Sixty-five percent of organizations now use generative AI in at least one business function, double the rate from 10 months earlier, according to McKinsey's 2026 State of AI research. Enterprise AI budgets grew at a median rate of 22% year-over-year, and 91% of businesses report using AI in at least one capacity. Yet the productivity data does not match the investment curve: an independent NBER study published in February 2026 found that 80% of companies actively using AI reported no measurable productivity impact.

McKinsey describes this as an AI productivity paradox. Adoption and capital investment are accelerating, but sustained performance improvement remains elusive in the aggregate. The 37% average productivity improvement reported in AI-augmented roles, compared to 12% for traditional automation, suggests value exists but the distribution is highly uneven across organizations, functions, and implementation quality.

Agentic AI adoption is expanding from experiment to scale. Twenty-three percent of organizations are now scaling an agentic AI system in at least one enterprise function, and another 39% are actively experimenting. Scaling remains concentrated, with no single business function reporting more than 10% of respondents deploying agents at scale.

The implementation gap has meaningful implications for firms selling to enterprise buyers. Decision-makers are under pressure to show AI results to leadership while navigating a market where the majority of deployments have yet to demonstrate measurable returns. Content and consulting that helps buyers close the implementation gap carries more persuasive weight than messaging that simply amplifies adoption statistics.