Why Billions in AI Spending Aren’t Paying Off for Most Companies

Billions Spent on AI, but Almost No One’s Making Money: Report Says

A recent report from McKinsey & Company highlights a striking disconnect: despite massive investments in artificial intelligence, especially generative AI, very few companies are seeing meaningful financial returns. Only 1% of executives surveyed consider their organizations to have reached a “mature” stage of AI adoption.

The report, titled “Superagency in the Workplace,” reveals that many companies pour resources into AI tools without clear strategies for implementation or measurable outcomes. This gap between spending and results points to a larger problem: AI's potential is real, but execution is falling short.

Big Spending, Limited Gains

AI is often compared to transformative inventions like the steam engine, yet companies struggle to convert enthusiasm into tangible outcomes. McKinsey data shows only 19% of senior executives report revenue increases above 5% linked to AI adoption. Another 39% saw modest gains between 1% and 5%, while 36% reported no revenue impact at all.

Cost savings from AI are also minimal. Just 23% of companies noted any significant reduction in operational expenses. These figures suggest most organizations remain stuck in small-scale AI experiments that fail to scale effectively.

Investments Will Grow, Despite Ambiguity

Despite these underwhelming results, optimism about AI remains high. The report finds 92% of companies plan to increase AI spending over the next three years. However, McKinsey cautions that while long-term benefits may appear, short-term returns are uncertain.

Many organizations invest out of strategic fear—concerned about falling behind competitors—without a clear plan themselves. Without defined roadmaps and expected outcomes, these investments risk being more performative than productive.

Who's to Blame?

Contrary to common belief, employee resistance is not the main obstacle to AI adoption. McKinsey's research shows leadership hesitation and poor strategic coordination are bigger issues. Executives are 2.4 times more likely to blame employees for slow adoption, but workers often lead in actual AI usage.

For example, employees using generative AI for at least 30% of daily tasks are three times more than what executives estimate. Nearly 50% of workers say formal training would improve adoption, yet 22% report little or no support. This gap points to a leadership disconnect from how AI tools are employed on the ground.

No Roadmap, No Results

The fundamental problem is the lack of a coherent AI strategy. Only 25% of executives say their company has a complete AI roadmap, while over half admit they are still drafting one. This leaves many stuck in a “pilot project trap,” where limited experiments never scale due to weak planning or unclear goals.

Stanford’s Erik Brynjolfsson, quoted in the report, sums it up: “This is the time to capture value from AI—and to hope your competitors are still just experimenting.” AI today is like the internet was 40 years ago. The challenge isn’t funding, tools, or talent, but leadership, strategic clarity, and internal alignment.

Success will go to companies that turn AI investments into structured, scalable action—not necessarily those who spend the most. Until then, the gap between AI’s promise and its payoff will likely persist.

Executives looking to build effective AI strategies should focus on:

• Creating clear, actionable roadmaps with measurable goals
• Aligning leadership and teams around AI use and outcomes
• Investing in employee training and support to boost adoption
• Scaling pilot projects with discipline and ambition

For those interested in practical AI skills and strategic frameworks, Complete AI Training offers courses that help executives and teams bridge the gap between investment and impact.