🔍 Read the full analysis: How To Make AI Profitable When Most Companies Don’t on ThorstenMeyerAI.com
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TL;DR
Surveys cited by ThorstenMeyerAI.com show a gap between companies using AI and those reporting material financial returns. McKinsey found 6% of respondents’ organizations qualified as AI high performers, while BCG reported that 5% of surveyed firms were getting value at scale. The figures point to workflow redesign, data, skills, cost and governance as key challenges, but the surveys do not establish that any one approach will guarantee profit.
A November 6 analysis on ThorstenMeyerAI.com says that AI use is spreading across businesses, but reported financial returns remain concentrated among a small share of companies. The article cites a 2026 McKinsey survey in which 6% of respondents qualified as high performers, defined as attributing at least 5% of earnings before interest and taxes (EBIT) to AI, and a BCG survey in which 5% of firms said they were getting value at scale.
The figures come from separate studies with different samples and definitions, so they are not directly interchangeable. McKinsey surveyed 1,719 respondents; according to coverage by The Register and TechTimes, 37% said AI had some effect on their organization’s EBIT, about the same share as a year earlier. The 6% high-performer figure uses a stricter threshold: at least 5% of EBIT attributed to AI. BCG surveyed 1,250 companies and reported that 5% were achieving value at scale, while 60% reported no material value.
Adoption figures show a different picture from the financial-return measures. Eurostat reported that 20.0% of EU enterprises with 10 or more employees used AI in 2025, up from 13.5% in 2024. Use varied by company size: 55% of large enterprises and 17% of small enterprises reported using AI. These are adoption measures, not evidence that the companies made money from it.
The analysis argues that companies often get stuck when they treat AI as a tool to add to an existing process rather than a reason to redesign the process itself. It points to agents that can read incoming information, retrieve relevant records and take permitted actions, with people overseeing exceptions. The author says this approach makes workflow design, data readiness and governance part of the business case, alongside choosing an AI model or software product.
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 Has Not Paid Off
The gap between reported use and reported returns matters because companies may be spending on software, integration and staff time without seeing a material effect on profits. The cited surveys suggest that adoption alone is not a reliable measure of business value: using AI somewhere in an organization does not show whether it has changed costs, revenue or productivity enough to affect EBIT.
The analysis frames the challenge as an operational one. If AI handles part of a workflow, a company still needs to decide what the system may do, how staff review its work and who is accountable when it makes an error. A pilot that produces a convincing demonstration may not work at scale if it depends on poor-quality records, does not connect safely to existing systems, or leaves the underlying process unchanged. These are explanations advanced in the article and survey material, not proof that every unsuccessful project failed for the same reason.
There are potential workforce effects as well. The article reports that 80% of McKinsey survey respondents who use AI said it had made them more productive. It also says 39% expected job cuts, up from 32% in the earlier survey, while expectations of cuts the previous year exceeded the cuts respondents later reported. Those responses reflect expectations and reported experience; they do not establish how employment will change across the economy.
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Adoption, Returns and Agent Projects
The source compares several kinds of evidence: official European adoption data, company surveys and industry forecasts. Those sources measure different things. Eurostat’s figure tracks whether firms use AI; McKinsey’s figures report respondents’ views of AI’s effect on EBIT; BCG’s findings describe companies’ reported progress in achieving value at scale. Their percentages should not be read as a single trend line or as a controlled comparison.
AI agents are a particular focus because, unlike a conventional assistant that mainly retrieves information or drafts text, an agent may take multiple steps and act within permissions set by an organization. The article says BCG respondents attributed about 17% of their AI value to agents and expected that share to reach 29% by 2028. Those are survey responses and expectations, not independently verified measurements of future returns. McKinsey reporting cited by The Register and TechTimes says that among companies with more than $1 billion in revenue, 40% reported scaling agents, compared with 27% previously.
Forecasts also underline the risks. Gartner predicts 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 organizations will abandon 60% of AI projects that lack AI-ready data. The article says 71% of European firms that considered AI but did not use it cited missing expertise. These figures describe forecasts or reported barriers, rather than confirmed outcomes for every organization.
“The new promise is not “digitise the process” but “let software process the work.””
— Thorsten Meyer, ThorstenMeyerAI.com
What the Survey Figures Cannot Show
The surveys do not establish that AI caused the reported financial results. They rely on respondents’ assessments, and the source material does not provide enough detail to compare the studies’ methods, industry mix or definitions of AI value directly. The 37% reporting some EBIT effect and the 6% meeting the high-performer threshold are distinct measures, not contradictory estimates of the same outcome.
It is also unclear how much of the reported productivity improvement will translate into lasting cost savings or higher revenue, and how widely the workforce expectations will match actual job changes. Gartner’s cancellation figures are forecasts through 2027, not a count of projects already cancelled. The cited material does not identify a universal workflow, company size or AI product that reliably produces profit.
Track Results Beyond the Pilot
The next test for companies will be whether projects move from pilots into routine operations with measurable business results. The source article recommends selecting a limited number of use cases, redesigning the relevant workflows, setting permissions and human review, and tracking value against a defined baseline. Those are recommendations, not guarantees of a return.
Gartner’s forecast puts project cancellations through the end of 2027 on the watch list, while BCG’s agent-value expectations extend to 2028. Future survey results may show whether companies are converting adoption into profit and whether agent projects deliver the value their sponsors expect. Until then, companies’ reported use, anticipated benefits and demonstrated financial effects should be treated as separate measures.
Key Questions
How many companies in the cited surveys report strong AI profit gains?
In the 2026 McKinsey survey cited by the article, 6% of respondents qualified as high performers by attributing at least 5% of EBIT to AI. BCG separately reported that 5% of its surveyed companies were getting value at scale. The studies use different samples and definitions.
Does widespread AI use mean companies are making money from it?
No. Eurostat measures AI use among EU enterprises, while McKinsey and BCG report different measures of perceived financial effect or value at scale. Adoption by itself does not establish a profit gain.
Why do AI projects fail to reach scale?
The source cites costs, unclear business value, weak risk controls, missing expertise and data that is not ready for AI. Gartner forecasts that more than 40% of agentic AI projects will be cancelled by the end of 2027, but that is a forecast rather than a confirmed cancellation rate.
What does the article recommend companies change?
It argues for redesigning workflows around specific business needs, setting limits on what AI agents can do, keeping people responsible for exceptions and measuring results against a clear baseline. The article does not claim these steps guarantee profit.
Source: ThorstenMeyerAI.com
