Why most AI projects stall

The pattern

A team gets excited about AI. They pick a tool, run some demos, get impressive results in a sandbox. Then they try to plug it into the real workflow and it doesn't quite fit. They adjust. It fits better but introduces a new problem. Three months later the project is "ongoing" and nobody's sure what it was supposed to ship.

This isn't an AI problem. It's a problem definition problem.

What's usually missing

The demos worked because the demo inputs were clean and the success criteria were vague. Real workflows have messy inputs, edge cases, and people who need to trust the output before they'll act on it. If you haven't defined what "good" looks like in concrete terms before you start, you can't tell when you're done — so you never are.

What works instead

Start with one workflow, one input type, one success metric. Get the AI doing that one thing well enough that a real person would hand it off and not look back. Then expand. The projects that ship are almost always narrower than the original vision and faster to value because of it.

The honest version

Most AI projects stall because someone convinced themselves that the hard part was the technology. The hard part is the same as it's always been: knowing exactly what problem you're solving and for whom.