To choose your first AI use case, give each process a score from 1 to 5 on five criteria: volume, clear rules, current cost, error tolerance, and data availability. Add the scores and rank the processes; the highest total, validated with a small POC, is your first case. A good first case is repetitive, measurable, and easy to correct.
- In short: you don't pick your first AI use case by how impressive it sounds, but by a score across five criteria — volume, clear rules, current cost, error tolerance, and data availability.
- A good first case is boring: repetitive, measurable, with clear rules and a visible cost you cut.
- Error tolerance is the most often ignored criterion — a first case where a mistake is costly is a trap.
- If you have no data about a process, it cannot be your first case, however tempting it is.
Why does it matter which AI use case you choose first?
A company's first AI project is the evidence on which leadership decides whether to invest further or not. Pick a case that is too ambitious, with unclear rules and no data, and you risk failing visibly and blocking any AI initiative for years. Pick one that is too small and you succeed, but nobody notices. The right case sits in the middle: important enough that the result matters, contained enough that you can deliver and measure it. In our projects, the first successful case changes the internal conversation — from "is it worth it?" to "what do we automate next?". That is why it is worth choosing with method, not enthusiasm.
Market context helps here: according to data cited by StartupCafe, only around 5% of Romanian companies use AI, while roughly 66% of SMEs say they want to. The gap is not about willingness, but about firms not knowing where to start. A well-chosen first case is exactly the answer to "where do I begin".
What criteria do I use to score each use case?
You list all candidate processes and give each a score from 1 to 5 on five criteria. You add up the scores; the case with the highest total is your candidate. It is not exact science, but it forces an honest conversation about what is realistic, not just what sounds good. The table below explains each criterion and what a low versus a high score means.
| Criterion | What a low score (1) means | What a high score (5) means |
|---|---|---|
| Volume | The process happens rarely, a few times a month | It repeats hundreds or thousands of times |
| Clear rules | Needs human judgement hard to spell out | The rules can be stated explicitly |
| Current cost | Costs little time and money today | Consumes a lot of costly, measurable time |
| Error tolerance | A mistake has serious consequences | A mistake is easy to catch and correct |
| Data availability | You have no data, or it's scattered and dirty | You have clean, structured, accessible data |
A detail that often surprises: error tolerance matters more than it seems. A process with high volume and clear rules, but where a single mistake can cost a client or a fine, is a poor first case — the risk cancels the benefit. Better to start with a process where the model proposes and a person confirms, so you build trust before fully automating.
How do I turn the scores into a ranked shortlist?
You add the five scores for each process and order them top down. Processes with a total of 20 or more are strong candidates for the first case. Those between 15 and 19 are possible, but look at which criterion drags them down — if it is "data availability", the first step is to gather the data, not jump straight to a model. Processes under 15 you leave for later. A single score of 1 on "data availability" or "error tolerance" is usually enough to drop a candidate, however good the rest looks — those are the two criteria that, on their own, can make a project fail.
Examples of processes that often score well: answering repetitive customer questions from the company's documentation, extracting data from invoices or contracts, classifying and routing incoming e-mail, auto-filling standard reports. All have volume, reasonably clear rules, and an easy-to-catch mistake. For a broader view of how the first case connects to an adoption strategy, see our guide on how to implement AI in your company.
What is the next step after choosing the first case?
Once you have a ranked shortlist, you validate the top candidate with a small POC before any serious investment — a short test that confirms it is feasible on your real data. If you want a second opinion on the shortlist, or you are not sure how to score your own processes, book a free initial call with the Sapio team. We go through your list together and decide which case deserves the first POC. The initial call is free; if the project needs a deeper assessment, the AI Technical Audit follows (our paid service, 2–4 weeks), but we never jump straight to it without a first conversation.
Only around 5% of Romanian companies use AI, while roughly 66% of SMEs say they want to — a gap reported by StartupCafe, based on adoption data.
Frequently asked questions
What is the best first AI use case for a company?
The one with the highest score across the five criteria: high volume, clear rules, measurable current cost, error tolerance, and available data. In practice, good first cases are boring — answering repetitive questions, extracting data from documents, classifying e-mail. They are repetitive, measurable, and a mistake is easy to catch and correct.
How many criteria do I use to evaluate an AI use case?
Five: volume, clear rules, current cost, error tolerance, and data availability. You give each a score from 1 to 5 and add them up. Processes with a total of 20 or more are strong candidates. A single 1 on "data availability" or "error tolerance" is usually enough to drop a candidate, however good the rest looks.
Can I start with AI if I don't have organised data?
Not for that case. If a process scores 1 on "data availability", the first step is not the model but gathering and cleaning the data. Meanwhile you can pick another case from the shortlist where data already exists. Without relevant data about a process, no model produces useful results, regardless of budget.
Why does error tolerance matter for the first case?
Because an AI model is sometimes wrong, and on the first project you want to build trust, not risk a disaster. A process where a single mistake costs a client or a fine is a poor first case, even with high volume. Better to start with one where the model proposes and a person confirms.
Where do I start with AI if my company has never used it?
Start with an inventory of repetitive, costly processes, then score them on the five criteria in this article. Pick the highest-scoring candidate and validate it with a small POC before any investment. The market gap — around 5% of firms use AI, but ~66% want to — comes precisely from missing this structured first step.
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