Is AI paying off for companies?

What the research shows

Almost every organisation now uses AI somewhere. In McKinsey's 2026 global survey, 89% of respondents said their organisation regularly uses AI in at least one business function, and 44% said AI is scaling across the enterprise, up from 38% a year earlier. The share using it in three or more functions rose from 51% to 56%.

Financial results have not kept pace. Only 37% of respondents attribute any EBIT impact to AI, essentially the same as in 2025. The group McKinsey calls AI high performers, who attribute at least 5% of EBIT to AI and describe its impact as significant, is still about 6% of respondents. Individual gains are clearer: 80% say AI has improved their own productivity.

Chief executives see the same gap. In PwC's 29th Global CEO Survey of 4,454 CEOs, 56% said AI had delivered no significant financial benefit so far, and only 12% reported both lower costs and higher revenue. Confidence in revenue growth over the next 12 months fell to 30%, from 38% a year earlier.

Returns are concentrating. PwC's April 2026 AI Performance study of 1,217 senior executives found that 74% of AI's economic value is captured by 20% of organisations. These leaders are twice as likely to redesign workflows around AI rather than add tools, and two to three times as likely to use AI to pursue growth. McKinsey reports the same pattern: nearly three-quarters of its high performers have fundamentally redesigned workflows, against a quarter of other respondents.

Costs are now part of the equation. One in five McKinsey respondents say AI operating costs, including tokens, constrain their use of AI, yet 60% expect their organisation to increase AI investment over the next year.

What comes next

• Investment keeps rising: 60% of respondents expect higher AI spending in the year ahead, and Gartner forecasts worldwide AI spending of $3.6 trillion in 2027.
• Operating costs, including tokens, are becoming a design constraint for agentic and coding use cases.
• The gap between AI leaders and other companies is likely to widen as leaders scale proven use cases and automate more decisions.

What it means for leaders

• Give every AI initiative a baseline and a business owner before scaling it.
• Redesign the workflow end to end rather than adding AI to the existing process.
• Pursue growth uses alongside efficiency, and track both.
• Treat AI operating costs as part of each use case's business case.