Many CEOs say AI is helping their businesses get more productive, but that does not mean they can yet show a reliable return. EY’s 2026 survey found that half of respondents named AI the biggest contributor to productivity gains over the prior 12 months; in the same survey, only 16% said they had clear, real-time visibility into AI’s return on investment (ROI). Separate PwC findings show that reported revenue and cost benefits remain uneven.
These are executives’ survey responses, not independent measurements proving that AI caused productivity to rise across businesses. The apparent contradiction is easier to understand when productivity, financial outcomes, adoption and the ability to measure ROI are treated as different things.
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Is AI actually making businesses more productive?
Some CEOs say it is. In EY’s 2026 CEO Outlook Survey, 50% of respondents said AI technologies were the single largest contributor to their company’s productivity gains over the previous 12 months. That is a statement about what surveyed leaders perceived and attributed to AI—not a controlled measurement of how much productivity changed or proof that AI caused the gains.
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Productivity can improve when a task takes less time, work moves faster, or the same team produces more. Those operational gains do not automatically create higher revenue or profit: a company still has to put the saved time or extra capacity to productive use, and account for the costs of introducing and operating the technology.
Are companies seeing a financial return on AI?
Not consistently, according to PwC’s 29th Global CEO Survey. Asked about AI’s financial effects over the prior 12 months, respondents reported a mix of outcomes:
| Outcome reported by CEOs | Share |
|---|---|
| Additional revenue from AI | 30% |
| Lower costs | 26% |
| Higher costs | 22% |
| Neither higher revenue nor lower costs | 56% |
| Both lower costs and higher revenue | 12% |
The categories are not all mutually exclusive: for example, the 12% who reported both lower costs and higher revenue can also appear among respondents reporting each of those outcomes. The figures are CEO-reported results, not an audited account of AI’s contribution to company finances. See PwC’s survey findings.
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The contrast with EY’s productivity result is not necessarily a conflict. A team may complete work faster without the company selling more, reducing payroll or other expenses, or increasing profit. PwC’s figures indicate that revenue and cost outcomes have not followed uniformly, even as some leaders identify productivity gains.
Why do CEOs report productivity gains if results are hard to show?
Productivity and ROI answer different questions. Productivity concerns the relationship between effort and output; ROI asks whether the value produced outweighs the costs involved. A faster process might improve output, but its financial value depends on whether the business can convert that improvement into something measurable, such as more sales, lower operating costs, or capacity for other work.
Measurement itself is another constraint. In EY’s 2026 survey, 16% of CEOs said their organizations had clear, real-time visibility into AI ROI. Another 24% said they could track some AI costs but had limited ability to assess overall ROI. EY also reported that 56% had at least some reliable visibility into AI costs and returns. The results point to uneven measurement—not an absence of measurement everywhere.
EY reported that 23% of CEOs said AI-driven productivity gains are often absorbed before they fully translate into bottom-line results. That helps explain how leaders can notice operational improvement yet struggle to connect it to growth or profitability. A gain may be real to the team doing the work while remaining difficult to isolate or value at the company level.
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How widely are businesses using AI?
Survey findings suggest that use and impact vary by company and by measure. PwC’s March 2026 workforce analysis said fewer than one-quarter of CEOs reported extensive AI use across major business areas. It also cited a separate 2025 PwC workforce survey in which 14% of workers said they used generative AI daily at work. These figures come from different survey populations; they are not a matched comparison of CEO and worker responses.
A separate PwC survey of 300 senior executives at U.S. companies examined AI agents. More than a third said their organizations had broadly adopted agents, 27% reported limited adoption, and 17% said adoption extended throughout the company. These are adoption reports, not measures of realized productivity or financial return. PwC notes that broad adoption does not necessarily mean deep impact. PwC’s AI-agent findings describe that survey.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What changes do CEOs expect AI to require?
Gartner’s April 2026 release reported that 80% of surveyed CEOs and other senior executives expected AI to require medium-to-high change in operational capabilities. The survey included 469 executives worldwide and concluded in Q4 2025. This is an expectation of organizational change, not evidence that the changes have already happened or that they have raised productivity. Gartner’s release gives the sample and timing.
That distinction matters: adopting a tool, changing how a business operates, reporting productivity gains, and demonstrating a financial return are separate stages. A company can be at one stage without having reached the next.
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- EY’s surveyed CEOs frequently identified AI as a major source of recent productivity gains, but the survey does not establish a causal, independently measured increase across businesses.
- PwC’s global CEO findings show mixed financial outcomes: some respondents reported revenue or cost benefits, while many reported neither higher revenue nor lower costs.
- EY’s ROI-visibility figures show that some companies can track costs and returns, but clear real-time visibility remains uncommon among respondents.
- PwC’s agent-adoption results and Gartner’s expectations about operational change provide context; neither proves that AI has already produced measurable company-wide gains.
The surveys differ in respondents, geography, field dates and questions, so their percentages should not be combined into a single estimate. Taken together, they support a measured conclusion: many executives see AI-related productivity potential or gains, while the path from faster work to demonstrable business value remains uneven.
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