AI Automation Payback Calculator (Process ROI)
Pick one repetitive process — invoice entry, ticket triage, bookkeeping categorization, data extraction — and enter how often it runs, how long a person takes per task, what share of tasks the automation can actually handle, and the human review each automated task still needs. The calculator converts the time you genuinely reclaim (after the review haircut) into money at your loaded hourly cost, subtracts per-task AI costs and the platform fee, and tells you how many months of that net saving it takes to recover the one-off build cost. If running costs eat the whole saving, it says the project never pays back — instead of printing a hopeful number.
The process today
The automation
What it costs
Net monthly saving
$3,855.33
$4,083.33 gross − $228.00 running costs / month
Payback
3.89 mo
Hours freed / mo
116.67
First-year ROI
208%
Savings & payback detail
Cumulative recovery vs build cost
Compare scenarios
Run the same calculation with two or three input sets side by side. Differences are highlighted; every number comes from the same tested formula as the calculator above.
| Input | Scenario A | Scenario B |
|---|---|---|
| Tasks Per Month | ||
| Minutes Per Task Manual | ||
| Loaded Hourly Cost | ||
| Automation Coverage Pct | ||
| Review Minutes Per Task | ||
| Ai Cost Per Task | ||
| Platform Cost Mo | ||
| Build Cost |
How it works
Hours freed per month = task volume × coverage × (manual minutes − review minutes) ÷ 60. Coverage is the honest share of tasks the automation handles end to end — 70% by default, because 100% is rare in practice. Review minutes are the haircut most ROI models skip: every automated task a human still checks gives some of the saving back. At the defaults, 2,000 tasks × 70% = 1,400 automated tasks, each saving 6 − 1 = 5 minutes, or 116.67 hours a month. If review time reaches or exceeds manual time, the calculator flags it explicitly: at that point automation destroys value rather than creating it.
Net monthly saving = hours freed × loaded hourly cost, minus running costs. The $35/h default tracks average hourly earnings for all US private-sector employees (the FRED CES0500000003 series prints roughly $36/h) — replace it with the loaded cost of whoever actually does the task. Running costs are the per-task AI cost × automated volume plus the monthly platform fee. In the example: 116.67 h × $35 = $4,083.33 gross; 1,400 tasks × $0.02 + $200 platform = $228 of running cost; net saving $3,855.33 a month.
Payback months = one-off build cost ÷ net monthly saving: a $15,000 build recovered at $3,855.33 a month pays back in about 3.9 months. First-year ROI = (12 × net monthly saving − build cost) ÷ build cost — 208.4% in the example. If the net monthly saving is zero or negative, payback is reported as never: no number of months recovers the build when each month adds nothing. The chart plots cumulative recovery against the build-cost line so you can see the crossing point — or see that there isn't one.
Frequently asked questions
How is this different from the AI Tool ROI calculator?+
The AI Tool ROI calculator models a team adopting a tool: hours saved per person per week, times headcount. This one models a single process at the task level — volume × handling time × coverage, minus the human-review haircut and the per-task inference cost. They answer different questions: 'is this tool worth rolling out to the team?' versus 'does automating this specific process pay for its build?'. Use both — a tool that clears the team-level bar can still hide individual processes that don't clear this one, and vice versa.
What should I put for AI cost per task?+
For LLM-based automation, it is tokens per task × the model's price per token — our LLM API Cost Calculator gives you that per-call figure to paste here. A typical document-processing call costs a cent or two; complex multi-step agents can cost far more, which is exactly why the input exists. For fixed-fee RPA or SaaS automation with no per-call metering, set it to 0 and put the whole fee in the monthly platform cost instead. Do not leave it at zero for metered APIs: at high task volumes, per-task cost is often the difference between a great payback and no payback.
Why does review time matter so much?+
Because it erodes the saving linearly, task by task. Every automated task that still needs a minute of human review gives that minute straight back: at 6 manual minutes and 1 review minute you keep 5/6 of the saving, at 3 review minutes only half. Once review time reaches or exceeds manual time, the 'automation' costs more human time than doing the work by hand — plus the API and platform bills — and the calculator warns you rather than showing a quiet negative. The most common automation-ROI mistake is assuming 100% coverage and zero review; the defaults here deliberately assume neither.