Methodology: how the calculator works

By Lawrence Goh, Director, Autonex Pte Ltd · Updated 23 Oct 2026

The AI Automation ROI calculator is deliberately simple and transparent. This page sets out every formula and assumption so you can check the results, adapt them, or rebuild them in a spreadsheet.

Inputs

InputMeaningDefault
PeopleNumber of people who do the selected tasks5
Hourly costFully loaded cost of one hour of their time40
Working weeksWeeks worked per year after leave and holidays46
Task hoursHours per person per week on each selected task13 in total
Time saved %Reduction in time on those tasks when AI-assisted35%
Adoption %Share of people who actually use the tool80%
Ramp-upMonths during which benefits are counted at 50%2
Setup costOne-off cost: design, integration, data cleanup, training, pilot5,000
Licence per userMonthly tool cost per active user30
Other running costsMonthly API usage, maintenance, support150

Formulas

Active users = ceil(people × adoption) Hours saved / week = Σ task hours × people × time saved × adoption Hours saved / year = hours saved per week × working weeks Value of time / yr = hours saved per year × hourly cost Running cost / yr = (licence × active users + other running costs) × 12 Year-1 value = value of time × (12 − 0.5 × ramp-up months) ÷ 12 Year-1 ROI = (year-1 value − running cost − setup) ÷ (running cost + setup) Years 2–3 net = value of time − running cost (each year) 3-year net value = year-1 net + year-2 net + year-3 net Payback = first month in which cumulative (monthly value − monthly running cost) ≥ setup cost, with monthly value halved during ramp-up

Key assumptions and limits

Why the default time savings are 35%

Controlled studies of AI assistance on specific work tasks have found average gains of about 14% (customer support) to 25% (consulting tasks), with larger gains for less experienced workers, and a slowdown of 19% for experienced developers in one trial. Highly repetitive tasks — formatting, extraction, routine drafting — can see much larger savings. A moderate 35% default on targeted repetitive tasks, with 20% and 50% presets either side, reflects that range. The evidence is summarised in what the research says.

Privacy

All calculations run in your browser. Nothing you enter is sent to us. If you use "Copy link", your inputs are placed in the page address so the scenario can be shared.

Changes and corrections

We review the defaults when significant new research is published. If you think a formula or source is wrong, please tell us.

Sources

  1. Brynjolfsson, Li & Raymond, "Generative AI at Work", NBER Working Paper 31161 (2023)
  2. Dell'Acqua, McFowland, Mollick, Lakhani et al., "Navigating the Jagged Technological Frontier", Harvard Business School working paper (2023), as reported by The Harvard Crimson
  3. Bick, Blandin & Deming, "The Impact of Generative AI on Work Productivity", Federal Reserve Bank of St. Louis (Feb 2025)
  4. Bick, Blandin & Deming, "The State of Generative AI Adoption in 2025", Federal Reserve Bank of St. Louis (Nov 2025)
  5. METR, "Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity" (July 2025)