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Many CEOs say AI is improving productivity, but reported gains do not automatically show up as higher revenue, lower costs, or profit. In EY’s 2026 survey, half of CEOs named AI the biggest contributor to productivity gains over the previous 12 months; the same survey found that only 16% had clear, real-time visibility into AI’s return on investment (ROI). These are executives’ survey responses, not independent proof that AI caused productivity to rise across businesses.
Is AI actually making businesses more productive?
CEOs surveyed by EY said it is helping. In an anonymous online survey of 1,200 CEOs at large companies in 21 countries, conducted in August and September 2026, 50% identified AI technologies as the single largest contributor to productivity gains over the previous 12 months. That measures what respondents attributed to AI; it is not a controlled measurement of output across companies, nor evidence that every business or worker experienced a gain. The sample was focused on large firms, so it should not be treated as a result for small businesses or the workforce as a whole. EY’s 2026 CEO Outlook Survey provides the survey details.
Productivity can improve without an immediate change in a company’s financial results. A team may complete work faster or produce more with the same resources, but the benefit may not reduce spending or generate additional sales. EY reported that 23% of CEOs said AI-driven gains are often absorbed before fully translating into bottom-line results.
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PwC’s 29th Global CEO Survey offers a separate view of reported financial outcomes. In the prior 12 months, 30% of CEOs reported additional revenue from AI, 26% reported lower costs, and 22% reported higher costs. More than half—56%—reported neither higher revenue nor lower costs. PwC also said 12% reported both increased revenue and reduced costs. These figures describe a different survey and different measures from EY’s productivity finding; they should not be combined into a single estimate of AI’s impact. PwC’s 29th Global CEO Survey reports the results.
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The results are not necessarily contradictory. Productivity describes how work gets done; revenue and cost figures describe financial outcomes. More output per hour may not produce more sales if demand is unchanged, and time saved may not lower costs if staffing or other spending stays the same. Conversely, adoption and implementation can add costs before any benefit appears.
Why can CEOs report productivity gains while ROI remains unclear?
Measuring an AI project’s overall return is harder than noticing a faster task or a useful output. A company needs to identify the costs involved, establish a credible baseline, and determine how much of a change came from AI rather than other business changes. Even when teams can see some savings or faster work, those local effects may be difficult to connect to company-wide revenue or profit.
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EY found that 16% of surveyed CEOs had clear, real-time visibility into AI ROI. Another 24% could track some AI costs but had limited ability to assess overall ROI. EY also reported that more than half had at least some reliable visibility into AI costs and returns. The picture is uneven measurement—not a complete absence of tracking, and not evidence that all AI initiatives lack value.
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How widely are businesses using AI, and what changes do executives expect?
Adoption measures provide context, but they do not establish that AI is delivering measurable results. PwC’s March 2026 workforce analysis said fewer than one-quarter of CEOs reported extensive AI use across major business areas. The analysis also cited a separate PwC workforce survey in which 14% of workers said they used generative AI daily at work. Those figures come from different survey populations and should not be compared as though they measured the same group or adoption rate. PwC’s workforce analysis discusses the CEO and worker findings.
In a different survey, PwC asked 300 senior executives at U.S. companies about AI agents. More than a third said their organizations had broadly adopted AI agents, 27% reported limited adoption, and 17% said agents had been adopted throughout the company. These categories describe reported deployment, not depth of use or financial impact; broad adoption alone does not show that an organization has achieved substantial productivity gains. PwC’s AI-agent findings explain that survey.
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Gartner’s April 2026 release reported that 80% of CEOs and other senior executives expected AI to require medium-to-high changes in operational capabilities. Its survey included 469 executives worldwide and was conducted across three quarters, ending in Q4 2025. This is an expectation about the scale of operational change, not a finding that those changes have already happened or improved productivity. Don Scheibenreif, Gartner Distinguished VP Analyst, summarized the distinction: “While digital business changes what the organization does, autonomous business changes how the organization does it.” Gartner’s April 2026 announcement describes the survey.
What the surveys do—and do not—establish
- They show reported perceptions and outcomes. EY’s productivity result reflects CEOs’ attribution; PwC’s figures reflect CEO reports of revenue and cost changes.
- They do not establish causation. The findings do not prove that AI alone caused a productivity or financial change.
- They are not directly interchangeable. The surveys differ in respondents, geography, field dates, and questions. A global CEO survey, a U.S. executive survey about agents, and a workforce survey measure different things.
- Expectations are not completed transformations. Gartner’s operational-change figure records what executives anticipate, not what has already been delivered.
Taken together, the evidence supports a narrower conclusion than “AI is making every company more productive”: many surveyed CEOs see AI as an important source of productivity improvement, while reported financial results and visibility into ROI remain uneven. Whether those gains become lasting business value depends on translating faster or more productive work into outcomes the organization can measure.
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