Turn execution evidence into learning
In your words: “Our systems tell us what happened, but not why the process failed or what to change”
- Learning from execution
- Execution evidence
- Governed learning
Decisions, exceptions, outcomes and context are stored in disconnected logs or not retained meaningfully. This becomes most visible when pilots move from assisting individuals to making or executing decisions inside operational workflows.
When execution evidence is retained and connected, the organisation can see not just what happened but why, and what to change.
Problems stop recurring, governance can show behaviour and agents build on lessons instead of repeating them.
Execution evidence converts into governed learning and adaptation, so each capability improves with use. In practical terms, each capability can move to the autonomy level justified by its value, risk and human-oversight needs.
- Greater auditability, less repeated failure and continuous improvement of specifications, controls and agent behaviour
- Clear autonomy and authority boundaries
- More credible AI value cases
- Continuous evidence and oversight
- Controlled progression from assistance to autonomy
- Which execution decisions are currently reproducible?
- How are exceptions used to improve specifications?
- Can governance reconstruct why an outcome occurred?
- What evidence must an agent produce before its action is accepted?
- Under what condition must authority return immediately to a human decision-maker?
NIST provides resources for testing, evaluation, verification and validation, reinforcing the need for evidence throughout AI operation and adaptation.
NIST: AI Resource Center
NIST’s Generative AI Profile extends the AI RMF with actions for identifying and managing risks specific to generative AI systems and their use.
NIST: Generative AI Profile