AI Automation ROI Calculator
The return on automating a repetitive task with a language model — labour hours saved against API cost, review time and the build — with payback and the volume at which it starts to pay.
Automation saves the minutes a person spent per task, less the minutes someone now spends reviewing the model's output, times the volume.
How the ai automation roi calculator works
Automation saves the minutes a person spent per task, less the minutes someone now spends reviewing the model's output, times the volume. Against that: the model's cost per task, the one-off build, and ongoing maintenance. The net figure per month and the payback on the build are what a business case needs.
The review share is the honest part: a model that is right 95% of the time still needs a human on the 5%, and the reviewer has to look at everything to find them.
Formula: monthly saving = tasks × (minutes saved − review minutes) / 60 × rate − tasks × model cost − maintenance; payback = build / monthly saving
Worked examples
| Inputs | Net saving per month | Note |
|---|---|---|
| 8,000 tasks a month, 6 minutes each | 35,707 | pays back in about a month |
| Heavy review | 6,373 | most of the saving gone |
| Low volume | 569 | a long payback |
FAQFrequently asked questions
How do I value the hours saved?
At the loaded hourly rate of the people who did the task — wages plus on-costs. If nobody is actually redeployed or hired less, the saving is capacity, not cash; say which.
Why count review time?
Because someone has to check the output, and for most tasks they check all of it. A minute of review per six-minute task is optimistic; five is common early on.
What is a good payback?
Under a year is easy to approve; under six months is compelling. Beyond eighteen months the assumptions, especially volume, deserve scrutiny.
What does the model cost per task include?
Input and output tokens at the provider's price, times the calls per task — retries and multi-step chains count. The token-cost page gives it.
What is missing from this?
Quality effects — faster turnaround, fewer errors, or new mistakes — and the change-management effort. They are real; they are just not on the invoice.
Where these figures come from
- Vaswani et al. (2017) — Attention Is All You Need — the transformer architecture the memory arithmetic follows
- Kaplan et al. (2020) — Scaling Laws for Neural Language Models — the compute relationship used for training estimates
- Hoffmann et al. (2022) — Training Compute-Optimal Large Language Models — the tokens-per-parameter guidance ("Chinchilla")
- IEEE 754 — Standard for Floating-Point Arithmetic — the numeric formats behind bytes per parameter
- NIST AI Risk Management Framework — the US federal AI framework
Last checked: September 2026. The relationships here are architectural, not vendor-specific: bytes per parameter follow the numeric format, KV-cache size follows the transformer definition, and token-per-word ratios come from published tokeniser behaviour.