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TL;DR
McKinsey’s 2026 survey found that 6% of respondents’ organizations qualified as AI “high performers,” attributing at least 5% of EBIT to AI. Other research also points to a gap between adoption and material returns, with workflow redesign, clear value measures and controls emerging as reported differences between leaders and laggards.
Only 6% of organizations in McKinsey’s 2026 survey qualified as AI “high performers,” meaning respondents attributed at least 5% of EBIT to AI, according to coverage by The Register and TechTimes. The finding, highlighted in an analysis published November 6 by ThorstenMeyerAI.com, underscores a gap between companies adopting AI and those reporting a substantial profit contribution from it.
McKinsey surveyed 1,719 respondents. In the survey, 37% said AI had some effect on their company’s EBIT, roughly the same share as a year earlier. The 6% high-performer figure uses a narrower threshold: at least 5% of EBIT attributed to AI. These are survey responses, not audited financial results, and they describe what respondents reported rather than independently verified company performance.
A separate Boston Consulting Group study of 1,250 companies found a similar concentration of reported value: 5% of companies said they were generating value from AI at scale, while 60% reported no material value, according to the source analysis. The figures come from a different survey and use BCG’s categories, so they should not be treated as directly interchangeable with McKinsey’s EBIT measure.
The source analysis points to a shift from using AI for isolated tasks toward redesigning end-to-end workflows. It says BCG respondents attributed about 17% of their AI value to agents and expected that share to reach 29% by 2028. McKinsey’s survey, as reported by The Register and TechTimes, found that among companies with more than $1 billion in revenue, 40% said they were scaling agents, up from 27%. Agents are systems that can take steps toward a task, rather than only respond to a prompt; the survey figures do not establish that every such deployment is profitable.
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 Not Profit
The findings matter because adoption alone does not show business impact. A company can run pilots, offer AI tools to staff or use AI in a limited process without changing costs, revenue or operating results enough to register as material value. The survey gap suggests that executives and investors should distinguish deployment counts from measurable financial outcomes.
For managers, the research points toward choosing a small number of valuable processes, setting a baseline and tracking the result after implementation. The source analysis says BCG’s higher-performing companies had deployed 62% of their AI initiatives, compared with 12% among laggards. That comparison is reported by the source; it does not, by itself, prove that deployment rates caused the difference in results.
Work redesign also changes who is responsible for decisions. If a system can classify a case, consult company records and take an action, people still need to set permissions, review exceptions and own outcomes. For employees, the distinction between assistance and delegated work may affect job tasks and oversight, but the cited surveys do not establish a uniform employment effect across industries.
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Adoption Across Europe and Beyond
Adoption indicators provide a different view from profit surveys. Eurostat reported that 20.0% of EU enterprises with 10 or more employees used AI in 2025, up from 13.5% in 2024. The 2025 rate was 55% among large enterprises and 17% among small ones. These figures measure use, not returns, and cover EU businesses meeting the employee threshold.
The source analysis also cites a persistent implementation barrier: among European firms that had considered AI but did not use it, 71% named a lack of expertise. That survey finding helps explain why interest or access to tools may not become a sustained deployment, though it does not identify every reason projects stall.
Forecasts underline the risk of treating a pilot as proof of a viable programme. Gartner forecasts that more than 40% of agentic AI projects will be canceled by the end of 2027 because of cost, unclear value or weak risk controls. Gartner also forecasts that organizations will abandon 60% of AI projects that lack AI-ready data. These are forecasts, not reported cancellation totals, and the cited material does not provide a project-by-project breakdown.
“The new promise is not ‘digitise the process’ but ‘let software process the work.’”
— Thorsten Meyer, ThorstenMeyerAI.com
How Much Profit AI Adds
The surveys do not establish a single, directly comparable rate of AI profitability. McKinsey’s 6% refers to respondents’ organizations meeting a specific EBIT threshold; BCG’s 5% refers to companies reporting value at scale. Both depend on survey responses and definitions, and neither figure should be read as an audited count of all companies.
The source material does not provide enough detail to determine which industries, AI applications or accounting methods account for the reported profit effects. Nor does it establish that workflow redesign, agent deployment or higher implementation rates alone caused better results. Gartner’s project-cancellation figures are forecasts through 2027, not completed outcomes.
Employment effects also remain uncertain. The source analysis says the share of McKinsey respondents expecting job cuts rose to 39% from 32%, while last year’s cuts fell short of respondents’ earlier expectations. The material does not specify a consistent measure of job losses across companies, or show how expectations will compare with actual staffing changes in 2026.
Testing AI Results Through 2028
The next test is whether companies can move from pilots to sustained deployments that show measurable effects on costs, revenue or productivity. Businesses will need to compare results with a clear baseline and account for the costs of data preparation, integration, oversight and risk controls, rather than relying on adoption figures alone.
BCG respondents expect agents’ share of AI value to rise from about 17% to 29% by 2028, according to the source analysis. That is an expectation, not a confirmed outcome. Gartner’s forecast that more than 40% of agentic projects will be canceled by the end of 2027 sets another point to watch: whether projects are discontinued, scaled or redesigned, and why.
Future survey releases and company disclosures may clarify whether the reported gap narrows. Until then, the available findings support a measured conclusion: AI use is spreading, but substantial profit gains remain concentrated among a small share of survey respondents.
Key Questions
What does the 6% figure mean?
It refers to the share of respondents in McKinsey’s 2026 survey whose organizations qualified as “high performers” by attributing at least 5% of EBIT to AI. It is a survey result, not an independently audited share of all companies.
How does BCG’s 5% figure differ?
BCG’s study of 1,250 companies found that 5% reported generating value from AI at scale. That category differs from McKinsey’s EBIT threshold, so the two percentages are similar in scale but do not measure exactly the same thing.
What do the findings suggest companies should do?
The source analysis points to selecting specific business problems, redesigning workflows rather than attaching AI to isolated tasks, and measuring results against a baseline. It also stresses controls and clear responsibility when systems can take actions.
Are AI agents already producing most AI value?
No such conclusion is supported by the figures provided. BCG respondents said agents accounted for about 17% of their AI value and expected 29% by 2028. The latter is an expectation, not a measured future result.
Does the research show that AI is cutting jobs?
The source says 39% of McKinsey respondents expected job cuts, up from 32%, and that earlier cuts fell short of expectations. Those figures describe expectations and the source’s account of prior cuts; they do not establish a uniform employment effect across companies.
Source: ThorstenMeyerAI.com
