There's a temptation, when you've finally been convinced AI is worth investing in, to build the impressive thing — the autonomous research agent that drafts reports, the support bot that handles tier-one tickets end-to-end, the pipeline that ingests your entire knowledge base and answers anything. We've watched a lot of these projects, and we'll say it plain: they almost always cost more than they earn, take longer than promised, and produce something nobody trusts.
The agents that work in production tend to start embarrassingly small. They do one thing — summarize this kind of email, extract these three fields from this kind of PDF, draft a first-pass reply for a human to edit — and they do it on a tight loop with a real person sitting next to them. The person catches the failures, files them in a "bad outputs" folder, and the team improves the prompt or the data on the next pass. That feedback loop is the whole game. Without it, you're shipping a guess.
The reason this matters isn't that small is virtuous. It's that AI quality is non-obvious. You can't tell, looking at a tool that works five times in a demo, whether it'll be 99% accurate or 70% accurate at scale — and the difference between those two numbers is the difference between magic and a quiet liability that erodes trust until somebody pulls the plug. A small first agent forces you to build the evaluation muscle (what does "good" actually look like for this task?) before you build the spectacular one. By the time you ship the bigger thing, you know how to measure it, fix it, and improve it.
So the awkward truth: the most valuable thing your first AI project can do is give your team a clear, honest understanding of how AI fails in your context — what it gets wrong, where it gets stuck, how it surprises you. The flashy demo can come after. If you start with the flashy demo, you usually end with a tool nobody uses and a leadership team that's quietly skeptical of the whole category.