How to know when the wheels are coming off your AI project?

Not polished. Not production-ready. Just working.

If you can't do that, something's broken. And it's probably not technical.

At CES last week, where I presented my interactive training on prompt engineering, Presidential Science Advisor Michael Kratsios put it bluntly: the U.S. is investing hundreds of billions into AI infrastructure to enable companies to create real ROI. But massive infrastructure investment isn't enough. You have to apply it to real ROI. That's the key to winning the AI game.

He's right. But here's what I'm seeing on the ground:

I ran a 1-day Snowball Sprint workshop in Japan recently. We went from idea → framing → real RAG registry with voice and substitution → working agentic Python application.

One day.

Participants were beyond excited. Then I asked: "Are you going to do this at your company?"

The room got quiet.

"The nail that sticks out gets hammered down."

They'd need 6 months of internal approvals to run a 1-day POC.

The government is investing billions to enable ROI. But most organizations can't give themselves permission to capture it.

I wrote recently about a team that spent 9 months and $2M+ without anything demoable. The problem wasn't AI complexity—it was solving the wrong problem at the wrong layer: https://www.uxforai.com/p/when-engineers-say-it-s-beyond-your-understanding-here-s-what-they-actually-mean

The wheels come off AI projects when organizations can't move fast enough to learn.

Not permission to ship. Permission to try.

Full CES report dropping Thursday.