b934797e65
- 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.
176 lines
4.8 KiB
Markdown
176 lines
4.8 KiB
Markdown
# FIELD TRIAL LOG
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**Observation Protocol for Decision Model Validation**
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|---|---|
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| **Version** | 1.0 |
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| **Status** | ACTIVE |
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| **Purpose** | Collect raw data from real customer cases |
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---
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## Log Entry Template
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| Field | Example | Notes |
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|-------|---------|-------|
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| **Date** | 2026-07-15 | |
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| **Customer Type** | Municipality / Property Owner / Contractor | |
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| **Decision** | Inspect Road A12 | What the system recommended |
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| **Decision Accepted** | Yes / No | Did the user accept? |
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| **Action Executed** | Yes / No / Pending | Was the action carried out? |
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| **Outcome** | Crack confirmed | What actually happened |
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| **Time to Decision** | 3 minutes | How long from login to decision |
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| **User Feedback** | "We trusted the recommendation." | Verbatim or summary |
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| **Confidence** | 87% | System confidence at decision time |
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| **Notes** | Free text | Anything else observed |
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---
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## Example Entries
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### Entry 1
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| Field | Value |
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|-------|-------|
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| Date | 2026-07-15 |
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| Customer Type | Municipality |
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| Decision | Inspect Road A12 |
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| Decision Accepted | Yes |
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| Action Executed | Yes |
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| Outcome | Crack confirmed, repaired within 14 days |
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| Time to Decision | 3 minutes |
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| User Feedback | "We trusted the recommendation." |
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| Confidence | 87% |
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| Notes | User clicked through Explainability chain |
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### Entry 2
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| Field | Value |
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|-------|-------|
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| Date | 2026-07-16 |
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| Customer Type | Property Owner |
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| Decision | Repair facade immediately |
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| Decision Accepted | No |
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| Action Executed | No |
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| Outcome | Deferred, user chose to wait |
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| Time to Decision | 5 minutes |
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| User Feedback | "Too expensive right now." |
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| Confidence | 92% |
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| Notes | Confidence was high but user rejected due to cost |
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---
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## Questions This Log Answers
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After collecting entries:
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- Were recommendations accepted? (Decision Adoption Rate)
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- Were actions executed? (Execution Rate)
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- Did outcomes match predictions? (Decision Accuracy)
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- Was confidence reasonable? (Calibration)
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- Which recommendations were ignored? (Rejection Analysis)
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- How long did decisions take? (Time to Decision)
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---
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## Future KPIs
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### Decision Adoption Rate
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```
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Recommendations accepted
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────────────────────────
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Total recommendations
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```
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Example: 72 of 100 = **72%**
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### Decision Accuracy
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```
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Decisions that proved correct
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─────────────────────────────
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Total decisions
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```
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Example: 65 of 72 executed = **90%**
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**Note:** These require months of data. Start collecting now, calculate later.
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---
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## Pilot Structure
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**Four phases — product research, not marketing:**
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| Phase | What You Do | What You Learn |
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|-------|-------------|----------------|
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| **1. Collection** | Film, photograph, create observations | What can actually be detected? |
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| **2. Analysis** | Let pipeline create Findings and Decisions | Are decisions understandable and relevant? |
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| **3. Verification** | Compare with reality on site | Was the recommendation correct? |
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| **4. Reflection** | Document what worked and what didn't | What needs to change in the model? |
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**Full chain captured:**
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```
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Reality → Observation → Evidence → Finding → Decision → Action → Outcome → Learning
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```
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## What to Measure
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**Not:** Did AI find a crack?
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**But:** Did this become a decision a real person could act on?
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**Example:**
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- AI finds 23 cracks
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- LandveX says: "Inspect Road A12 within 30 days"
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- **The second is the product.**
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## Document "Non-Decisions"
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Also valuable:
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- "No action needed"
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- "More data required"
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- "Cannot recommend anything yet"
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If the system always tries to give advice even when evidence is weak, you risk building a system that feels confident when it shouldn't.
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## Film the Workflow
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Since you are conducting the pilots, film more than just infrastructure. Also film:
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- How you find an area
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- Why you choose a mission
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- How you document
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- How long it takes
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- What feels unclear
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- When you become uncertain
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- When the system saves time
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This material becomes invaluable for product development, onboarding, training, and sales.
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## Observer Mindset
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**Principle:** If data contradicts the model, the model should change.
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Use the same approach during pilots. If you notice:
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- A step feels unnecessary
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- A decision becomes unclear
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- A recommendation cannot be acted on
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**Document first, change model later.** Build the model from real workflows instead of assumptions.
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**The first 20–50 real missions will give you more valuable product knowledge than months of additional modeling.**
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## Status
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**ACTIVE — Awaiting first customer case**
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---
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## ändringshistoria
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| Version | Datum | Beskrivning |
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|---------|-------|-------------|
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| 1.0 | 2026-07-02 | Initial field trial log template |
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| 1.1 | 2026-07-02 | Added pilot phases, measurement principles, filming protocol |
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