Everyone knows the adoption numbers are bad

Everyone knows the adoption numbers are bad.

Nobody's saying why they're actually bad.

60% of the workforce now has sanctioned AI tools. Only 11% of organizations have moved agentic pilots into production. That gap gets reported every week. What doesn't get said: most organizations are solving the wrong problem.

They're asking "which model should we use?"

That question is already obsolete.

This week OpenAI released pricing tiers that looked like a product announcement. They weren't. They were a blueprint for how AI systems are designed from here. A nano model at $0.20 per million tokens isn't priced to be your assistant. It's priced to run as a subagent inside a larger system, handling classification while a more capable model handles reasoning.

And the gap between 60% and 11% suddenly makes more sense. Organizations are still in "tool selection" mode while the underlying architecture has already shifted to orchestrated systems. It's not that people are resistant. It's that the question they're trying to answer ("which AI should my team use?") doesn't map to the problem anymore.

The blockers are real: data governance, legacy systems, a workforce that's uncertain rather than resistant. But those are management problems. They require organizational design thinking.

The companies that close that gap won't do it by finding a better model. They'll do it by figuring out which model plays which role, and building the systems around that.

I dig into this (and the rest of what moved this week) in the new episode of The Human in the Loop.

#AIAdoption #TechStrategy #TheHumanInTheLoop