McKinsey’s The State of AI in 2025 report captures an interesting contradiction. On one side, almost every organization surveyed is using AI in some form. On the other, only a small fraction—roughly six percent—are actually capturing enterprise-level value. Most companies remain stuck in pilot mode, scaling cautiously, experimenting in silos.

However, there are a few companies out there that are rewriting how they operate altogether. McKinsey calls this small cohort the “AI high performers.” These organizations are ambitious—sometimes aggressively so—and the difference between them and everyone else is their mindset. They don’t think of AI as a department-level experiment or a collection of isolated tools; they think of it as a transformational capability that should touch every corner of the business.

According to the report, they’re three times more likely to have redesigned workflows, far more likely to have executives actively leading AI initiatives, and many dedicate more than 20 percent of their digital budgets to AI technology. (Read: they put their money where their mouth is.)

Which made me wonder, could success and failure in AI transformation really all boil down to the scale of one’s ambition? I’m sure it’s not quite that simple.

Then again, maybe it is.

Ambition as the Dividing Line

When you look closely at how high performers behave, you can see that they move beyond isolated use cases and think in terms of systems. Instead of asking how AI can support a single team, they ask what would need to change so AI can support the organization as a whole. They treat AI as a catalyst for rethinking processes, roles, and decision-making. They give teams permission to test, learn, and adapt. In short, they create the conditions where AI can actually matter.

Most organizations, by comparison, are still operating with narrower ambitions. They run responsible pilots, contain the risk, collect their findings—and often remain exactly where they started. The problem isn’t that these efforts are flawed, though. It’s that they rarely accumulate into transformation, because the organization never develops the muscle to operationalize AI at scale.

When leaders view AI as a series of experiments rather than a capability with strategic implications, their organizations tend to behave accordingly. They think smaller, act smaller, and ultimately achieve smaller outcomes.

Conversely, the companies treating AI as a fundamental shift are not waiting for perfect clarity before committing. They’re building clarity by committing. That is the difference.

So the question becomes less about technology and more about leadership. If an organization truly believes AI will reshape its industry, its operating posture should reflect that belief. And if it doesn’t believe that—or isn’t sure—then pilots and incremental moves may be all that’s appropriate. But those two positions shouldn’t be confused, and every organization has to take one.

Because in the end, AI results tend to rise—or fall—to the level of an organization’s ambition. Companies treating AI as a contained initiative are getting contained outcomes. Companies treating it as a chance to rethink how they work are already discovering what that kind of ambition makes possible.

In this moment, I posit that the risk isn’t overreaching. It’s assuming that small steps will somehow lead to large-scale change. They rarely do. Ambition, more than anything else, appears to be the dividing line.