From b934797e651d69e07c7379b66504c23dcb789fa9 Mon Sep 17 00:00:00 2001
From: Bernt <bernt@aamos.systems>
Date: Thu, 2 Jul 2026 12:21:20 +0000
Subject: [PATCH] =?UTF-8?q?docs:=20Field=20Trial=20Log=20v1.1=20=E2=80=94?=
 =?UTF-8?q?=20Pilot=20phases=20+=20filming=20protocol?=
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- 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.
---
 docs/design/FIELD_TRIAL_LOG.md | 62 ++++++++++++++++++++++++++++++++++
 1 file changed, 62 insertions(+)

diff --git a/docs/design/FIELD_TRIAL_LOG.md b/docs/design/FIELD_TRIAL_LOG.md
index c259b6243..5bbebd977 100644
--- a/docs/design/FIELD_TRIAL_LOG.md
+++ b/docs/design/FIELD_TRIAL_LOG.md
@@ -100,6 +100,67 @@ Example: 65 of 72 executed = **90%**
 
 ---
 
+## Pilot Structure
+
+**Four phases — product research, not marketing:**
+
+| Phase | What You Do | What You Learn |
+|-------|-------------|----------------|
+| **1. Collection** | Film, photograph, create observations | What can actually be detected? |
+| **2. Analysis** | Let pipeline create Findings and Decisions | Are decisions understandable and relevant? |
+| **3. Verification** | Compare with reality on site | Was the recommendation correct? |
+| **4. Reflection** | Document what worked and what didn't | What needs to change in the model? |
+
+**Full chain captured:**
+```
+Reality → Observation → Evidence → Finding → Decision → Action → Outcome → Learning
+```
+
+## What to Measure
+
+**Not:** Did AI find a crack?
+**But:** Did this become a decision a real person could act on?
+
+**Example:**
+- AI finds 23 cracks
+- LandveX says: "Inspect Road A12 within 30 days"
+- **The second is the product.**
+
+## Document "Non-Decisions"
+
+Also valuable:
+- "No action needed"
+- "More data required"
+- "Cannot recommend anything yet"
+
+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.
+
+## Film the Workflow
+
+Since you are conducting the pilots, film more than just infrastructure. Also film:
+- How you find an area
+- Why you choose a mission
+- How you document
+- How long it takes
+- What feels unclear
+- When you become uncertain
+- When the system saves time
+
+This material becomes invaluable for product development, onboarding, training, and sales.
+
+## Observer Mindset
+
+**Principle:** If data contradicts the model, the model should change.
+
+Use the same approach during pilots. If you notice:
+- A step feels unnecessary
+- A decision becomes unclear
+- A recommendation cannot be acted on
+
+**Document first, change model later.** Build the model from real workflows instead of assumptions.
+
+**The first 20–50 real missions will give you more valuable product knowledge than months of additional modeling.**
+
 ## Status
 
 **ACTIVE — Awaiting first customer case**
@@ -111,3 +172,4 @@ Example: 65 of 72 executed = **90%**
 | Version | Datum | Beskrivning |
 |---------|-------|-------------|
 | 1.0 | 2026-07-02 | Initial field trial log template |
+| 1.1 | 2026-07-02 | Added pilot phases, measurement principles, filming protocol |