Start with the current workflow cost
Use observed volume and effort rather than generic “AI saves 30%” assumptions.
Model recoverable capacity as a range
Not every minute disappears. AI may add review, exception handling and maintenance. Model low/base/high recoverable shares and show the assumptions behind each.
Include the costs people forget
| Cost | Include |
|---|---|
| Implementation | Design, integration, testing, data cleanup |
| Ongoing | Model/API, automation platform, monitoring |
| Human review | Time retained for approvals / exceptions |
| Change | Training, process updates, ownership |
| Risk | Expected cost of corrections or incidents where measurable |
Distinguish capacity from realized value
Ten hours returned is not automatically ten hours of payroll savings. The business may use that capacity for more customers, faster response, reduced backlog or less overtime. Track the actual operational outcome.
Illustrative calculation
40 runs/week × 12 minutes = 8 hours/week. At $45/hour that is $18,720/year of baseline effort. If a controlled pilot returns 55–70% after review overhead, modeled capacity is $10,296–$13,104 before implementation and ongoing costs.
Frequently asked questions
What is a good ROI for AI automation?
There is no universal threshold. Compare expected net value with implementation risk, confidence, payback time and alternative uses of capital.
Are labor hours saved the same as cost savings?
No. Returned hours are capacity unless they reduce actual cost, avoid hiring, increase output or create another measurable business outcome.
How should uncertain estimates be handled?
Use ranges, label confidence and validate the largest assumptions with a pilot before making a larger investment.