Claim 1: Iterative collaboration drives value
Microsoft's research on 297 early Copilot users found that high-value implementations involve iterative collaboration rather than one-off queries
Microsoft's research on 297 early Copilot users found that high-value implementations involve iterative collaboration rather than one-off queries
Human-AI collaboration in medical diagnosis achieves 90% accuracy, surpassing humans alone (81%) or AI alone (73%)
McDonald's China increased monthly employee AI transactions from 2,000 to 30,000 after implementing Azure AI and GitHub Copilot
Most enterprises skip foundational adoption stages; 68% of C-suite report rushed integration creates division
Effective AI co-thinking requires memory retention, dedicated project contexts, and custom instructions promoting critical questioning over agreement
"Teams that got real value weren't using AI for one-off tasks. They were iterating."
Kamil Banc
90% accuracy
Human-AI collaboration in medical diagnosis vs. 81% (humans alone) or 73% (AI alone)
15x growth
McDonald's China monthly AI transactions: 2,000 → 30,000
68% report division
C-suite executives say rushed AI integration creates organizational friction
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The article examines the shift from using AI as a task-completing assistant ("coworker" mode) to collaborative strategic advisor ("co-thinker" mode). Microsoft's research on Copilot users shows iterative collaboration drives high-value outcomes. Evidence from medical diagnosis, enterprise deployments (McDonald's China 15x growth), and organizational research (68% of C-suite report friction from rushed integration) demonstrates that staged implementation and intentional configuration maximize AI value while preventing organizational division.