Assumption Tracking
summary
A clear recipe: draft assumptions, rank each by risk and confidence (1–5), and validate only the high‑risk low‑confidence ones. A construction case study uses project meatbag — a manual service that runs customer workflows — implemented with Google Sheets automation, hired data‑entry contractors, then a script that becomes an algorithm. The team tracks 600 items across five projects over four months including a half‑billion‑dollar interchange, finds an unexpected auto‑extract product, and the approach proves effective.
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chapters (9)
- 0:00Intro
- 0:46What's assumption tracking?
- 4:33So how does assumption tracking work?
- 14:14Draft your assumptions
- 26:05Example assumptions
- 32:45Rank assumptions by risk and confidence
- 40:58Assumption ranking as a remote group
- 41:51Step 3: Validate and invalidate!
- 45:01Bring this into your process
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