🔍 Read the full analysis: AI’s Profit Problem: Broad Use, Few Companies Cashing In on ThorstenMeyerAI.com
TL;DR
Surveys cited in a November 6, 2026 report show that corporate AI use is spreading, but significant financial returns remain limited. McKinsey found 6% of respondents’ companies attributed at least 5% of EBIT to AI, while BCG found 5% of surveyed companies had created value at scale.
Corporate AI use is spreading faster than reported profit gains, according to a review of surveys and forecasts published November 6 by ThorstenMeyerAI.com. In McKinsey’s 2026 survey, 6% of respondents said AI accounted for at least 5% of their company’s EBIT, while Boston Consulting Group found that 5% of 1,250 surveyed companies were getting value from AI at scale.
The findings point to a gap between adoption and measurable business returns. McKinsey’s survey of 1,719 respondents found that 37% said AI had some effect on their company’s earnings before interest and taxes (EBIT), about the same share as a year earlier. The 6% classified as “high performers” said AI accounted for at least 5% of EBIT, according to coverage of the survey by The Register and TechTimes.
BCG reported a similar concentration of results: 5% of 1,250 companies were getting value at scale, while 60% reported no material value. The source article says BCG’s survey respondents attributed about 17% of their AI value to agents and expected that share to reach 29% by 2028. Agents are systems designed to take steps toward a task, rather than only responding to a prompt.
Adoption figures vary by geography and company size. Eurostat reported that 20% of EU enterprises with at least 10 employees used AI in 2025, up from 13.5% in 2024. Usage was reported by 55% of large enterprises and 17% of small enterprises. These figures measure use, not whether AI improved profits.
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 Adoption Has Not Equaled Profit
The figures matter because companies are moving beyond tests of chatbots and other tools toward decisions about how work is organized. The report describes a shift from digitizing a process—putting forms and records on screen—to having software handle parts of the work, such as reading an invoice, checking it against a contract and flagging exceptions for a person.
That shift can affect costs, staffing and accountability, but adoption alone does not establish a financial return. The survey results suggest that many companies have not yet translated AI use into material value. For executives, the difference may depend on whether a system changes an entire workflow or is simply added to existing software. For employees, AI may support productivity while also prompting concerns about job cuts; those effects will vary by role and company.
The report cites McKinsey respondents who use AI: 80% said it made them more productive. At the same time, 39% expected job cuts, up from 32% the previous year. The source notes that prior reported job reductions fell well short of respondents’ earlier expectations. These are survey responses, not a forecast that a particular share of jobs will disappear.
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From Digitized Tasks to AI Workflows
Earlier digital transformation projects often moved paper processes into software while leaving people responsible for interpreting cases and making decisions. The report argues that AI can take on more of those intermediate steps by reading unstructured material, checking information across systems and carrying out actions within set limits.
That approach also changes what a transformation programme must address. Gartner’s guidance, as quoted in the source, distinguishes agents for tasks requiring decisions, automation for routine workflows and assistants for simple information retrieval. Gartner warns that adding agents to older systems can be technically complex and disrupt existing workflows; it says companies may need to redesign processes rather than attach AI to them.
The source also reports that BCG’s higher-performing companies had deployed 62% of their AI initiatives, compared with 12% among laggards. Those figures describe deployment rates reported in BCG’s research; they do not by themselves prove that deployment caused stronger financial results. Eurostat’s lower overall EU usage rate also shows that adoption remains uneven, particularly between small and large firms.
What the Surveys Cannot Yet Show
The reported measures are not directly interchangeable. Eurostat measures whether enterprises use AI; McKinsey asks respondents about AI’s effect on company EBIT; and BCG reports whether companies are creating value at scale. The surveys also cover different groups and rely on reported responses, so they do not establish a single adoption-to-profit rate for all businesses.
It is also unclear from the source material how much of the reported financial impact is attributable to AI rather than other changes, or how results differ by industry, specific tool and length of deployment. Forecasts about agents and cancelled projects are projections, not final outcomes. Gartner’s estimate that more than 40% of agentic AI projects may be cancelled by the end of 2027 is tied to cost, unclear value or weak risk controls; it is not a count of projects already cancelled.
The Test Is Measurable Results
Companies will need to show whether AI projects improve defined outcomes as they move from pilots into everyday operations. The report points to workflow redesign, focused use cases, measurement of value and rules governing what agents can do as factors associated with stronger results.
Gartner forecasts that more than 40% of agentic AI projects will be cancelled by the end of 2027 because of cost, unclear value or weak risk controls. It also forecasts that organizations will abandon 60% of AI projects that lack AI-ready data. Those projections make project selection, data readiness and oversight near-term tests for businesses, not guarantees about what will happen.
Further survey results and company-level disclosures will show whether the gap between AI adoption and financial returns narrows. For now, the cited research shows widespread experimentation and uneven reported value, while the scale and durability of AI’s effect on profits remain unsettled.
Key Questions
How many companies report significant profit gains from AI?
In McKinsey’s 2026 survey, 6% of respondents qualified as “high performers,” meaning they attributed at least 5% of company EBIT to AI. BCG separately found that 5% of 1,250 surveyed companies were getting value at scale.
How common is AI use among EU businesses?
Eurostat reported that 20% of EU enterprises with at least 10 employees used AI in 2025, compared with 13.5% in 2024. Usage was 55% among large enterprises and 17% among small enterprises.
Why are companies struggling to turn AI use into profit?
The source report points to projects that remain isolated pilots, fail to redesign workflows or lack clear value measures and suitable data. Gartner also identifies cost and weak risk controls as reasons agentic AI projects may be cancelled.
What does the report say about AI and jobs?
McKinsey survey respondents who use AI often reported productivity gains: 80% said it made them more productive. However, 39% of respondents expected job cuts, compared with 32% the year before. These are survey responses and do not establish how many jobs will be eliminated.
Source: ThorstenMeyerAI.com
