can reconstruct what AI contributed, what was reviewed, who decided, and what evidence supports the result under an AI governance model.

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Traceability supports both control and confidence

Evidence is not only something to show during an inspection. It helps people review work consistently, understand uncertainty, investigate exceptions, and improve the operating model over time.

  • Context

    Keep the intended use, input context, source references, and output class visible.

  • Review

    Record who reviewed the output, what they decided, and why.

  • Change

    Retain relevant model, prompt, rules, retrieval, and workflow version context.

What a useful evidence chain can show

  • What happened

    Identify the workflow, input, AI contribution, output, and downstream action.

  • Who was accountable

    Make initiation, review, acceptance, rejection, escalation, and approval attributable.

  • What was known

    Preserve source evidence, uncertainty, missing information, and relevant version context.

  • What changed

    Connect material changes to review, evaluation, rollback, and follow-up decisions.

Proportionate evidence by output impact

Informational support does not require the same evidence chain as output incorporated into a controlled record. The planning model should help the team collect the right evidence for the process impact without treating every output identically.

  • Understandable

    A reviewer can follow the path from input to output to decision.

  • Retrievable

    The evidence can be found and reviewed when the quality team needs it.

Next steps

Continue the decision

Need to strengthen AI evidence?

Bring the workflow, output, reviewer, and retrieval questions your team is working through.

Discuss your pathway