Enterprise AI impact isn’t about the most powerful model, but about the smartest steering.

09.28.2026

Author - Enrique Dans

As a CEO, would you give a brilliant employee total freedom with no objectives, no constraints, no supervision, and no feedback?

This is something you have probably noticed several times. When you use an AI chatbot for personal questions, decision-making and research it works amazingly well. But when you try your company’s AI… well, the results tend to be much less impressive, unless we are talking about purely “administrative” uses. 

That’s because when you use it, you yourself are doing the steering, the corrections, the reframing, the “forget about this, give me more of that,” until you decide that the answer is good enough. Doing all that supervisory work is pretty much invisible, because it feels natural, like when you do it with a coworker or a subordinate. 

But when you switch from personal help to running a process, the steering function has to appear someplace else, and that’s not necessarily easy. The move from assistants to human-agent teams, and later to “human-led, agent-operated” workflows is much more problematic, much less natural, and generates many more issues. 

The essence is in the nature of the roles: while copilots propose, humans typically judge. That creates an essentially different managerial concern: Who checks what? Is this sequence of actions still heading toward the outcome we wanted? 

Right now, I believe that the main challenge is no longer to prove that agents can work, but to provide them with the layer of reliability that high-value workflows require: policies, permissions, monitoring, escalation rules, and production feedback. Even OpenAI has created a product, Presence, designed for these concerns. 

According to Gartner, more than 40% of agentic AI projects will get canceled by the end of 2027 due to cost concerns, unclear business value, or inadequate risk controls, and many of the current projects are being misapplied or are still in a proof-of-concept phase. And as I see it, the problem is not a lack of enthusiasm, but a lack of a reliable control layer around autonomous work. 

Someone has to decide what an agent may do, what it may not do, when it must stop, and when a human has to take over. The more autonomy that agents get to have and the less direct human oversight they receive, the more room there is for misunderstanding or for unintended actions. And this is where I think we are confusing two completely different things: Intelligence is not the same as control.

Sure, a model can be great when it comes to generating ideas, interpreting language, or choosing possible actions, but control means something completely different: staying aligned to the original goal regardless of how much the context or the circumstances may change. That’s the essence of management. Great companies don’t just hire smart people and walk away. They also define goals, budgets, decision rights, escalation rules, incentives, and review cycles. 

We have spent three years making AI astonishingly capable. Now we are beginning to discover that capability without steering is not autonomy but drift. The next enterprise breakthrough will come from putting something competent in the empty seat.