AI Agent ROI Calculator
Work out what automating a task is actually worth — in hours, in money, and in how long it takes to pay back. It discounts the answer the way reality does, which is why the number it gives you is smaller than the ones you’ll see elsewhere.
- Before the recovery discount
- 936 hrs · $43,618
- Net after the cost of the automation
- —
- Payback period
- —
Summary you can paste anywhere
How it calculates
- Gross hours. Hours a week × 52 × the number of people doing the task.
- Gross value. Those hours × your fully loaded hourly cost. Loaded means salary plus benefits and payroll taxes — the default of $46.60 is the US private-industry average total compensation per hour worked, straight from the BLS.
- The recovery discount. Multiplied by a recovery rate, default 60%. This is the step almost every other calculator skips.
- Payback. Your annual automation cost ÷ the monthly value recovered.
Why the recovery rate exists
An agent that handles 80% of cases does not save 80% of the time. Someone still reads the queue, handles the exceptions, and checks the output while trust is being built.
Workday and Hanover Research surveyed 3,200 people in November 2025 and found that nearly 40% of AI time savings are lost to rework — correcting errors, rewriting, verifying. Hence the 60% default.
The most striking evidence is a randomised controlled trial by METR: experienced developers using AI tools were 19% slower — while believing afterwards that they had been 20% faster. A 39-point gap between what people feel and what actually happened. That is why self-reported savings need discounting, and why this calculator lets you push the rate down to 40%.
What this deliberately does not do. It does not pre-fill an automation cost. There is no independent published benchmark for what AI agent work costs — every range online is self-published by firms selling it, and they disagree with each other by a factor of twenty. Get a real quote and put that in.
It also excludes error and delay costs — the invoice paid twice, the lead that went cold over a weekend, the contract that auto-renewed. Those are often larger than the labour saving, and too specific to your operation for any generic calculator to guess.
A reality check on payback
Deloitte surveyed 1,854 senior executives in late 2025 and found most organisations report satisfactory ROI over two to four years. Only 6% achieved payback in under a year, and only 10% reported significant returns from agentic AI specifically.
If this calculator hands you a three-month payback, that is a good sign — but it is well outside what most companies actually experience. Sanity-check the inputs before anyone builds anything on the strength of it.
Every number on this page, and where it came from
Nothing here is an unsourced figure. Each default traces to a named study with a published sample size and method:
US Bureau of Labor Statistics · Released 12 June 2026 · Government statistics
Total employer compensation for private industry workers averaged $46.60 per hour worked. Wages and salaries averaged $32.60 (69.9%); benefits $14.01 (30.1%). Based on ~28,500 occupational observations from ~6,700 private establishments.
US Bureau of Labor Statistics · March 2026 reference period · Government statistics
Paid leave costs $3.54 per hour worked against wages of $32.60 per hour worked. ECEC is measured per hour WORKED, not per hour paid — leave hours are excluded from the denominator.
Slack Workforce Lab · Published 27 February 2024 · Vendor-published, independent fieldwork
Desk workers report spending 41% of their time on tasks that are 'low value, repetitive or lack meaningful contribution to their core job functions' — roughly two working days a week. Fielded by Qualtrics, 10,281 desk workers across six countries, 10-29 January 2024.
Workday / Hanover Research · Published 14 January 2026 · Vendor-published, independent fieldwork
Nearly 40% of AI time savings are lost to rework — correcting errors, rewriting content and verifying outputs. 85% of employees report saving one to seven hours a week with AI, but only 14% consistently get clear positive net outcomes. Fielded by Hanover Research, 3,200 respondents, November 2025.
METR · Published 10 July 2025 · Randomised controlled trial
Experienced developers were 19% SLOWER when using AI tools — while expecting a 24% speed-up beforehand and still believing they had been sped up by 20% afterwards. 16 developers, 246 real tasks. The only randomised controlled trial in this area.
Bain & Company · Published 1 June 2026 · Independent research
37% of companies targeted cost reductions of 11-20%, but nearly 40% of those who measured outcomes landed in the 0-10% bucket instead. 951 global companies across nine sectors.
Deloitte · Published 22 October 2025 · Independent research
Most organisations report satisfactory ROI over two to four years. Only 6% achieved payback in under a year, and only 10% report significant ROI from agentic AI specifically. 1,854 senior executives, fielded August-September 2025.
US Office of Personnel Management · 5 U.S.C. 5504(b) · Government statistics
Federal hourly rates are computed on a 2,087-hour year under 5 U.S.C. 5504(b). A GAO study found an average of 2,087 work hours per year across a 28-year cycle. The familiar 2,080 (40 x 52) is a convention rather than a published standard.
Numbers we deliberately did not use
- “An EY study found 30–50% of RPA projects fail.” Not a study. It traces to a 2016 EY whitepaper saying “we have seen as many as 30 to 50%” — a practitioner observation with no sample and no method, later relabelled a study in an op-ed by a competing vendor’s CEO.
- “MIT: 95% of GenAI pilots produce zero return.” The authors state the figures “reflect our interview sample of 52 organizations and may not represent broader market patterns.”
- McKinsey’s “28% of the week on email.” Published 2012, no disclosed methodology.
If a vendor quotes you any of those three, it is worth asking whether they checked.
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