- Added four pilot phases (product research, not marketing):
1. Collection — what can actually be detected?
2. Analysis — are decisions understandable?
3. Verification — was recommendation correct?
4. Reflection — what needs to change?
- Measurement principle:
- Not: Did AI find a crack?
- But: Did this become a decision a real person could act on?
- Document 'non-decisions' — equally valuable as clear decisions
- Film workflow, not just infrastructure:
- How you find area, choose mission, document
- What feels unclear, when you become uncertain
- When system saves time
- Observer mindset: document first, change model later
- Build model from real workflows, not assumptions
Rationale: First 20-50 real missions teach more than months of modeling.
- Created FIELD_TRIAL_LOG.md — observation protocol for real customer cases
- Log entry template with 10 fields
- Two example entries (accepted and rejected decisions)
- Questions the log answers: adoption, accuracy, calibration, rejection analysis
- Future KPIs: Decision Adoption Rate, Decision Accuracy, Time to Decision
- Updated MEMORY.md with Control Intelligence & Decision Model section
- Decision Pipeline v1.0 summary
- Decision Object v1.0 (STRUCTURE FROZEN)
- Status: VALIDATED FOR FIELD TRIALS
- Three target customer cases
- Future KPIs
- Key principle: No more modeling until first real customer case
- Added Decision Adoption Rate and Decision Accuracy as future product KPIs
- Not implemented yet — start collecting data now, calculate later
- Decision Adoption = accepted / total recommendations
- Decision Accuracy = correct / total decisions
Rationale: Stop modeling, start observing. First real customer case
will teach more than the last twenty documents combined.
Bernt