AI ROI Calculator
What AI tooling actually returns once adoption, ramp-up and setup cost are in the model. Every calculator on this subject belongs to somebody selling AI, and all three of those assumptions are the ones they quietly skip.
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Every AI ROI calculator on the first page of results belongs to somebody selling AI. They assume every seat gets used from day one, nobody spends a week learning the thing, and setup was free. This one asks for all three, because those are the assumptions that turn a real number into a slide.
Seats you would be paying for.
Once they are up to speed. Be conservative.
Base pay across those people.
Payroll tax, benefits, equipment.
Of those with access, how many really use it.
Months before the full saving arrives.
One-off. Integration, migration, getting people trained.
Subscriptions plus token or usage spend.
At 70% adoption over a 3-month ramp, this returns 285%. On the assumptions a vendor calculator makes — everyone uses it, immediately, and setup was free — the same inputs would read 2500%. The honest version of the question is the other way round: each person needs to save 1.1 hours a week for this to wash its face, against the 4 you have assumed.
Hours saved is the input everything else depends on and the one nobody measures. If it is a guess, halve it and see whether the case still holds — that is the number a finance team will press on.
The saving only arrives if the work actually changes shape. Connect GitHub and Tekk turns what you want into specs your coding agent can execute against the real repo — which is where the hours go.
The model
hourly cost = salary × loaded multiplier ÷ 2,080
gross saving = people × hours saved per week × 52 × hourly cost
adjusted saving = gross saving × adoption
net benefit = saving over the horizon − setup − monthly cost × months
The first two lines are what every calculator on this subject does. The third and fourth are where the argument actually lives.
Worked example
Ten people, four hours saved each per week, $120,000 average salary at a 1.3 multiplier, 70% adoption, three-month ramp, $20,000 of setup and $500 a month in tooling and tokens.
| Step | Working | Result |
|---|---|---|
| Fully-loaded hourly cost | $120,000 × 1.3 ÷ 2,080 | $75 |
| Gross annual saving | 10 × 4 × 52 × $75 | $156,000 |
| After 70% adoption | × 0.70 | $109,200 |
| Total cost over 12 months | $20,000 + $500 × 12 | $26,000 |
| Saving realised (with ramp) | $100,100 | |
| Net benefit | $74,100 | |
| ROI | $74,100 ÷ $26,000 | 285% |
| Payback | 3.4 months |
What the vendor version says
Take the same inputs. Assume everyone uses it, from day one, and that setup was free:
| Realistic | Vendor assumptions | |
|---|---|---|
| Adoption | 70% | 100% |
| Ramp | 3 months | none |
| Setup cost | $20,000 | $0 |
| Saving over 12 months | $100,100 | $156,000 |
| Total cost | $26,000 | $6,000 |
| ROI | 285% | 2,500% |
Both numbers come from the same spreadsheet. The difference is entirely in three assumptions that no calculator asking for your work email is going to volunteer.
The question worth asking instead
Rather than "what is the ROI", ask how wrong could the estimate be before this stops working.
On the numbers above, the tool only needs to save each person about one hour a week to cover its own cost across the year — against the four hours assumed. The case survives being wrong by a factor of three.
That is a far more useful thing to put in front of a finance team than a percentage, because it is falsifiable and it is honest about the uncertainty.
Where this model is weakest
Hours saved is a guess. Nearly nobody measures it. Most published figures come from vendor case studies or from asking people how productive they felt, which is not the same question.
Saved time is not automatically money. An hour freed up only converts to value if it goes into work that was queued behind it. For an engineer with a backlog, it does. For someone with nothing waiting, the saving is real to them and invisible on the P&L. The model assumes redeployment, and that assumption deserves more scrutiny than the arithmetic.
Tooling costs move. Usage-based pricing is notoriously bad at staying where you put it. Use the worst month you have seen, not the average.
How it works
- 1
Start with the hours
People with access, hours each saves per week once up to speed, and what an hour of their time costs fully loaded. That last figure uses the same 2,080-hour convention as the other calculators here, so the numbers stay comparable across tools.
- 2
Then apply the three honest discounts
Adoption, because not everyone given a licence uses it. Ramp, because the saving arrives gradually. Setup cost, because integration and training are real money. This AI ROI calculator asks for all three and shows what the answer would be without them.
- 3
Read the payback, not the ROI
A percentage is easy to argue with. A month number — when the cumulative benefit crosses zero — is harder to dismiss and easier to check later. The chart plots it, starting below the line by whatever setup cost you entered.
Frequently asked questions
- How do you calculate ROI on AI tools?
- The AI ROI calculator above starts from hours saved multiplied by the fully-loaded cost of an hour, minus what the tooling costs, over a chosen period. Ten people saving four hours a week each at $75 an hour is $156,000 a year gross — before adoption, ramp and setup take their share.
- Why does this show two different ROI numbers?
- Because the assumptions matter more than the arithmetic. On the defaults, a realistic model returns 285% over a year. The same inputs with full adoption, no ramp and no setup cost — the assumptions most vendor calculators make — read around 2,500%. Neither is a lie; only one is defensible.
- What adoption rate should I assume?
- Lower than you would like. Handing out licences is not adoption, and 60–80% of seats in genuine weekly use is a good outcome for a tool people were not asking for. If you have telemetry from a pilot, use it; if you are guessing, guess low and let the number surprise you upward.
- Why does ramp-up matter?
- Because the saving does not start on the first Monday. People learn the tool, discover which tasks it is good for, and rebuild habits around it — and for the first month or two they are often slower, not faster. Three months to full effect is a reasonable planning assumption for a team tool.
- What counts as setup cost?
- Integration work, data migration, security review, the time spent choosing between options, and training. It is one-off and it is the line most often left out, which is why so many published payback periods are under a month. On the defaults, removing it alone changes the answer more than anything else on the page.
- How many hours does it need to save to be worth it?
- Less than you would think, and the tool computes it. On the defaults it is about one hour per person per week against the four assumed — so the case survives being wrong by a factor of three. That is the number worth quoting, because it reframes the question from "will this work" to "how badly could we be wrong and still be fine".
- Should I include token or usage spend?
- Yes, in the monthly cost field alongside subscriptions. Usage-based AI spend is famously bad at staying where you put it, so use the highest month you have seen rather than the average, and revisit it once real usage settles.
- Does an ai agent roi calculator work differently?
- The arithmetic is the same, but two inputs behave differently. An ai agent roi calculator should treat token spend as the dominant monthly cost rather than a rounding error, since agents consume far more per task than a chat interface. And ramp is usually longer, because an agent has to be given access, guardrails and a review process before anyone trusts its output.
- Is hours saved a reliable input?
- No, and it is the input everything else depends on. Almost nobody measures it; most figures come from a vendor case study or a survey of how productive people felt. The honest approach is to halve whatever number you first thought of and check the case still holds.
- Does saved time actually turn into money?
- Only if the freed hours go somewhere useful. An hour saved by an engineer with a backlog converts to output; an hour saved by someone with nothing queued converts to nothing measurable. This is the strongest objection to any AI ROI calculator and the model does not solve it — it assumes the hours are redeployed.
- What is a good payback period for AI tooling?
- Under six months is comfortable and under twelve is defensible for anything with a setup cost attached. Beyond that the assumptions are doing more work than the evidence, and by the time the payback date arrives the tooling landscape will have changed enough that you would be re-evaluating anyway.
- Should I model this per team or company-wide?
- Per team, then add them up. Adoption and hours saved vary enormously between functions — an engineering team and a support team will not converge on the same numbers — and a company-wide average hides the teams where the tool is not working.
- Do you store the numbers I enter?
- No. The calculation runs entirely in your browser. Nothing is sent to a server, nothing is logged, and there is no account — which is a meaningful difference from a vendor calculator that asks for your work email before showing the result.
- Why is this free, and what is Tekk?
- Tekk is a spec-driven development platform for people building software with AI coding agents. We are not neutral about whether AI tooling is worth it, which is precisely why this page shows the pessimistic model next to the flattering one. No signup, no run limit, no upsell inside the tool.
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