docs(bi): sanitize documentation for public consumption

Removed internal strategic information and reframed claims to align
with framework rules (inst_016, inst_017, inst_018).

Changes:
- Removed frontmatter: media_rollout_notes, strategic_assessment
- Removed maturity claims: 'novel approach', 'key innovation'
- Removed unsupported claims: 'early evidence suggests', 'critical missing piece'

Replaced with:
- Research-appropriate language: 'research prototype', 'experimental system'
- Empirical framing: 'Research Question: Can...'
- Tentative conclusions: 'remains an empirical question'
- Validation requirements emphasized throughout

Document now appropriate for public consumption while maintaining
technical accuracy and research integrity.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
TheFlow 2025-10-27 12:17:38 +13:00
parent f9369967b1
commit 1f57c13d98

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@ -16,28 +16,6 @@ version_history:
- "Current capability assessment"
- "Short-term and long-term development roadmap"
- "Research goals and limitations documented"
media_rollout_notes: |
IMPORTANT: This is a research prototype demonstrating governance ROI visualization concepts.
Before public announcement:
- Validate cost calculation methodology with industry data
- Add disclaimers about illustrative values
- Test with pilot organizations for feedback
- Consider peer review of approach
Timeline considerations:
- Blog post planned for early November 2025
- UI integration requires careful messaging about prototype status
- Media rollout should emphasize research contribution, not commercial tool
strategic_assessment: |
CRITICAL INSIGHT: ROI visualization may be the key differentiator for framework adoption.
Organizations don't buy "governance frameworks" - they buy incident cost avoidance,
compliance evidence, and team productivity metrics. This tool demonstrates how
AI governance can be measured and justified to leadership.
However: Must maintain research integrity. Current cost factors are illustrative
placeholders, not validated industry benchmarks.
---
# Governance Business Intelligence Tools
@ -51,13 +29,13 @@ strategic_assessment: |
## Executive Summary
This document describes a novel approach to **quantifying AI governance framework value** through business intelligence tools. The Tractatus Framework has implemented a research prototype that transforms technical governance metrics into executive-decision-relevant insights including cost avoidance, compliance evidence, and team productivity analysis.
This document describes a **research prototype** for quantifying AI governance framework value through business intelligence tools. The Tractatus Framework has implemented an experimental system that transforms technical governance metrics into executive-decision-relevant insights including cost avoidance estimates, compliance evidence, and team productivity analysis.
**Key Innovation**: Automatic classification of AI-assisted work by activity type, risk level, and stakeholder impact enables real-time ROI calculation and organizational benchmarking.
**Research Question**: Can automatic classification of AI-assisted work by activity type, risk level, and stakeholder impact enable meaningful ROI visualization and organizational benchmarking?
**Research Status**: Current implementation demonstrates feasibility. Cost factors are illustrative placeholders requiring validation. Methodology is sound; specific values need organizational customization.
**Prototype Status**: Current implementation demonstrates technical feasibility. Cost factors are illustrative placeholders requiring validation against real organizational data. The classification methodology is heuristic-based; accuracy depends on organizational customization and empirical validation.
**Strategic Potential**: Early evidence suggests ROI visualization is the critical missing piece for governance framework adoption at scale.
**Open Research Direction**: Whether ROI visualization addresses adoption barriers for governance frameworks remains an empirical question requiring pilot deployments and comparative studies.
---