Annual saving = hours per week × loaded hourly cost × 52 × automation rate. Payback = build cost ÷ (annual saving − annual operating cost). That's the whole model, and it's deliberately boring. The arithmetic has never been what makes AI business cases wrong — the inputs are.
Worth doing before you commit, because the base rate is bad: 95% of enterprise generative-AI pilots deliver no measurable P&L impact (MIT Project NANDA, 2025). A number you built honestly before starting is the cheapest protection against joining them.
A worked example
Support triage consumes 20 hours a week. Loaded cost of the people doing it is $75/hour. So the workflow costs about $78,000 a year. At a 60% automation rate, the agent saves roughly $46,800 a year. Operating the system runs $3,000 a month, or $36,000 — leaving about $10,800 of net annual benefit against a build.
And there's the lesson: on a $75,000 build, that's a payback measured in years, which means this is a bad project *as scoped* — despite a 60% automation rate that sounds like a triumph. Most business cases never surface that, because most business cases quietly omit the operating line.
Change one input and it inverts. At 40 hours a week the same workflow costs $156,000, saves $93,600, nets $57,600 after operations, and pays back a $75,000 build in about sixteen months. Volume is the variable that decides almost everything — which is why the first question is always which workflow, not which model.
The four inputs, and how to get them honestly
- Hours per week. Measure, don't estimate. Ask the people doing the work, and expect the real number to differ from the manager's estimate in both directions.
- Loaded hourly cost. Salary plus benefits, tax, software, and management overhead — typically 1.25–1.4× base salary. Using base salary alone understates the case; using a blended executive rate overstates it.
- Automation rate. What share of cases the system closes end to end, with no human touch. Not deflection, not assistance — completion.
- Annual operating cost. Monitoring, eval regressions, drift management, model migrations. Ours is $3,000–$20,000 a month; whatever you use, it is not zero.
The three numbers people inflate
Automation rate. The most common failure. Teams model 90% because the demo handled 9 of 10 curated cases; production sees the long tail and lands at 55%. Model 50–70% for a well-scoped workflow and treat anything above that as upside you have to earn. A support agent reaching 60–70% autonomous resolution is a good outcome, not a floor.
Hours saved as money saved. Freeing 12 hours a week across a team of eight doesn't remove a salary — it removes ninety minutes each from eight people, who fill it with other work. That's real value, but it is capacity, not cash, and a CFO will make that distinction even if the business case doesn't. Say which one you're claiming.
Time to value. Savings do not start on the contract date. A build takes about 90 days and ramps after that, so year one typically captures a fraction of the annual figure. Model it from go-live, not from kickoff.
What to do with a long payback
If payback lands past 18 months, don't tune the spreadsheet — change the project. Three moves, in order of how often they work:
- Pick a higher-volume workflow. Volume dominates every other input. The second-choice workflow with twice the throughput usually beats the favourite.
- Narrow the scope. A tighter agent with a higher completion rate on a smaller case set often beats a broad one that half-handles everything.
- Check the non-cash benefits are real. Response time, consistency, after-hours coverage, and reduced churn can carry a project — but only if someone will actually commit to a number for them. If nobody will, they aren't benefits, they're hopes.
The counter-case is worth stating too: IDC puts the average enterprise return at $3.70 per $1 invested in generative AI, though that's a Microsoft-sponsored study, so treat it as an optimistic ceiling rather than a planning input. Your own arithmetic on your own workflow beats any benchmark.
Presenting it internally
Bring three scenarios, not one. Conservative (40% automation), expected (60%), and optimistic (75%) — with payback for each. A single confident number invites someone to attack the assumption; a range shows you know which assumption matters and pre-empts the question. Name the automation rate as the sensitive variable before anyone else does.
Then state what makes the number real: one named owner, a defined metric, and a go-live date. That's the part of the Audit → Build → Operate model that turns a projection into something anyone is accountable for.
The ROI calculator runs this arithmetic if you'd rather not build the spreadsheet, and what an AI agent costs gives you a real build number to divide by. If you're not yet sure the workflow qualifies at all, the five signs come first.
