Quality and Error Effects

Model the error rate in each scenario

Error costs can dominate the case. AI with human review often lowers error rates (consistent extraction, flagged anomalies), but AI without review can raise them through confident mistakes. Estimate error rates per scenario from a pilot, multiply by volume and cost per error, and include the review design in the case. This also shows why removing review to save time can destroy value.

Error costs in three scenarios, run

I ran this with plain Python 3. All figures belong to one worked example (an invoice-processing team) with invented but internally consistent numbers; prices are placeholders. At 12,000 invoices and $25 per error, manual work at 4% costs $12,000 a month, AI with review at 1.5% costs $4,500, and AI without review at 6% would cost $18,000, worse than today.

volume = 12000
scenarios = {  # error rate, cost per error
    "manual today": (0.040, 25.0),
    "AI + human review": (0.015, 25.0),
    "AI without review": (0.060, 25.0),
}
for name, (rate, cost) in scenarios.items():
    print(f"{name:<18} errors/month {volume * rate:>5.0f}  cost ${volume * rate * cost:>7,.0f}")

Output:

manual today       errors/month   480  cost $ 12,000
AI + human review  errors/month   180  cost $  4,500
AI without review  errors/month   720  cost $ 18,000

Measure error rates in the pilot

Error rates vary widely by document type and model; use pilot data, not vendor claims.

त्वरित जाँच: What can happen if human review is removed to save time?

• Costs disappear
• Errors always fall
• Error costs can rise enough to wipe out the savings
• Volume doubles