Most AI proofs of concept die. Here’s the one thing that saves them.

Most AI proofs of concept die because nobody defined what "works" means before building. Not because the model was bad. Not because the data was dirty. Because the team skipped the one conversation that matters: what does success look like, in numbers, before we write a line of code.

The pattern we keep seeing

A team gets excited about an AI use case. They spin up a model. They get 90% accuracy on a test set. Everyone celebrates. Then six months later the project is shelved because nobody can explain what it changed.

We’ve seen this play out at three different companies in the last year alone. The model works. The business case doesn’t.

Define "works" before you build

Before any PoC starts, answer three questions:

1. What metric moves? Not "we’ll save time." How much time? For whom? Measured how?
2. What’s the baseline? If you don’t know the current state, you can’t prove improvement.
3. What’s the kill threshold? At what point do you walk away? Decide this before you’re emotionally invested.

A logistics company we worked with defined their success metric as "reduce invoice matching time from 47 hours/week to under 10." That’s specific. That’s measurable. That’s the kind of statement that either proves out or doesn’t.

Why teams skip this

Because it’s uncomfortable. Defining success means accepting you might fail. It’s easier to start building and figure it out later. But "figure it out later" is how you end up with a model nobody uses and a budget nobody wants to explain.

The counterpoint

Yes, sometimes you genuinely don’t know what’s possible until you build something. Exploration has value. But even exploratory work needs a decision framework: "If we discover X, we proceed. If we discover Y, we stop." That’s not a constraint. That’s discipline.

The short version

Define your success metric, your baseline, and your kill threshold before you build anything. Everything else is expensive guessing.