Enterprise AI adoption metrics hide a widening skill gap

Vasuman Moza, CEO of Varick Agents, argues that enterprise AI adoption metrics hide a barbell distribution: a small group of power users captures most of the value while much of the workforce barely uses the tools or uses them poorly. Drawing on anonymized enterprise examples and public McKinsey and MIT figures, he says companies should measure how much work is manual, hybrid, or automated instead of treating logins as success. His proposed split is to train and reward power users for sharing what they build, while putting background agents into existing business systems for everyone else—a distinction that matters for productivity, AI spending, and realistic rollout plans.