Give each output an accountable owner
Document the decision, intended users and advisory or operational role. Define who can accept a suggestion, who can stop a pilot and who owns the underlying equipment or network. A model team and an operational decision owner may be different groups.
Make the data trail inspectable
Record the origin, permissions, asset identifiers, timestamps, quality limits and model version. Include missing and stale data in validation. Our pilot builder asks whether these records exist. Its checklist is an editorial discussion aid and does not certify a system.
Keep the rollout observable
Begin with a baseline and a bounded shadow evaluation where suitable. Agree acceptance criteria before reviewing results. Include false alarms, drift checks, incident handling and rollback. The IEA distinguishes pilot, trial and routine deployment in its 2026 network survey method; those stages should not be conflated. [I3]
Respect the operational boundary
Connecting an AI system to operational technology changes the review required. Evaluate access, segmentation, monitoring, supplier responsibilities and the effect of bad outputs with qualified teams. No tool on this website connects to equipment or authorises an operating action.
A practical evaluation framework
| Decision area | Evidence to record | Question to ask |
|---|---|---|
| Evidence | Baseline, data provenance and test record | Can an independent reviewer reproduce the comparison? |
| People | Decision owner and human intervention | Who is available when outputs are uncertain? |
| Rollout | Shadow trial, stop conditions and cyber review | What causes a pause or rollback? |
Primary references
- [I3] IEA | AI applications in electricity networks, survey method | 16 September 2026
- [N3] Siemens Energy | Q2 FY2026 shareholder letter | 22 May 2026
Reviewed 7 November 2026. Evaluation questions are independent editorial analysis.
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