AI marketing use by country & business size: a 27-country lookup

The first 2 columns below answer how widespread marketing-or-sales AI use was across all covered enterprises in each size class. They include businesses that used no AI. The next 2 ask how common that purpose was among enterprises already using AI. They describe a narrower group within the same country & size class.

Start with the country row, keep the size class attached & read the column heading before carrying a percentage into a proposal. “Among AI users” is part of the result. Removing those words would turn a statement about a subset into a statement about the whole covered market.

All 27 countries remain in English country-name alphabetical order. Each has 4 published points, giving 108 values in this table. The table shows the published values with 2 decimal places; the download also includes the source flags. The number of displayed decimals is a presentation choice, not a claim about survey precision.

The lookup is designed for a specific handoff: a colleague should be able to see which country, business size & comparison group a statement describes. It does not rank markets or combine the country rows into an EU average. A national reference can frame a question; the client’s own circumstances still supply the answer.^[1]

Small enterprises in this study have 10–49 people working in the business, including employees & self-employed people. Large enterprises have at least 250. We selected these 2 published classes to make a consistent comparison; firms with 50–249 people are outside this particular table.^[2]

The activity scope is also defined. Eurostat’s enterprise survey covers a specified set of non-financial industries. A country row combines those covered activities rather than describing marketing agencies, retailers or manufacturers separately. A business can use the country reference while recording that it is broader than its own sector.

The table does not cover firms with fewer than 10 persons, solo creators, consumers or workers as individuals. Creators pitching business clients may find the comparison useful, but their own AI habits were not measured here. A small studio should not assume that the small-enterprise column describes studios of its size.

The source records marketing or sales as one purpose, so reported use may concern either activity. A content proposal needs to say which task it addresses. These figures do not isolate social content creation, generative AI or a specific tool. Keeping the combined definition visible prevents a broad use category from becoming a claim about an exact workflow.^[1][5]

Austria & Belgium are the first 2 countries in the alphabetical table. We use them to explain the measures, rather than choose countries for their position in a ranking. The example below retains any-AI use alongside the 2 marketing-or-sales measures, with the same definitions for each size class.

In Austria, 26.18% of small enterprises & 68.26% of large enterprises used any AI. Marketing-or-sales AI use across all covered enterprises was 11.63% & 25.31%, respectively. Among AI users, the published marketing-or-sales points were 44.41% for small firms & 37.08% for large firms. The broad-market comparison & the comparison among existing users give a reader different context.

Belgium offers a second reading. Any-AI use was 28.82% for small enterprises & 76.41% for large ones. Marketing-or-sales use across all covered enterprises was 7.35% & 29.68%; among AI users, it was 25.52% & 38.84%. The conditional points should stay attached to the businesses they describe, rather than being substituted for the whole-market figures.

These are published point estimates. The Austrian example does not establish that an underlying population difference is certain, that size causes a choice of AI purpose or that small firms use marketing AI more intensively. The survey points describe whether the purpose was reported. They supply no measure of time spent, campaign quality, budget or return.

Eurostat already publishes analysis of AI purposes by enterprise size at EU level, as well as country-level purpose results. Our contribution is the complete country-by-size lookup with both marketing denominators side by side. A reader can inspect the corresponding pairing for their country instead of borrowing the EU-wide pattern.^[3]

Compare the 2 all-enterprise columns when the question concerns how widespread reported marketing-or-sales AI use was in the covered market. Compare the 2 AI-user columns when the question concerns its place among businesses already using AI. Changing both business size & comparison group at once makes the interpretation harder to follow.

Any-AI prevalence explains why both views are worth keeping, but it is not a performance score. An enterprise can use AI for a purpose outside marketing or sales. The conditional measure asks about marketing or sales within the group of AI users; it does not describe whether the technology was purchased, built internally or supplied by an agency.

We used Eurostat’s directly published conditional percentages. Each remains a source estimate, rather than a ratio reconstructed from the displayed marketing & any-AI percentages. Separate published values can have their own rounding & estimation properties. Reading the 3 measures together does not require their formatted values to multiply or divide into an exact identity.

For a cross-border brief, retain the country-specific rows rather than average them. Country samples, activity mixes & business populations differ. Equal weight for every country would create a new measure, not the share of enterprises across the EU. This study supplies neither that aggregate nor a causal explanation for differences between countries.^[4]

Consider an invented Austrian wholesaler with 28 people working in the business. Its owner confirms that the business currently uses no AI. An independent creator is discussing a recurring product-content assignment. The business, confirmation & assignment are fictional; the Austrian survey values remain the actual estimates above.

The broad-market note can say: “In the 2025 covered small-enterprise population in Austria, 11.63% reported AI use for marketing or sales.” The creator should not present the 44.41% figure among AI users as the share of all comparable small businesses. More importantly, the owner’s confirmation already establishes this particular client’s current position. The national figure does not override it.

The next discussion can therefore concern the proposed task: what the client wants help producing, which materials can be used & who reviews the work. These are suggested briefing questions, not causes of nonuse measured by our study. The data do not show whether this fictional company lacks skills, objects to AI or would pay for a new service.

The creator’s proposed handoff might read: “Prepare a monthly product-content brief from materials the owner supplies. Agree which parts, if any, may use AI; name the person reviewing accuracy before publication. Country context: Austria, small covered enterprises, 2025; marketing-or-sales AI use across all covered firms, 11.63%.” This is an invented assignment, not a service tested in the survey. Its value here is that the statistical context & the proposed responsibility can be read separately.

Now consider an invented Belgian wholesaler with 320 people working in the business. Its marketing lead confirms that it already uses AI for stock planning, but has not adopted it for marketing or sales. The agency records that confirmation separately from the benchmark. For large Belgian AI users, the relevant published marketing-or-sales point was 38.84%; across all covered large enterprises, it was 29.68%.

This second brief can ask what an additional marketing use would require within an existing AI-using business. It can identify the proposed output, the person approving it & the question the client wants to resolve. The country statistic does not prove that stock-planning tools transfer to marketing, that the client is ready to expand use or that an agency proposal will succeed.

The agency’s second handoff can be equally concrete: “Map the existing stock-planning process & the proposed marketing task separately. Ask which information may be used for marketing, who owns the output & what the client wants to learn. Benchmark context: Belgium, large AI-using enterprises, 2025; 38.84% reported marketing-or-sales AI use.” The confirmation makes the AI-user reference relevant to the discussion; it still does not reveal this client’s requirements or authorize a particular use of its information.

Neither fictional client is a survey observation we can identify. The examples show how known client information changes the conversation. A creator or agency should ask about that information directly, then use the table as dated context. A national benchmark is useful when it makes a proposal clearer, rather than when it stands in for learning what the client actually does.

A reusable note can begin: “Client country: __. Business size: __ persons, including employees & self-employed persons. Known AI use: __, confirmed by __ on __. Proposed marketing task: __.” The confirmation belongs to the client discussion. The survey does not fill in those blanks for an individual business.

Add the statistical context on the next line: “Eurostat, 2025; covered non-financial enterprises; country __; size class __; marketing or sales AI use __%; comparison group: all covered enterprises / AI-using enterprises.” Choose one group explicitly. If both percentages matter, give each its own label instead of leaving 2 unexplained numbers.

Then record the decision still needing evidence: “What we need to learn before proposing the work: __. Who will review the output: __. What would count as a useful result for this client: __.” Those are practical suggestions for a brief. The study does not test this template or establish that using it improves campaign outcomes.

Keep the source period visible as the note moves from researcher to account lead to creator. A proposal written in 2026 can cite 2025 evidence accurately. Updating the proposal date does not update the survey. That small distinction preserves the reference without giving a colleague the impression that the table measures adoption at the time of the pitch.

When a colleague receives the note, they can check the match before reusing it. A different business size calls for the corresponding source class. An unknown AI status calls for a client question, rather than an assumption based on the country percentage. If the conversation establishes that the business is outside the covered population, keep that mismatch visible & find evidence suited to its actual circumstances.

The fixed source extract contains a value for every selected country, size & measure: 162 published points across the 3 series. No selected value was missing. The Netherlands small-enterprise observations carry Eurostat’s “b” flag in all 3 series, indicating a break in the time series. We keep that flag attached to the row & in the downloads.^[6]

This article compares the 2025 cross-section. It makes no before-and-after adoption claim, including for the flagged Dutch observations. An available number is not permission to disregard a source qualification. If a colleague later wants a trend, the relevant country notes & historical series need their own examination.

The estimates also do not establish a country’s supply of clients, demand for an agency or likelihood of buying a tool. A national business population contains different activities & operating circumstances. Use the table to make the reference in a brief more precise, while keeping the client conversation responsible for the commercial assumptions.

We fixed the 27 EU member countries & the 2 size classes before deriving the table. From Eurostat dataset isoc_eb_ai, we selected 2025, the covered non-financial activity aggregate C10-S951_X_K & the marketing-or-sales indicator E_AI_PMS. We retrieved its all-enterprise unit PC_ENT & its AI-user unit PC_ENT_AI_TANY directly. Any-AI context uses E_AI_TANY with PC_ENT.^[1]

The source was acquired October 24, 2026; all 3 responses reported the same dataset update of June 15, 2026. We retained the 162 original points & flags, with all countries in English country-name alphabetical order. The main table presents the 108 marketing values. The worked example presents all 3 measures for Austria & Belgium, giving 12 cells.

The complete data also retain 54 descriptive differences: the large-enterprise published point minus the small-enterprise point, separately for each marketing denominator. These are literal arithmetic comparisons of published estimates. We do not turn their signs into country categories, counts, rankings or claims about certain population differences.^[7]

The source review did not establish a fixed rounding rule for the published estimates, & we did not calculate uncertainty intervals for these comparisons. The number of digits alone cannot supply that information. We therefore describe published points without claiming statistically certain differences or an ordering that survives source rounding. The raw values remain available alongside the table’s 2-decimal display.

The package preserves the fixed selection, source responses, flags, values & calculation method. This is an independent adaptation of Eurostat data, with presentation & interpretation by Influence IQ; Eurostat is not responsible for those changes.^[8] The intended handoff is simple: a complete country reference beside a clearly described client, with the unanswered business questions left visible.