Enterprise AI adoption isn’t a technology problem. It’s an incentives problem.
At the top of the organization, AI is all anyone talks about. Executives discuss it constantly. Board decks, strategy offsites, earnings calls. It’s framed as inevitable, transformational, existential. Something the company must do or risk falling behind.
One layer down, the VPs feel the pressure.
Their job is to translate executive urgency into something operational. So meetings multiply. Committees form. Roadmaps get drafted. Everyone talks about “use cases” and “enablement.” Progress is measured in conversations, not outcomes.
Then you get to the people actually doing the work.
The managers running teams. The individual contributors inside old systems, messy workflows, and brittle processes. And for them, AI doesn’t feel inevitable or empowering. It feels risky.
They don’t know how to use it. No one has shown them how in a way that maps to their actual job. The systems they work in weren’t designed for it. And most importantly, there’s no clear upside for them personally. If anything, there’s downside.
Using AI puts a spotlight on their work. It digitizes it. Measures it. Makes it legible in ways it wasn’t before. It can expose gaps in skills, judgment, or output. It can signal that their role is easier to automate than previously assumed. And it asks them to help train a system that may eventually reduce the need for their role at all.
From the perspective of the average employee, the incentives are backwards. You’re asked to be more efficient, but efficiency doesn’t come with higher pay. You’re asked to move faster, but speed doesn’t come with more security. You’re asked to help the company cut costs or increase leverage, but the benefits of that rarely flow downward.
So people hesitate. Not because they’re anti-technology. Not because they’re lazy. But because they understand the game they’re in.
If using AI means more work for the same pay, more scrutiny without more protection, and a higher risk of being replaced before the organization figures out how value gets redistributed, then quiet adoption is the rational choice. Use it privately. Don’t surface it. Don’t volunteer to redesign the system around you.
This is why enterprise AI feels stalled. The executives want transformation. The middle layers want alignment. And the people closest to the work are being asked to take the most risk for the least reward.
Until that changes, AI adoption will stay uneven and mostly invisible. Not because the tools don’t work, but because the incentives don’t.
And I’m looking to invest in things that help alleviate this problem! Because it’s not a matter of if, but when.
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