How to choose your first AI use case

Most businesses have more candidate AI use cases than budget or attention. The skill isn't generating ideas. It's choosing the one most likely to pay off first. Here's a simple, honest way to do that, scored on what actually matters rather than what sounds exciting.

Score on three things: value, feasibility, and data

Almost every good first use case is strong on the same three dimensions. Rate each candidate from 1 to 5 on all three and you'll quickly see which ones rise to the top.

1. Value

How much does solving this actually matter? Look for the combination of frequency and pain: a small saving on something that happens constantly often beats a big saving on something rare. Be honest about whether the value is real and measurable, or just plausible.

2. Feasibility

How hard is it to do well? A use case where a human reviews the output before it's used (drafting, summarizing, flagging) is far more forgiving than one that acts on its own. Favor problems where a wrong answer is caught easily and cheaply, not ones where a mistake goes straight to a customer or a ledger.

3. Data

Does the data you'd need exist, can you access it, and is it good enough? A brilliant use case with no usable data is a research project, not a first project. A modest use case sitting on clean, available data will get you to a result far faster.

Then sanity-check the shortlist

Once you've scored candidates, run the top few past three more questions before you commit:

• Will people use it? A tool that fits how the team already works beats a better tool they have to go out of their way for.
• Can we measure it? If you can't tell whether it worked, you can't justify doing more.
• Is the downside contained? If it goes wrong, is that an annoyance or a headline? Start where the downside is an annoyance.

Put it in order

1. List your candidate use cases. Aim for five to ten.
2. Score each 1–5 on value, feasibility, and data.
3. Shortlist the highest combined scores.
4. Sanity-check adoption, measurability, and downside.
5. Pick one. Prove it. Then come back to the list.

The list isn't wasted after you pick. It becomes the start of your roadmap. Prove the first one, learn from it, and the next choice gets easier and better-informed each time.

In short

Don't pick the flashiest use case. Score honestly on value, feasibility, and data; check that people will use it, you can measure it, and the downside is contained. The winner is usually the unglamorous one with a clear before-and-after, and that's exactly the one to start with.