Enterprise AI success requires proving business impact, not deploying capability¶
Insight: According to the Design of AI publication, 85% of enterprise AI projects fail and 91% of models degrade silently. The critical gap is not model capability but measurement: teams celebrate demos and ship features without proving business impact. "Shipping isn't impact." A calibration framework (Power/Speed/Impact/Joy, borrowed from F1 racing) prioritizes outcome measurement over feature velocity.
Detail: Supporting data: 80% of ChatGPT users sent fewer than 1,000 messages in 2025; 84% of the world has never used AI; only 0.3% pay for it. METR research found experienced developers using AI tools showed "zero productivity gain" despite faster work. John Maeda's research shows design teams being replaced with "prompt engineering pods," eliminating judgment from product decisions. The "hollowing out effect" threatens PMs next: "if your PM's job becomes prompt-wrangling instead of deciding what to build and why, you've automated the wrong layer." Research also shows removing struggle from workflows "destroys the learning that builds expertise."