- Farah Jama Principle: AI and data initiatives require forensic-level transparency (whole-of-case visibility) across the entire process (upstream to downstream), as demonstrated by the Farah Jama case in forensic science. Organizations fail when treating AI projects as isolated IT tasks rather than integrated workflows.
- Root Cause: Lack of end-to-end visibility in AI projects mirrors forensic failures where scientists missed upstream/downstream connections (e.g., evidence handling chain).
- Failure Pattern: AI initiatives collapse when siloed (e.g., data team vs. deployment team) instead of adopting forensic-style process mapping.
- Solution: Implement cross-functional process mapping from data ingestion to business impact, requiring all stakeholders to document upstream/downstream dependencies.
This insight is one of three surprising insights from Paul’s knowledge base (see 2026 04 14 I Feel Lucky Generate insights from All Topics).
Source Notes
- 2026-04-23: [[lab-notes/2026-04-23-Anthropics-Compute-Miscalculation-Claude-Demand-and-Strategic-Impact|Anthropic’s Compute Miscalculation: Claude Demand and Strategic Impact]]
- 2026-04-14: [[lab-notes/2026-04-14-Optimizing-AI-Costs-and-Privacy-with-Local-Open-Source-Models-and-Hybr|“But OpenClaw is expensive…“]]
- 2026-04-14: I Looked At Amazon After They Fired 16,000 Engineers. Their AI Broke Everything.