TL;DR
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AI adoption is growing, but recent surveys indicate that substantial financial returns remain concentrated among a small share of companies. McKinsey found 6% of respondents attributed at least 5% of company EBIT to AI, while BCG reported that 5% of surveyed firms had achieved value at scale. The surveys rely on different samples and definitions, so their results are not directly comparable.
Most companies are adopting AI, but few report large profit gains, according to recent surveys of businesses. In McKinsey’s 2026 survey, 6% of respondents said AI accounted for at least 5% of their company’s earnings before interest and taxes (EBIT), while 37% reported some effect on EBIT, figures reported by The Register and TechTimes. The results point to a gap between using AI and demonstrating a substantial financial return.
McKinsey surveyed 1,719 respondents for its 2026 State of AI survey. The share reporting some AI effect on EBIT was 37%, about the same as a year earlier, according to The Register’s report. The 6% figure refers to respondents classified as “high performers”: they attributed at least 5% of company EBIT to AI. These are self-reported survey results, not audited financial accounts.
A separate BCG survey of 1,250 companies, published in September 2025, found that 5% had achieved value from AI at scale and 60% reported no material value so far. BCG said 35% were scaling some returns. Its categories and survey population differ from McKinsey’s, so the percentages describe related but distinct measures rather than a single head-to-head comparison.
Adoption measures also vary by region and company size. Eurostat reported that 20.0% of EU enterprises with at least 10 employees used AI in 2025, up from 13.5% in 2024. The rate was 55% among large enterprises and 17% among small ones. Eurostat measures whether firms use AI; it does not measure whether that use improved profits.
AI adoption is widespread large returns remain rare
Recent surveys show a gap between companies using AI and those reporting substantial financial value. Their measures differ, so the results are not directly comparable.
Profit reports remain uncommon
The surveys describe different ways companies assess AI value.
- 6% said AI contributed at least 5% of company EBIT.
- 37% reported some effect on EBIT.
- 5% said they had achieved value at scale.
- 60% reported no material value so far.
AI use varies by company size
Eurostat counts adoption among EU enterprises with at least 10 employees, not financial returns.
What the figures can show
Each source captures a different part of AI’s business impact.
Self-reported impact
McKinsey asked respondents about AI’s effect on company EBIT; these are not audited accounts.
Different definitions
BCG assessed value creation and scaling, using a different survey population and measure.
Use is not profit
Eurostat counts whether firms use AI, not whether it improves their financial results.
From Adoption to Measurable Returns
The findings matter because the spread of AI tools does not, by itself, show that businesses are changing their economics. A company can test chatbots or automate a narrow task without changing how a whole process is handled. The reported profit figures suggest that large, measurable returns remain uncommon, even as usage expands.
The source material describes a shift from software that stores and routes information to systems that can interpret inputs, consult other systems and take bounded actions. If companies redesign workflows around those capabilities, they may reduce manual handling or speed up decisions. But the surveys do not establish that any one implementation approach caused stronger financial performance.
For workers, the reported productivity gains and concern about job cuts point to different effects unfolding at once. McKinsey respondents who use AI said it improved their productivity, while a larger share than the previous year expected job reductions. Those responses capture expectations and perceptions; they do not show how many jobs were actually eliminated because of AI.
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How the Surveys Measure AI
The figures come from surveys and official adoption statistics that use different definitions. McKinsey asked executives about AI’s effect on EBIT; BCG assessed value creation and scaling among surveyed companies; Eurostat counted business use among EU enterprises with 10 or more employees. Each helps describe part of the picture, but none alone provides a universal measure of AI’s business impact.
The source material also reports that AI agents contributed about 17% of AI value in BCG respondents’ estimates, with respondents expecting that share to reach 29% by 2028. McKinsey reporting cited in the same material says 40% of respondents at companies with more than $1 billion in revenue were scaling agents, up from 27%. These are survey findings and expectations, not independently verified market-wide totals.
Project cancellations are another sign of the implementation challenge. Gartner forecasts that more than 40% of agentic AI projects will be cancelled by the end of 2027 because of costs, unclear value or weak risk controls. Gartner also forecasts that organisations will abandon 60% of AI projects that lack AI-ready data. These are forecasts, not completed cancellation counts.
“6% qualified as “high performers,” attributing at least 5% of company EBIT to AI.”
— McKinsey survey respondents, as reported by The Register and TechTimes
What the Profit Figures Cannot Show
The survey results do not establish how much AI changed company profits across the economy. They rely on executive responses, and the source material does not provide audited financial data linking specific AI projects to EBIT changes. It also does not establish whether companies classified as high performers would have achieved similar results without AI.
The percentages should not be treated as directly comparable: McKinsey and BCG use different samples and definitions, while Eurostat tracks adoption rather than value. The source material also does not identify which industries or particular AI applications account for most of the reported gains. Gartner’s cancellation figures remain forecasts, and the scale of actual cancellations by the end of 2027 is not yet known.
More Evidence as Projects Scale
Companies will be able to judge the gap more clearly as AI projects move beyond pilots and businesses publish results tied to specific workflows. The survey findings suggest that leaders are focusing on a limited number of uses, workflow redesign, measurement and controls. Those are approaches described in the source material, not a guarantee of financial returns.
Gartner’s forecast makes the period through the end of 2027 a key test for agentic AI projects, particularly those facing cost, data or risk-control problems. Further survey rounds and company disclosures may show whether broader use translates into more firms reporting material financial impact. Until then, the main confirmed picture remains uneven adoption and a small share of respondents reporting large AI-linked profit contributions.
Key Questions
What does the 6% figure measure?
It is the share of McKinsey’s 2026 survey respondents classified as high performers who attributed at least 5% of company EBIT to AI. It is a self-reported survey result, not an audited measure covering all companies.
Does 20% of EU businesses using AI mean AI is profitable for them?
No. Eurostat’s 20.0% figure measures whether EU enterprises with at least 10 employees used AI in 2025. It does not measure financial returns.
How do the McKinsey and BCG results differ?
McKinsey reported that 6% of respondents met its threshold for AI contributing at least 5% of EBIT. BCG reported that 5% of surveyed companies achieved value at scale. The surveys use different samples and definitions, so the figures are not directly interchangeable.
Are AI job cuts confirmed by these findings?
No. The source material says a larger share of McKinsey respondents expected job cuts than a year earlier. That describes expectations; it does not establish how many jobs were actually cut because of AI.
Source: Thorsten Meyer AI
