Workflow Economics Calculator
Quantify the invisible labor in one workflow before anyone builds anything. Current-state figures come straight from your inputs. Future-state figures are a model, clearly labeled, and depend on assumptions you control.
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Current cost per outcome $13.47. Modeled cost per outcome $8.86.
Current state
From your inputs- Annual human hours
- 5,440
- 907 per person
- Annual labor cost
- $264,000
- Annual rework cost
- $35,200
- Cost per outcome
- $13.47
- Labor, rework and tools
- Manual handoffs per year
- 72,000
- Each one is an integration candidate
Modeled future state
Estimate, not a guarantee- Recoverable capacity
- 2,176 h/yr
- About 1.2 FTE at 1,800 h/yr
- Throughput, same team
- +66.7%
- AI / automation run cost
- $9,000/yr
- Cost per outcome
- $8.86
- Annual net saving
- $110,680
- Labor and rework saved, minus AI run cost
- Payback
- Not modeled
- Add a build estimate to model it
The model assumes recovered hours go back into the same work. Real results depend on data quality, exception rates and adoption. We measure them against a baseline before and after any deployment.
How the model works
A model is only as good as its assumptions. Here is exactly what this one does, so you can judge it.
- Current state
- Hours = outcomes × (minutes per outcome + rework rate × rework minutes). Labor and rework cost = hours × loaded rate. Cost per outcome adds your current tool spend.
- Recoverable capacity
- Covered outcomes keep the review share of their original time. Everything else on those outcomes is modeled as recovered.
- Throughput
- Assumes the same team spends the recovered hours on the same work, so output grows as time per outcome falls.
- Future cost per outcome
- Remaining human time plus current tool spend plus the AI / automation run cost for covered outcomes.
- Payback
- Your one-time build estimate divided by the modeled monthly saving. Shown only when you enter an estimate and the saving is positive.
- Full-time equivalents
- Recoverable hours divided by 1,800 hours per person per year. Capacity to redeploy, not a headcount target.
This is an estimate, not a quote, a guarantee or a client result. Real outcomes depend on data quality, exception rates, integration and adoption. In an engagement, we replace every assumption with a measured baseline before anything is deployed.
Turn the estimate into a measured baseline
Bring us one workflow that is slow, repetitive, error-prone or expensive. We'll help determine whether AI is actually the right lever.