AI ROI Model With the Cost Side Filled In
Most GenAI ROI claims quote the benefit side and wave at the costs. Here is the honest arithmetic, using the invoice-coding pilot from A.1. Substitute your own numbers; the structure is the point.
| Line | Item | Amount |
|---|---|---|
| B1 | Time saved: 4,100 inv/mo × 4 min saved × €0.60/min loaded cost | + €118,000/yr |
| B2 | Error reduction: coding errors 2.9%→1.2%, × €35 avg rework cost | + €29,000/yr |
| C1 | Inference: ~2,500 tokens/invoice at mid-tier rates | − €1,800/yr |
| C2 | Build: 6 person-weeks (builder + AP lead time) incl. eval construction | − €28,000 one-off |
| C3 | Integration & security review (ERP connector, key management) | − €15,000 one-off |
| C4 | Change management: training, floor support, two process revisions | − €12,000 one-off |
| C5 | Run: monitoring, eval upkeep, quarterly model-upgrade regression | − €14,000/yr |
| Year-1 net (B1+B2 − C1 − C5 − one-offs) | ≈ + €76,000 | |
| Steady-state net (recurring only) | ≈ + €131,000/yr |
Three lessons generalize. Inference (C1) is almost never the cost that matters; people costs (C2-C5) dominate, which is the 10-20-70 rule showing up in the budget. The benefit lines are real only if the saved minutes are redeployed, Chapter 2's evaporation problem, so pair the model with an explicit answer to "what will the AP team do with the time?" And the steady-state line is contingent on C5 actually being spent: an unmonitored system's benefits decay silently as inputs drift and models change.
This template is part of the FDE Toolkit in GenAI for Business. The method behind it is in Chapter 14: Working Like a Forward Deployed Engineer.