What are AI errors
costing your team?
Estimate the cost of AI errors in your data analysis - and the time spent verifying accuracy.
Total annual exposure
The modeled cost of misdirected decisions and manual verification.
✓ View formula
Decision risk
At $750M in revenue, Your team directs an estimated $75M growth budget. When decisions rely on chains of AI analysis, errors can compound.
Probability that every analysis in the chain is correct, at a 10% per-analysis error rate — the rate FUSE measures in production. Errors are modeled as independent. Correlated failures (same connector, same join, same ambiguous question) would make this curve shallower; strict downstream propagation would make it steeper. This is the simplest case, not the worst one.
View chart data
| Analyses in chain | Unverified AI (Clean %) | FUSE + Emet |
|---|---|---|
| 1 | 90.0% | 100% |
| 2 | 81.0% | 100% |
| 3 | 72.9% | 100% |
| 4 | 65.6% | 100% |
| 5 | 59.0% | 100% |
| 6 | 53.1% | 100% |
| 7 | 47.8% | 100% |
| 8 | 43.0% | 100% |
| 9 | 38.7% | 100% |
| 10 | 34.9% | 100% |
✓ View formula
- Growth budget: $750M × 10% benchmark.
- Decisions: 60 people × 5 material decisions/year.
- 55.00000000000001% AI influence: Share of material decisions informed by AI analysis.
- 35% decision-changing: Only 35% of flawed analyses are wrong in a way that alters the decision.
- 20% value lost: The penalty on a misdirected dollar vs correct allocation.
Verification tax
Your team of 60 spends an estimated 4.5 hours a week per person checking AI output before acting.
✓ View formula
12,420 hrs × $69.71/hr = $865,798
- $69.71/hr: Derived from $145K fully loaded cost per person over 2080 paid hours.
- Loaded cost: Base salary × 1.43 multiplier for benefits and overhead (BLS Employer Costs).