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How accurate must the AI workflow be to justify deployment?
Balance gross process savings with review cost, model cost and expected error losses.
Example scenario
Editable demonstration inputs, not verified industry benchmarks. Enable JavaScript to calculate your own scenario.
- Annual AI-processed tasks
- 100,000
- Process saving / task before review
- $3
- Human review cost / task
- $1
- AI & platform cost / task
- $0
- Residual material-error rate
- 1 %
- Expected cost / material error
- $150
- Annual fixed implementation cost
- $40,000
Annual risk-adjusted AI benefit
Example result only, not a forecast or promise.
- Residual error cost / year
- $150,000
- Break-even residual error rate
- 1.2%
- Savings before residual errors
- $180,000
What to do with this result
Segment errors by severity and escalate intolerable cases; expected financial value cannot justify unsafe or noncompliant outputs.
Know what is inside the model.
Planning model, not personalized financial advice. Your inputs remain in your browser.
Calculation method
Annual net = tasks × (saving − review − model cost − residual error rate × error loss) − fixed cost. Maximum tolerable error rate = (saving − review − model cost − fixed/tasks) / error loss.
Assumptions and limitations
- Example inputs are editable scenarios, not Aryo results or independently verified industry benchmarks.
- Time value is not guaranteed payroll savings. Include adoption, human review, exceptions, security and change management in implementation decisions.