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AI adoption is rising, but surveys cited in a November 6, 2026 report show that only a small share of companies attribute substantial profit gains to it. The findings point to workflow redesign, clear value measures and stronger data and risk controls as key conditions for scaling AI.
A report published November 6, 2026, brings together survey findings showing that companies are adopting AI faster than they are turning it into measurable profit. In McKinsey’s 2026 survey, 6 percent of respondents said AI contributed at least 5 percent of their company’s earnings before interest and taxes, while 37 percent reported some effect on that measure.
The figures come from surveys with different populations and definitions, so they do not form a single league table. McKinsey surveyed 1,719 respondents; its results, as reported by The Register and TechTimes, show the share reporting an AI effect on earnings before interest and taxes remained around 37 percent, while the 6 percent classified as “high performers” attributed at least 5 percent of EBIT to AI. These are respondents’ reports, not audited company accounts.
Boston Consulting Group’s 2025 study of 1,250 companies found a similar concentration of returns: 5 percent said they were creating value from AI at scale, while 60 percent reported no material value. BCG respondents said AI agents accounted for about 17 percent of AI value at the time and expected that share to reach 29 percent by 2028. Those figures reflect survey responses and expectations, not independently verified outcomes.
Adoption varies by company size and geography. Eurostat reported that 20 percent of EU enterprises with at least 10 employees used AI in 2025, up from 13.5 percent in 2024. Use reached 55 percent among large enterprises and 17 percent among small ones. In McKinsey’s survey, 40 percent of respondents at companies with more than $1 billion in revenue said their firms were scaling AI agents, compared with 27 percent a year earlier, according to The Register and TechTimes.
How to Make AI Pay Off at Your Company
AI adoption is growing faster than reported profit gains. The surveys point to redesigned workflows, clear value measures and strong controls as conditions for scaling.
Adoption is not profit
Surveys show a gap between companies using AI and those reporting substantial value.
Value remains concentrated
BCG found 5% of companies created value from AI at scale; 60% reported no material value.
Productivity and uncertainty
Among AI users, 80% said AI made them more productive. Expected job cuts are expectations, not confirmed outcomes.
Redesign the whole workflow
AI is more likely to deliver value when it changes how work gets done, with people handling cases that need judgment.
Start with a limited number of workflows.
Measure whether the workflow delivers that value.
Decide what AI may access, approve or send, and when people review.
Readiness shapes who can scale
Adoption differs by company size, while expertise and deployment remain obstacles.
| Signal | Finding | What it suggests |
|---|---|---|
| EU enterprise use | 55% large; 17% small | Adoption varies by size |
| AI expertise | 71% of considering non-users cited a skills gap | Build staff capability |
| Initiatives deployed | 62% among BCG leaders; 12% among laggards | Execution separates leaders |
Why AI Gains Stay Concentrated
The results suggest that buying AI tools or running pilots does not, by itself, establish a route to higher profits. The report argues that returns are more likely when companies redesign a complete workflow around AI: for example, routing an invoice through checks and exception handling, with a person reviewing cases that need judgment. That approach can change how work is divided between software and staff, rather than simply adding another screen to an existing process.
The stakes extend to employees. In McKinsey’s survey, 80 percent of respondents who use AI said it made them more productive. At the same time, 39 percent of respondents expected job cuts, up from 32 percent the prior year. The source notes that earlier expected cuts exceeded the cuts reported afterward, so these figures describe expectations, not confirmed workforce outcomes.
For executives, the gap between adoption and reported returns makes measurement and oversight practical concerns. A company that cannot connect an AI deployment to a defined business result may struggle to decide whether to expand it, change the workflow or stop the project. Systems that can take actions on their own also raise questions about what they may access, approve or send without human review.
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From Digital Tools to Workflow Automation
Earlier digital transformation programmes often moved paper processes into software while leaving people to interpret each case and make decisions. The report describes AI as a shift toward systems that can read unstructured inputs, retrieve information from other systems and take specified steps within set limits. AI agents are presented as one part of that shift: systems that perform multiple steps, rather than only generating a response.
The underlying adoption numbers show why the shift is uneven. Eurostat measures whether enterprises use AI, not whether it improves financial performance. Corporate surveys ask executives to assess impact and value, using their own reporting and study definitions. The figures can indicate patterns, but they should not be treated as directly comparable measures of the same outcome.
Execution and readiness remain obstacles. BCG reported that its leading companies had deployed 62 percent of their AI initiatives, compared with 12 percent among lagging companies. Eurostat found that among EU firms that had considered AI but did not use it, 71 percent cited a lack of relevant expertise. Gartner forecasts, as summarized in the source material, say more than 40 percent of agentic AI projects could be cancelled by the end of 2027 because of cost, unclear value or weak risk controls. Gartner also forecasts that organizations will abandon 60 percent of AI projects that lack AI-ready data. These are forecasts, not observed cancellation rates.
“Six percent qualified as “high performers,” attributing at least 5 percent of their company’s EBIT to AI.”
— McKinsey 2026 survey respondents, as reported by The Register and TechTimes
What the Surveys Cannot Establish
The survey figures do not establish that AI caused the reported financial or productivity effects. They rely on respondents’ assessments, and the studies use different samples and definitions. The source material does not provide audited financial data that would verify the reported EBIT contributions across companies.
It is also unclear how many agent projects will reach deployment or deliver the returns respondents expect by 2028. Gartner’s cancellation estimates are forecasts, and companies’ reported plans may change. The cited figures on job cuts measure expectations; they do not show how many jobs have been lost specifically because of AI.
How Companies Will Test AI Value
The next test for companies is whether they can move beyond isolated pilots and show results from defined workflows. The report recommends choosing a limited number of use cases, measuring their business value and setting rules for what AI systems may do. It also points to data readiness and staff expertise as conditions that can determine whether projects progress.
For investors, employees and executives, the available milestones are the next rounds of company reporting and survey results. They may show whether the share of firms reporting financial gains rises, whether agent deployments expand, and whether Gartner’s projected cancellations occur. The current evidence does not settle those questions.
Key Questions
How many companies report major profit gains from AI?
In McKinsey’s 2026 survey, 6 percent of respondents said AI contributed at least 5 percent of company EBIT. The result is self-reported and should not be read as an audited estimate of all companies.
How widely was AI used in the EU in 2025?
Eurostat reported AI use at 20 percent of EU enterprises with at least 10 employees, up from 13.5 percent in 2024. The rate was 55 percent among large enterprises and 17 percent among small ones.
What are AI agents in this report?
They are AI systems that can take multiple steps toward a task, such as reading an input, looking up information and acting within limits set by an organization. The report describes them as more action-oriented than systems that only answer questions.
Why do AI projects fail to scale?
The cited research points to unclear value, cost, weak risk controls, inadequate data and limited expertise. Gartner’s cancellation figures are forecasts, while Eurostat’s expertise figure comes from EU enterprises that considered AI but did not use it.
Source: Thorsten Meyer AI
