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Slava Kotenev
Slava Kotenev

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Back-of-envelope ROI for automation: a formula and a worked example

#ai #automation #business #productivity

Most automation projects get approved on a feeling: "it will save a lot of time". A one-page calculation does better, and it takes ten minutes. Below is the formula I would use, a worked example, and the inputs that most often make it wrong.

All numbers in the example are illustrative, invented for the arithmetic. They are not a client result.

The formula 

monthly_saving = volume × share_automated × minutes_saved_per_item / 60 × hourly_cost
monthly_cost   = running_cost + review_cost + error_cost
net_monthly    = monthly_saving − monthly_cost
payback_months = build_cost / net_monthly
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Four of these terms hide the real work:

  • share_automated is not a wish, it is a measured number from a pilot (for example, the share of items that clear a confidence threshold at an agreed accuracy).
  • review_cost is the time people still spend checking what the system hands back. Forget it and the saving is overstated.
  • error_cost is (wrong automated items per month) × (cost of fixing one). It is the term that punishes a threshold set too low.
  • minutes_saved_per_item comes from timing the manual process on a real sample, not from a guess.

A worked example (illustrative numbers)

Take a process with 2,000 items a month, 6 minutes each by hand, at a loaded cost of $25 per hour.

  • Manual cost today: 2,000 × 6 / 60 × 25 = $5,000 per month.
  • Suppose a pilot shows 75% of items can be automated at the agreed accuracy, and each automated item still costs 0.5 minutes of spot-check.
  • Saving: 2,000 × 0.75 × (6 − 0.5) / 60 × 25 = $3,437 per month.
  • Running cost (API, hosting, monitoring time): $400 per month.
  • Errors: 1.5% of 1,500 automated items = 22 wrong, at $15 each to fix = $338 per month.
  • Net: 3,437 − 400 − 338 = about $2,700 per month.
  • If the build costs $8,000, payback is about 3 months.

Stacked layers: saving, running cost, errors

What breaks the calculation

  1. Volume that is smaller than it feels. Count last quarter's real items. A process that "never stops" is often 200 items a month.
  2. A baseline nobody measured. If you don't know the 6 minutes, the whole saving is a story.
  3. Ignoring the exception path. The 25% that goes to humans is usually the hard 25%, and it may take longer than the average item did before.
  4. Treating the build as the only cost. Monitoring and re-tuning continue after go-live.
  5. Counting saved hours as saved money. If nobody's payroll changes, the real benefit is capacity, speed or fewer errors. Say which one you mean.

Sensitivity beats a single answer

Re-run the sum with automated share at 55% and error cost doubled. If the project still pays back within your tolerance, it is robust. If it only works at your best case, you have learned that for the price of a spreadsheet.

Takeaway

Put volume, baseline, measured automation share, review time and error cost on one page before any build starts. If the numbers don't clear your payback threshold, the right answer is "not this process", and that is a good outcome for ten minutes of arithmetic.

Portfolio and contacts: @Kotenev01_bot.

I write about practical, measurable AI adoption for business: demos on test data, no invented case studies. Telegram channel: @kotenev01_en

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