Most organizations do not have an AI adoption problem anymore. They have a value realization problem.
By 2025, AI use has become common. McKinsey reports that 78% of organizations now use AI in at least one business function. But that number is misleading if it is treated as proof of progress. In the same research, more than 80% of respondents say their organizations are not seeing tangible enterprise-level EBIT impact from generative AI.
That gap matters.
It suggests many organizations are not yet solving the right problems in the right way. They are experimenting, launching pilots, and adding tools, but often without enough discipline around value pools, ownership, workflow fit, and operating readiness. AI gets deployed because it is available, not because the organization has clearly identified where it can create measurable value.
This is why AI activity is such a poor proxy for AI progress. A company can have many pilots and still lack:
- the right use cases
- clear economic logic
- named business ownership
- a path into real workflows
- post-go-live value tracking
The real question is not, “Where are we using AI?” It is,
“Where will AI change outcomes in a way the business can actually capture?”