The models worked.
The demos impressed.
The adoption failed anyway.

In this episode, we unpack why AI initiatives collapse not because of algorithms, but because no one is clearly responsible once the excitement fades. Ownership gaps, blurred accountability, pilots with no operational future, and organizations that confuse experimentation with deployment.

This isn’t a story about broken technology.
It’s a story about humans outsourcing responsibility to systems they barely manage.

If AI adoption keeps failing, the real question isn’t what the model got wrong.
It’s who was supposed to own it when things became real.

And as always:
who approved today’s AI news?