A new AI spending study published by PwC on April 13 finds that nearly three-quarters of all AI economic value is captured by just one-fifth of organizations, revealing a stark and widening divide between a small group of companies turning AI into measurable returns and the majority still stuck attempting to move beyond pilot projects.
PwC’s press release published April 13 frames the divide as structural rather than temporary: top performers have built strong AI foundations, including a technology environment that enables integration, a clearly defined roadmap, formalized risk processes, and an organizational culture that supports adoption. Most companies have not done that preparatory work before investing heavily in tools. The result is the pattern both MIT and PwC have now documented independently: large capital outlays, minimal measurable return, and a growing gap between the companies that got the foundations right and those that did not.
Gartner describes the current moment as AI’s “Trough of Disillusionment,” the phase of the hype cycle where experiments and implementations fail to deliver, interest wanes, and the market separates into survivors and casualties.
AI Spending: Why Most Companies Are Failing to Turn Investment Into Returns
The failure pattern is consistent across both studies. Companies are purchasing AI capabilities before identifying high-value use cases or establishing clear success metrics, creating solutions searching for problems. Only 14 percent of workers use generative AI on a daily basis despite massive corporate spending, according to a separate PwC workforce study. That adoption gap means the tools being bought are not being integrated into workflows at a scale that produces measurable output gains. Deploying AI without changing the underlying processes it is supposed to improve produces no return because the technology alone cannot restructure how work gets done.
What the 20 Percent Who Are Winning Are Doing Differently
The companies capturing 74 percent of AI’s economic value share a common approach: they started with business strategy, not technology selection. They identified specific areas where AI could enhance competitive positioning before investing, built the data infrastructure and governance frameworks needed for AI to function reliably, and then scaled from demonstrated wins rather than attempting enterprise-wide transformation from a standing start. PwC’s AI fitness index, which analyzed 60 AI management and investment practices, found that AI use and AI foundations together predict which companies produce returns.
What Comes Next as the Gap Widens
As crypto.news has reported, the gap between AI leaders and laggards is now visible in profit margin data and is beginning to show up in competitive positioning across industries. As crypto.news has noted, the AI integration decisions companies make in 2026 are directly affecting headcount and capital allocation in ways that are reshaping sector-level employment trends. Kande’s warning that the gap “will widen quickly for those that don’t act” reflects PwC’s view that 2026 is the year the divide between AI leaders and followers becomes durable rather than correctable.
