How to choose your first AI use case

Most first AI projects fail for a simple reason: they start with a model or a tool, not with a problem the business already feels. The best first use case is rarely the most exciting one. It is the one that is frequent, measurable and owned by someone who wants it fixed.

AI adoption starts with the right problem

Before you look at vendors or models, list the work your team repeats every week. Look for tasks that follow a pattern, rely on information you already have and cost real hours or real money when they go wrong.

Start with repetitive work

Repetitive work is where AI earns trust fastest, because the before-and-after is easy to see. Good candidates usually look like this:

• Reading and routing incoming emails or tickets

• Answering the same customer questions

• Moving data between systems

• Checking documents against a checklist

• Drafting first versions of reports

Put a number on the problem

Estimate how many times the task happens, how long it takes and what an error costs. If you cannot put a rough number on it, you will not be able to prove the project worked.

Potential impact = time saved + cost of mistakes avoided + revenue the team can now chase.

Check whether AI is actually necessary

Some problems are better solved with a clearer process or a simple automation. AI is worth it when the task needs judgement over messy inputs like text, images or conversations.