Generative AI has moved rapidly from experimental demonstrations into everyday enterprise software. Companies are deploying AI assistants for programming, customer service, document analysis, marketing, research and internal operations.
Yet installing AI software does not automatically produce proportional productivity improvements.
One reason is coordination friction.
An AI system may successfully automate one individual task while leaving the surrounding business process unchanged. A model might summarize a document in seconds, for example, but employees could still spend hours transferring information between incompatible systems, requesting approvals or manually checking whether the AI-generated output is accurate.
As organizations deploy larger numbers of AI agents, another challenge emerges: someone still needs to coordinate them.
Agents require permissions, access controls, monitoring and escalation rules. Companies must determine what an AI system can execute independently and when a human must intervene.
This means the real productivity opportunity may come from business-process redesign rather than simple tool adoption.
Instead of asking, “Where can we add AI?”, companies may need to ask, “If this workflow were designed today with AI available from the beginning, how would it operate?”
That could involve removing unnecessary approval layers, integrating fragmented databases and redesigning jobs around human judgment rather than repetitive information processing.
The distinction matters financially. Paying for dozens of AI subscriptions while maintaining inefficient workflows can simply add another technology expense.
The companies that generate meaningful returns may be those willing to redesign the underlying organization—not merely place AI on top of processes created decades ago.
