AI training fails because we measure activity, not thinking. Studies of designers and students show clicks, usage, and AI knowledge don’t predict better judgment. Transformation happens between stimulus and action—and today’s metrics can’t see it.
AI rollouts fail not because of technology, but psychology. Research shows that mismatched AI personalities degrade performance and managers penalize workers for AI use even when quality holds. Ignoring collaboration dynamics and credit attribution turns AI adoption into hidden dysfunction.
AI lets individuals think beyond their domain—but it also generates plausible fiction. Studies from HBS, the BBC, and European researchers show why experienced professionals are essential: they’re the ones who can tell when AI sounds right but is dangerously wrong.
AI fails two-thirds of real jobs—not because it’s useless, but because it can’t work alone and humans don’t know when to trust it. Drawing on UpBench and Nature studies, this essay shows why AI transformation requires apprenticeship, not installation.
AI doesn’t fail at scale because of weak adoption—it stalls because organizations can’t absorb the capacity it creates. This essay breaks down three hidden constraints—redeployment discipline, managerial activation, and governance design—that determine whether AI drives EBIT or just faster busywork.