What to look for in your first deployment
A good first use case has several traits at once. It's repeatable — it involves something that happens daily or weekly, not once a quarter, because only then does the saving actually add up. It hurts — someone at the company is losing real time on it and knows it, so the solution has a ready audience from day one. The data for it already exists — it sits in a system or in spreadsheets, you don't have to start collecting it from scratch. And it's narrow enough to build and demonstrate quickly, before management's patience runs out.
That sounds less impressive than "we'll deploy AI to forecast our entire production." And that's the point. The first project isn't meant to dazzle — it's meant to work, get used, and give the company proof that it works. Only that proof opens the door to harder things.
The most common trap
The trap works like this: the most interesting idea is usually the most ambitious one, and the most ambitious one means the most unknowns. Lots of data that isn't in good shape yet. Integrations with multiple systems. An effect that only becomes visible after months. Each of these is a risk on its own, and combined they produce a project that easily stalls and is hard to defend to management once it starts dragging on.
The other side of the same trap is picking a process just because it's convenient, even though it doesn't really hurt anyone. The solution gets built, it works, and nobody uses it, because the problem it solved wasn't painful enough. The first use case has to hit the point where real pain meets available data.
Where to get that list
Sometimes the first candidate is obvious — one process is clearly crying out for support and you don't have to search long. More often there are several candidates, and it's hard to tell from the outside — sometimes even from the inside — which one will win. That's what a readiness audit is for: it goes through processes, data, and people, pulls out concrete ideas specific to that company, and ranks them by feasibility and expected return. Instead of guessing, you get a list ordered from most promising down. What the audit covers and when it makes sense is something I've laid out separately.
What's next
If you have several AI ideas in front of you and don't know which to start with, check each one against four things: is it repeatable, does it really hurt, does the data already exist, and can it be built quickly. Whichever passes all four is your first deployment, even if it's not the most interesting one. And if there are more candidates and the choice isn't clear, an audit will do that sorting for you.
→ See how the audit picks your first project
→ Book a 30-minute consultation
