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AI adoption slows down when change management is treated as a side workstream instead of a production dependency.
A structured transition plan protects momentum and prevents operational friction from becoming the default brake on scale.
When AI programs stall, the manager layer is often where the friction becomes visible. Teams can understand the tool and still fail to change the operating rhythm around it.
Executives may sponsor the program and teams may test the tool, but managers decide how the work is actually reviewed, escalated, and reinforced each week.
If manager expectations are rewritten after the deployment, the organization spends the first months in confusion. Transition work has to happen before scale, not after it.
The right question is not whether people were trained. It is whether management routines, reporting lines, and feedback loops now support the new workflow.