FIELD NOTE / INSTAGRAM
A leadership AI scorecard needs five fields.
The complete written thought and the evidence behind it. The video edition will follow its public release.
The written argument is here.
This approved Instagram edition is on the journal now. Its video player and original platform link will appear after each public release is verified.
A leadership AI scorecard needs five fields.
Video caption
A leadership AI scorecard needs five fields.
People may not be able to reconstruct the why.
Use consent-safe records and compare to source.
Do this because review capacity is finite.
#EricFieldNotes
Full written post / accessibility read
If management uses AI to produce and consume its own reporting, I want one card per consequential decision. Who owned it? Which source did they inspect? What condition was retained? What actually happened? What did reversal cost?
Picture twelve immaculate summaries and a customer decision nobody can explain. The original exception is somewhere in the source folder, but the active plan links only to a paraphrase. That is not a dashboard success.
Choose a small sample of decisions, reconstruct the chain, score condition retention and note owner corrections. The scorecard is a proposed audit. NIST governance guidance does not claim this exact form has been validated.
If low-risk drafts are correct, let them flow. If a decision changes customer rights, budget a source readback and explicit owner. Spend human attention on the commitment boundary, and measure whether later reversals fall.
#EricFieldNotes
Four-beat scene transcript
1. A leadership AI scorecard needs five fields.
If management uses AI to produce and consume its own reporting, I want one card per consequential decision. Who owned it? Which source did they inspect? What condition was retained? What actually happened? What did reversal cost?
Visual: Owner, source, caveat, action and reversal cost.
2. The document count can rise while confidence falls.
Picture twelve immaculate summaries and a customer decision nobody can explain. The original exception is somewhere in the source folder, but the active plan links only to a paraphrase. That is not a dashboard success.
Visual: People may not be able to reconstruct the why.
3. Test five fields on a real sample.
Choose a small sample of decisions, reconstruct the chain, score condition retention and note owner corrections. The scorecard is a proposed audit. NIST governance guidance does not claim this exact form has been validated.
Visual: Use consent-safe records and compare to source.
4. Fund attention where reversals hurt.
If low-risk drafts are correct, let them flow. If a decision changes customer rights, budget a source readback and explicit owner. Spend human attention on the commitment boundary, and measure whether later reversals fall.
Visual: Do this because review capacity is finite.
Research and claim limits
- NIST AI RMF 1.0 (S171)
- Lee et al., The Impact of Generative AI on Critical Thinking, CHI 2025 (S172)
The examples identified as illustrative or simulated are design probes, not reported incidents. Vendor specifications do not establish workload performance.