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Your individuals are faster. Your organisation isn't.
This is the AI Productivity Trap. Solving it isn't a tools question. It's an operating-model question.
This is the AI Productivity Trap. Solving it isn't a tools question. It's an operating-model question.
Two years into enterprise AI rollouts, the numbers tell a strange story. Individuals say they're faster. Surveys agree. Adoption is real. But the throughput dashboards haven't shifted, and the P&L hasn't moved. Activity is up. Motion is flat.
The gap isn't in the tools. The tools work. The gap is in what sits between tasks: the coordination, the handoffs, the waiting, the re-explaining. AI made the tasks faster. The space between tasks is unchanged.
Activity is not the same as motion.
A 30% gain on the ten percent of work that is actual production is a three percent gain on the whole. The other ninety percent, the coordination tax, doesn't move because the operating model around it didn't change.
The book calls this the AI Productivity Trap. AI reduces the cost of creation but increases the cost of coherence. More output requires more coordination. More coordination doesn't lift throughput. It absorbs it.
The next move isn't another tool deployment. It's an operating-model change. Roles redefined as judgment systems rather than task lists. Persistent apprentices that carry the work between humans. A mesh that lets apprentices coordinate without funnelling everything through their humans first. A Work Graph that makes flow visible at the enterprise level.
The lift this time is collective. Not faster individuals. A faster organisation, built to a shape AI can actually accelerate.
The complete operating-model answer to the AI Productivity Trap. By Christopher Jackson, May 2026.
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