How to Measure ROI from AI Implementation
In the press and at conferences, you periodically hear claims like this: some company implemented AI and saved or earned colossal amounts — anywhere from tens of percent to multiple-fold growth. I’ve read claims like this many times, but I have never once seen anyone show how exactly that number was calculated.
I’ve been implementing AI and developing complex, high-load IT systems for about 25 years, and I know that claims like this deserve careful scrutiny. Here’s why.
When I see a number like that, the question that immediately comes to mind is: did they account for infrastructure? How much will you pay Amazon, Google, or Microsoft for cloud per month, or how much will your own servers cost? How much will model training cost? How much data will that require, and how much time and money will go into preparing that data the right way?
I have never once seen things like this included in the calculations, and yet this is a significant part of the cost of implementation.
Implementing AI and getting ROI are not the same thing. There is no direct link between these two events unless someone has calculated everything in advance and factored all the critically important components into the project’s economics.
The AI ROI calculation itself comes down to three specific numbers: how much the process costs right now, what its real improvement potential is, how much it will cost to develop, implement, and operate the solution. Without any one of the three, this isn’t a forecast – it’s a lottery.
What does ROI from AI implementation actually consist of?
The AI ROI calculation rests on three numbers. If even one is missing, it’s already fantasy.
The first number is how much the process costs right now. The second is what its real improvement potential is, without optimistic assumptions. The third is how much implementation will cost.
Let’s take a very simple, illustrative example. Say some all-inclusive hotel chain has 100 hotels in its network, and each hotel throws away, say, 70 kilograms of food per day that guests didn’t eat – and that’s a very optimistic estimate.
| Metric | Value |
| Hotels in the chain | 100 |
| Food wasted per hotel/day | 70 kg (optimistic estimate) |
| Average cost per kg | €7 |
| Loss per hotel/day | ~€500 |
| Loss across the chain | Millions of euros per year |
And this number still isn’t precise, because you need to add the cost of food waste disposal and the cost of energy spent preparing the discarded dishes. Once we add that data, we get the first number precisely.
Next you need to understand the real potential for reducing these losses: not “we’ll optimize everything,” but actually try to understand what can even be controlled in all-inclusive hotel restaurants and how these losses could be reduced. Account for guest demographics? Track which dishes are ordered more often? Survey guests at check-in? Track how much is thrown away and optimize the menu? There can end up being many options, and it’s entirely possible you’ll need to take several and implement them together, or bet on one particular option.
Only after that can you move on to calculating the third number. You need to understand, at least at a high level, how the system will be structured, and roughly estimate the infrastructure cost.
The ROI calculators I’ve come across most often ask what industry your business operates in, maybe ask for a few numbers, and then give you incomprehensible industry-wide figures – most often gathered by an LLM from the internet, at best with some calculations added on top. Not a single question about the specifics of the business, not a single requested metric or benchmark – just averaged data, pulled out of thin air, so to speak. You cannot rely on forecasts like that.
The unit-economics structure of a premium restaurant, a fast-food chain, and a delivery-only dark kitchen are fundamentally different. Literally everything about them differs – different cost structures, production intensity, food cost, payroll. And each type of establishment has its own bottleneck: for one it’s the volume of waste, for another it’s the speed of production and order processing.
Based on everything above, we can conclude that the potential ROI from investing in AI solutions can only be calculated with real precision after a deep analysis specific to each individual business, based on its own indicators. Moreover, for the most accurate estimates, the correct approach is to build a prototype of the chosen solution and see what real results it produces, and only then decide where and how to move forward – refining all the figures before full-scale implementation of the solution.
Where companies most often go wrong calculating ROI themselves
The most common mistake in ROI calculations is misjudging the cost of implementation.
Companies treat this superficially: they estimate roughly the cost of development itself and an approximate infrastructure cost, but factor in only superficially – or not at all – things like model training, data preparation, staff training, scaling the system across multiple locations (where applicable), and the time needed to roll out the system.
The second common mistake: being overly optimistic about the improvement potential, especially when there hasn’t been a prototype stage to actually test the ideas and measure real numbers in practice.
Many people look at the task with excessive optimism, and then are equally surprised when the numbers turn out to be several times more modest in practice.
Very often the projected figures diverge sharply from practice, and before spending money on development and implementation, the correct approach is to run deep calculations and modeling, develop prototypes of several solution variants, run testing and data collection, run the analysis and modeling again, and only after that make decisions about allocating significant budgets for development and implementation. In large companies with many locations – as in the example with the all-inclusive hotel restaurants – the cost of implementing, maintaining, and operating various solutions can become a very noticeable expense line, directly affecting the payback and economic efficiency of the solution. There are other expense items that companies systematically leave out of the ROI calculation – more on those separately.
Hidden costs that only become visible after launch
Even a correct calculation doesn’t guarantee the numbers will hold up after launch. On paper, you’re calculating the process as it looks before anyone has actually gone through it. But as soon as the system is rolled out in practice, things surface that simply weren’t visible at the calculation stage: process changes, retraining the team, who deploys the solution on-site and how.
It’s not a matter of the quality of the calculation. It’s that some of the variables can only be known in practice. And every case in business is highly unique, so successful experience across dozens of other projects tells you nothing about how a new one will go. Nobody knows in advance how a team will actually integrate a new process into their work, how long retraining will take, what will go wrong during rollout, and what adjustments will be needed during operation. All of this only shows up after launch.
Why honest AI payback is always a range, not an exact number
The payback of an AI implementation – or of implementing any new IT system – can never be expressed as a single number, and that’s not a sign of incompetence. Behind it lie real uncertainties: there are too many unknowns and variables in the equation, and they change over time.
Today a material costs one price; in two months it’s up 50%, or three times more expensive. We calculated the savings at raw material price X. If the price changes, all the calculations look different too. For example, we learned to save on raw materials, and then the material got 20% more expensive – then the savings become even more significant. If, on the other hand, it got cheaper, it might turn out that the implementation cost wasn’t worth it. The same goes for new expenses. For example, average market pay for the staff involved in the process goes up, and the savings no longer look as convincing.
That’s exactly why payback is always a range – a low, medium, and high scenario. Not a single number that a vendor neatly presents on a slide.
If a calculator or a vendor gives you exactly one number, that’s a clear signal of a lack of data behind the calculation.
How to calculate the payback and potential ROI of AI implementation in your own business
You need to get the first number: how much the process costs right now. Most often a company already has this – it’s just scattered across different reports.
Second, you need to assess the improvement potential. Assess it honestly and without inflating it, based on what can realistically be changed in the process – not on wishes.
Third, the cost of implementation – at the start, this can only be estimated approximately, but even a high-level estimate of the system architecture, what equipment and servers will be needed, model training, and staff, already gives a reasonably accurate figure.
If a company doesn’t have precise data on the current cost of the process, that’s not a reason to stop. Gathering that data is the first step, and it can be done in parallel with developing a prototype: a small pilot on a limited part of the process shows whether the idea itself works, even before the full economics have been calculated. From there, run the next round of calculations and modeling based on the prototype.
We specifically designed our ROI calculator so that it doesn’t calculate or invent numbers on its own, but takes real data, supplements it with information using an LLM, and then performs a calculation as close as possible to the specific business.
Conclusion
Payback from AI implementation doesn’t happen on its own. ROI comes down to three numbers: how much the process costs right now, what its real improvement potential is, and how much implementation will cost. Without one of the three, it’s not an ROI calculation – it’s a guess. What’s more, the most accurate calculations can only be obtained after developing a prototype of the solution and collecting real data, followed by refining the calculations.
Calculate your three numbers. If you don’t have the data for that yet, start with a small pilot on one part of the process – it will show whether the idea itself works, even before ROI has been fully calculated.
If you need help: contact me via SOFYCOD corporate web site Contact Author, and I can consult with you and help calculate the payback potential and possible ROI from AI implementation.