How AI Adoption Works (or Doesn’t)

Fewer than 10% of enterprises have scaled AI into production, according to McKinsey.

Their proposed fix?

More McKinsey, in the shape of a new partnership with an AI startup to help companies move "from experimentation to production-grade deployment."

The assumption is that the gap between experimentation and scale is a deployment problem, which means if you connect the right systems and stand up the right governance, behavior will follow.

Right?

Well, not really. That exact assumption is the reason the 70% transformation failure rate exists.

Every enterprise tech cycle before AI followed the same pattern:

CRM goes live with full executive sponsorship → adoption metrics hit 90% → dashboards turn green → and six months later sales teams are running shadow spreadsheets because the system was more designed for governance than for how deals actually progress.

After working with 500+ clients, I can tell you the failure point is never the tech “deployment”, but actually getting people to want to work differently inside whatever you just deployed.

No platform integration solves that.

Companies keep treating the distance between "people have access to the tool" and "people change how they operate" as a challenge that the next technology and systems integration partnership will close.

The real challenge is a mindset and behavioral one; its about human adaptation.

If nothing changes, AI is going to follow the same path as most of the SaaS deployments over the past 20 years.

The companies that actually scale AI will be the ones who figured out how to get their people to want to work differently, and stick to it.

So before signing the next deployment contract, the question worth sitting with is whether your last enterprise rollout actually changed how the organization operates or just made individuals slightly more productive inside the same broken processes.

If the honest answer is the second one, more McKinsey isn't going to fix that.