When everyone has AI and the company still learns nothing

AI adoption often begins with familiar enterprise steps like purchasing licenses, defining acceptable use, running training, and forming champion networks. Early rollout can look successful through visible activity such as shared use cases and internal pilots. A later “messy middle” emerges when AI use becomes widespread but uneven, partially hidden, hard to measure, and not connected to organizational learning. Teams may use AI in very different ways, from simple autocomplete to deeper workflows, while management focuses on ROI, license usage, prompt counts, surveys, and selective PoCs. Without mechanisms to move discoveries from individuals to teams and then into organizational capabilities, companies may learn little despite increased individual output.

"Individual productivity gains from AI do not automatically become organizational gains. People may get faster, write better, analyze more, automate more, or quietly become cyborg versions of themselves. The company may still learn almost nothing."

"The “messy middle” of AI adoption starts when AI use is everywhere, uneven, partially hidden, difficult to compare, and not yet connected to organizational learning. Everyone has Copilot now. The first phase of AI adoption is (mostly) comfortable because it looks like other enterprise rollouts."

"Management sees license usage (“Where is the ROI for the 2 mio € we paid Anthropic last year?”), maybe prompt counts, maybe a survey, maybe a few internal PoCs that feel encouraging enough to put into a steering committee deck. In other companies, AI went straight to IT and died."

"The second phase is much stranger: one team uses Copilot as autocomplete and calls it a day. Another team runs Claude or Gemini or Cursor in pockets, and every team has at least one person who is much further along than the official enablement material assumes. Some of this is visible, yet much of it is not."

Read at Robert Glaser