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TL;DR
AI adoption is growing, but major reported profit gains remain concentrated among a small share of companies. McKinsey’s 2026 survey found 6% qualified as high performers, while BCG reported that 5% of surveyed firms were getting value from AI at scale.
Only 6% of respondents in McKinsey’s 2026 AI survey qualified as high performers, meaning they attributed at least 5% of company EBIT to AI, according to findings reported by The Register and TechTimes. The result highlights a gap between expanding corporate use of AI and the comparatively small share of organizations reporting substantial profit effects.
The McKinsey survey included 1,719 respondents. Thirty-seven percent said AI had some effect on their company’s EBIT, roughly the same share as a year earlier. The 6% figure uses a narrower measure: respondents had to report that AI accounted for at least 5% of EBIT to qualify as high performers. It does not mean that only 6% of companies use AI, or that the remaining organizations receive no benefit.
A separate survey by Boston Consulting Group, covering 1,250 companies, found a similar concentration of reported results. BCG said 5% of firms get value from AI at scale, while 60% reported no material value. The survey figures are not directly interchangeable: the organizations, questions and definitions of performance differ.
Adoption is broader than those profit measures suggest. Eurostat reported that 20.0% of EU enterprises with at least 10 employees used AI in 2025, up from 13.5% in 2024. Use varied by company size: 55% of large enterprises used AI, compared with 17% of small enterprises. Those statistics measure adoption, not the financial return on AI.
Infographic 01 · Who gets value from AI
Most companies use AI. About 6 percent profit from it in a big way
Adoption keeps rising. The share of companies that can show a large effect on profit has barely moved.
Share of McKinsey’s 2026 respondents who attribute at least 5 percent of their company’s EBIT to AI and call the impact significant. Flat versus 2025, as reported by The Register.
Wide use, thin profit
Different surveys, different bases: read each line with its source
Sources: McKinsey State of AI 2026 (n = 1,719), as reported by The Register, 25 Aug 2026 and TechTimes, 26 Aug 2026 · BCG, The Widening AI Value Gap, Sept 2025 (n = 1,250) · Eurostat, 11 Dec 2025 · Eurostat Statistics Explained (enterprises with 10+ employees). Survey figures are self-reported by executives; Eurostat counts use, not value.
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Why AI Adoption Is Outrunning Returns
The gap matters because companies are moving from testing AI tools toward changing how work is done, yet survey results suggest that widespread use has not translated into equally widespread gains in reported profit. For executives and investors, the distinction between deploying a tool and improving financial performance is central: adoption rates alone cannot show whether AI is reducing costs, increasing revenue or changing productivity in a lasting way.
The findings also point to different outcomes among firms. BCG respondents associated stronger results with redesigning core workflows end to end, rather than adding isolated pilots. The source says leaders had deployed 62% of their AI initiatives, compared with 12% among laggards. That is a reported difference in deployment rates, not proof that deployment by itself causes higher returns.
For employees, the picture is mixed. In McKinsey’s survey, 80% of respondents who use AI said it made them more productive. At the same time, 39% expected job cuts, up from 32% in the previous survey. The source also says earlier expectations of job cuts exceeded the cuts subsequently reported. These are respondent views and expectations, not a direct count of jobs eliminated by AI.
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From Digitized Tasks to AI Workflows
Earlier digital-transformation projects often moved paper processes onto software while leaving people responsible for interpreting cases and deciding what to do. The shift described in the source is toward systems that can process some of that work: for example, reading an invoice, checking it against a contract and flagging an exception. Such systems may combine language models with access to company data and the ability to take bounded actions.
The change raises questions beyond which software a company buys. Businesses need to decide which steps an AI system can take, when a person must review a result and who is responsible when the system makes a mistake. The source argues that workflow redesign, supervision and governance are part of implementation, not separate concerns to address after a tool is deployed.
Agent use is growing, but the estimates refer to different surveys and timeframes. BCG respondents said agents accounted for about 17% of their AI value and expected that share to reach 29% by 2028. McKinsey reporting said 40% of companies with more than $1 billion in revenue reported scaling agents, up from 27%. These figures describe survey responses, not a single independently measured market-wide adoption rate.
“Only 6 percent qualify as “high performers” with at least 5 percent of EBIT attributed to AI.”
— McKinsey survey findings, as reported by The Register and TechTimes
What the Survey Numbers Cannot Show
The figures do not establish that AI caused the reported EBIT effects, nor do they provide a common measure that allows the McKinsey and BCG results to be combined. The source summarizes survey findings reported by news outlets and does not provide the full underlying questionnaires, respondent-level data or detailed methodology for every statistic. The 6% result is a share of respondents meeting a stated performance threshold, not an audited count of companies.
It is also unclear how returns vary by industry, by AI application or over time. The source does not specify how many respondents attributed lower costs versus higher revenue to AI, or how the survey treated costs such as implementation, computing and staff training. Productivity reports and expectations about job cuts do not, on their own, establish measured changes in output or employment.
Gartner’s forecasts are predictions, not confirmed outcomes. It forecasts that more than 40% of agentic AI projects will be cancelled by the end of 2027, citing cost, unclear value or weak risk controls. It also forecasts that organizations will abandon 60% of AI projects that lack AI-ready data. The source does not give the full forecast methodology or a detailed definition of the project categories.
The Test Is Measurable Business Value
Companies will need to show whether AI projects improve results beyond initial adoption. That means setting a baseline, tracking outcomes such as processing time, error rates, operating costs or revenue, and accounting for the cost of building and running the system. The source’s findings suggest that a pilot count or the number of employees with access to a tool is not enough to establish a financial return.
Agent projects also face practical tests before they can be scaled: access to reliable data, clear limits on what a system may do, review of exceptions and accountable human oversight. Gartner’s forecast of cancellations through 2027 makes project costs, value measures and risk controls relevant near-term issues, though the forecast does not determine how any particular company’s projects will fare.
Further survey results and company-level reporting may clarify which uses produce sustained gains and whether those gains affect staffing or operating models. For now, the confirmed picture in the cited surveys is an adoption-return gap: AI use is spreading, while only a small minority meets the reported high-profit threshold.
Key Questions
Does the 6% figure mean only 6% of companies use AI?
No. It refers to the share of McKinsey survey respondents whose companies attributed at least 5% of EBIT to AI. Eurostat separately found that 20.0% of EU enterprises with 10 or more employees used AI in 2025.
What did McKinsey count as a high performer?
In the 2026 survey, a high performer was defined as an organization attributing at least 5% of EBIT to AI. The survey included 1,719 respondents, according to the source material.
What did the BCG survey find?
BCG’s survey of 1,250 firms found that 5% were getting value from AI at scale, while 60% reported no material value. Those results use BCG’s survey and definitions, so they should not be treated as identical to McKinsey’s figures.
Why might AI projects fail to produce measurable returns?
The source points to projects that remain isolated pilots rather than changing whole workflows, as well as cost, unclear value, weak risk controls and data that is not ready for AI. Gartner’s cancellation estimates are forecasts, not confirmed results.
Source: ThorstenMeyerAI.com
