Enterprise AI impact isn’t about the most powerful model—it’s about the smartest steering

This is something you have probably noticed several times—at least, I certainly have. When you use an AI chatbot for personal questions, decision-making, research, and the like, 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. Why is this happening? Basically, 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. Understanding the distinction between assistance and autonomy The essence is in the nature of the roles: While copilots propose, humans typically judge. That—in th…