AI glossary for businesses
ROI of AI
In short
The ROI (return on investment) of AI compares the value an AI project creates with its full cost. Value can be time saved, extra revenue, fewer errors or faster service. Costs include licences, setup, training and the time people spend checking output. ROI is only credible when you measure it against a baseline, ideally in a pilot.
How ROI of AI works
The basic formula is simple: benefits minus costs, divided by costs. The hard part is counting both sides honestly.
Think of buying a dishwasher for a small restaurant. The benefit is not "a nicer kitchen". It is staff hours saved, fewer broken plates and faster table turnover. The cost is not only the price tag. It includes installation, water, electricity and maintenance. AI is the same.
Benefits usually fall into four groups:
time saved on a task, multiplied by the number of people and their hourly cost;
extra revenue, for example from better product pages or faster quotes;
fewer errors and less rework;
faster service, which can help keep customers.
Costs include licences or usage fees, setup and integration, training, time spent checking AI output, and ongoing maintenance.
In practice for a small business
A four-person support team at a Greek e-shop spends about ten hours each per week sorting and answering routine emails. Suppose a pilot with AI triage and draft replies cuts that by an estimated 30%. That frees about 12 hours a week across the team. Multiply by the hourly cost and compare it with the tool, setup and review time. If the numbers hold over the pilot period, you have a case to scale. These figures are only an example, so measure your own. See the email triage use case and the guide on how to measure ROI on an AI project.
Why it matters and what to watch out for
No baseline, no ROI. Measure the current process before you start.
Time saved is not money saved. Freed hours only pay off if they go to useful work.
Count review time. If staff spend a long time fixing AI output, the savings shrink fast.
Include risk costs. A data incident or a published error has a cost. Following GDPR and the EU AI Act is part of the real budget.
Watch usage fees. Pay-per-use tools can grow with volume. Track them monthly.