🔍 Read the full analysis: What The AI Profit Gap Says About Business Adoption on ThorstenMeyerAI.com
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TL;DR
Surveys cited by ThorstenMeyerAI.com show a widening gap between business AI use and reported financial returns: 37% of McKinsey respondents said AI affected company EBIT, while 6% met its high-performer threshold. The findings point to workflow redesign, skills, data readiness and governance as barriers, though the surveys do not establish that AI caused the reported profit outcomes.
Businesses are adopting AI faster than they are reporting substantial profit gains from it, according to surveys and forecasts reviewed in a November 6 analysis by ThorstenMeyerAI.com. The article cites McKinsey findings that 37% of respondents said AI had some effect on company EBIT, while 6% qualified as high performers by attributing at least 5% of EBIT to AI. The figures underscore a distinction between deploying AI and translating it into measurable financial results.
The figures come from surveys with different samples and definitions, rather than a single measure of business adoption. McKinsey’s 2026 survey included 1,719 respondents; its reported 37% figure was about the same as a year earlier. The analysis says the survey’s 6% high-performer group attributed at least 5% of EBIT to AI. The source article cites reporting by The Register and TechTimes for those findings.
A separate BCG study of 1,250 companies found that 5% were getting value from AI at scale, while 60% reported no material value, according to the analysis. BCG respondents said AI agents accounted for about 17% of AI value at the time of the study and expected that share to reach 29% by 2028. These are survey responses and expectations, not independently verified measures of future performance.
European adoption figures show a different part of the picture. Eurostat data cited in the article says 20.0% of EU enterprises with at least 10 employees used AI in 2025, up from 13.5% in the previous year. Adoption was reported at 55% among large enterprises and 17% among small ones. Those figures measure use, not whether AI improved profit or productivity.
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 Use Is Not Yet Profit
The gap matters because companies may treat buying or piloting AI as evidence of business transformation when the financial results remain limited or unmeasured. The survey figures suggest that deployment alone is not a reliable indicator of returns. They do not prove AI has no value: the measures differ, and the reported outcomes rely on respondents’ assessments.
The analysis points to changes in how work is organised as a possible dividing line. AI systems can process documents, retrieve information and take actions within set limits. To generate business value, companies may need to redesign an entire workflow, decide which steps remain under human review and set clear permissions for automated actions. The source attributes this approach to findings and guidance from BCG and Gartner.
For employees, the reported effects are mixed. The analysis says 80% of McKinsey respondents who use AI reported productivity gains. At the same time, 39% expected job cuts, up from 32% in the previous survey, though the article says last year’s reported cuts fell short of what respondents had expected. These survey responses indicate concern, not a confirmed forecast of job losses.
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From Digitised Forms to Automated Work
The article frames AI as a shift from digitising existing processes to having software handle more of the work itself. Earlier transformation projects often moved paper forms and records into software while people continued to interpret cases and make decisions. Newer AI tools can, in principle, read unstructured material such as emails or invoices, consult other systems and take specified actions.
That capability also changes the risks and the work required to deploy it. The analysis quotes Gartner guidance distinguishing agents for tasks requiring decisions, conventional automation for routine workflows and assistants for simple information retrieval. It also cites Gartner’s warning that agents added to older systems can disrupt workflows and be technically complex. BCG’s survey findings, as presented in the source, similarly associate larger gains with redesigning core workflows rather than relying on isolated pilots.
There are signs of growth in agent use, but the figures are not equivalent to proof of returns. The article reports that 40% of companies with more than $1 billion in revenue said they were scaling agents, compared with 27% previously, citing McKinsey results reported by The Register and TechTimes. It also cites Gartner forecasts that more than 40% of agentic AI projects could be cancelled by the end of 2027 because of cost, unclear value or weak risk controls. Forecasts describe expected outcomes, not completed cancellations.
What the Surveys Cannot Establish
The figures do not offer a single, directly comparable measure of AI’s business impact. The studies differ in sample, definitions and questions, and much of the evidence is based on what respondents say their organisations use or achieve. The source article does not provide enough methodological detail here to determine how representative each result is or whether reported financial effects were independently audited.
It is also unclear how much of the reported EBIT impact can be attributed specifically to AI rather than other changes in a company. The figures do not show which industries or individual applications account for the results, nor do they establish that the expected growth in agent use will produce higher profits. Gartner’s project-cancellation estimates are forecasts, while BCG’s 2028 agent-value figure is an expectation reported by survey respondents.
Evidence to Watch in 2027
The next test is whether businesses can show repeatable results beyond adoption and pilot counts. That means tracking financial and operational measures against a stated baseline, while identifying which processes changed and what role AI played. The source analysis recommends concentrating on a limited set of use cases, redesigning workflows and setting rules for what agents may do.
Gartner’s forecast puts project cancellations by the end of 2027 among the developments to watch, particularly for agentic AI initiatives with unclear value, high cost or weak risk controls. Further survey releases may show whether reported EBIT effects and adoption rates move together. Until then, the available findings describe a gap between use and reported returns, not a settled verdict on AI’s long-term financial impact.
Key Questions
How many companies in the cited McKinsey survey reported AI affecting EBIT?
37% of respondents said AI had some effect on their company’s EBIT in McKinsey’s 2026 survey of 1,719 respondents. The source says that share was about the same as a year earlier.
What does McKinsey’s 6% high-performer figure mean?
The analysis says 6% qualified as high performers because they attributed at least 5% of company EBIT to AI. This is a survey classification, not an independently verified finding that AI alone caused that share of EBIT.
How widespread was AI use in EU businesses in 2025?
Eurostat data cited in the article says 20.0% of EU enterprises with at least 10 employees used AI in 2025, up from 13.5%. The reported rate was 55% for large enterprises and 17% for small ones.
Why do some AI projects fail to produce measurable value?
The analysis points to fragmented pilots, workflows that have not been redesigned, missing expertise, data-readiness problems and weak governance. Gartner forecasts that more than 40% of agentic AI projects could be cancelled by the end of 2027 because of cost, unclear value or risk controls; that is a forecast, not a completed outcome.
Source: ThorstenMeyerAI.com
