automation-break-evenlisted

# Accuracy is not savings The model is 92% accurate, so it removes 92% of the work. It does not. With ordinary review costs it removes about 57%, and below roughly 38% accuracy it removes nothing at all and starts adding work. This is the most expensive error in AI business cases, because it is arithmetic on a real measurement and it goes straight into a budget. ## The refusal **Never multiply accuracy by volume or headcount.** If the three cost constants have not been measured, refuse to give a savings figure. Give the break-even instead, which is computable from assumptions and honest about being one. ## The three constants Everything relative to doing the task from scratch (= 1.0): | | what it is | typical | |---|---|---| | `fromScratch` | doing the work with no draft | 1.0 | | `reviewGoodDraft` | skim a correct draft, agree, move on | 0.3 to 0.4 | | `reviewBadDraft` | read it, find the flaw, discard, start over | 1.3 to 1.6 | `reviewBadDraft` is above 1.0 and that is the whole point. Handling a wrong answer costs more than never having had one. ## Procedure 1. **Time the three constants on real work.** Twenty items each is usually enough to separate 0.35 from 1.4. If you cannot measure them, say the numbers are assumed and show the break-even curve rather than a point estimate. 2. `breakEven(reviewGoodDraft, reviewBadDraft)` from `lib/stats.ts`. Report it first. 3. `realSaving(accuracy, good, bad)` for the actual figure. 4. **Carry the accuracy interval th