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…
When using an AI chatbot for personal queries, decision-making or research, it performs remarkably well. However, when applied to a company's AI, the outcomes are often less impressive—unless the application is strictly administrative. The reason behind this disparity lies in the steering required for corporate processes. While using the AI personally, individuals naturally engage in steering, corrections, and adjustments until they deem the answer satisfactory.
When transitioning the AI's role from personal assistance to running a process, the responsibility of steering must shift elsewhere, which can be challenging to implement efficiently. The difference between assistance and autonomy is crucial. While copilots propose actions, humans typically evaluate and judge. In corporate settings that may span several hours to days or involve multiple systems and departments, this distinction becomes even more significant.
The challenge arises from the long-running agents, which can struggle with continuity and efficiency unless aided by a surrounding system. The real challenge is not proving that agents can work but providing them with a layer of reliability that high-value workflows demand. This includes policies, permissions, monitoring, escalation rules, and production feedback.
Even OpenAI has developed a product, Presence, to address these concerns. CEO-level questions should focus on how to keep autonomous systems aligned with the desired business outcomes, rather than solely on the intelligence or number of agents deployed. Successful implementation of generative AI relies on redesigning workflows, establishing clear goals, budgets, decision rights, escalation rules, and review cycles.
Without proper steering, intelligence alone is insufficient; it can lead to drift and unintended consequences.
Written by urgent.news from Fast Company's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
