AI for marketing and sales in Spain and the EU: 2025 data

In 2025, 6.0% of companies in Spain (10+ employees, excluding finance) use AI for marketing or sales versus 6.9% in the EU‑27, according to Eurostat’s ICT usage in enterprises survey (dataset isoc_eb_ai; updated 2026‑06‑15). By size in Spain: 4.6% in 10–49 employees, 10.5% in 50–249 and 22.6% in 250+ (Eurostat, 2025).

Key takeaways

• Spain: 6.0% of companies use AI for marketing or sales in 2025; EU‑27: 6.9% (Eurostat, isoc_eb_ai).
• By size in Spain (2025): 4.6% (10–49), 10.5% (50–249), 22.6% (250+).
• Spain’s adoption grew from 2.2% (2023) to 6.0% (2025) for AI in marketing/sales.
• At least one AI technology in use: 20.3% of firms in Spain vs 20.0% in the EU‑27 (2025).
• Countries: Netherlands 12.3% and Germany 9.1% vs Portugal 4.3% and France 1.0% (2023) for AI in marketing/sales.

How many companies use AI for marketing and sales in 2025

According to Eurostat’s survey on the use of ICT in enterprises (dataset isoc_eb_ai, updated 2026‑06‑15), in 2025, 6.0% of Spanish companies (10+ employees, excluding the financial sector) use AI for marketing or sales. In the EU‑27 the figure is 6.9%. Among reference countries, the Netherlands reaches 12.3%, Germany 9.1%, Italy 5.4%, Portugal 4.3% and France 1.0% (France is 2023 data).

Adoption is rising quickly in Spain: from 2.2% in 2023 to 3.3% in 2024 and 6.0% in 2025. The baseline is modest, but the growth suggests many firms are moving from pilots to operational use in areas like content for buyers, lead qualification and sales automations.

It helps to read this metric alongside broader AI adoption: 20.3% of Spanish firms report using at least one AI technology for any purpose (EU‑27: 20.0%), yet only 6.0% apply AI specifically to marketing or sales. This gap is a clear opportunity for B2B marketing and commercial teams to repurpose existing AI capabilities toward demand capture and revenue.

If you are deciding where to start, check whether your organisation already uses language generation or text analysis in other processes; those skills and datasets can be extended into the go‑to‑market motion with relatively low friction.

1. Netherlands12.3 %
2. Germany9.1 %
3. EU-276.9 %
4. Spain6.0 %
5. Italy5.4 %
6. Portugal4.3 %
7. France1.0 %

Source: Eurostat (isoc_eb_ai), 2025

Companies using AI for marketing or sales (Eurostat, isoc_eb_ai). Percent of enterprises with 10+ employees, excluding the financial sector.

Scope	Value and year
Spain · Total (10+ employees)	6.0% (2025)
Spain · 10 to 49 employees	4.6% (2025)
Spain · 50 to 249 employees	10.5% (2025)
Spain · 250 or more	22.6% (2025)
EU‑27 · Total (10+ employees)	6.9% (2025)
EU‑27 · 10 to 49 employees	5.8% (2025)
EU‑27 · 50 to 249 employees	10.8% (2025)
EU‑27 · 250 or more	20.9% (2025)
Germany · Total (10+ employees)	9.1% (2025)
France · Total (10+ employees)	1.0% (2023)
Italy · Total (10+ employees)	5.4% (2025)
Portugal · Total (10+ employees)	4.3% (2025)
Netherlands · Total (10+ employees)	12.3% (2025)

Size differences: SMEs versus large firms

The size gap is significant. In Spain (2025), 4.6% of firms with 10–49 employees use AI for marketing or sales, 10.5% among 50–249, and 22.6% among 250+. In the EU‑27 the rates are 5.8%, 10.8% and 20.9% respectively (Eurostat, isoc_eb_ai). Larger organisations benefit from more data, specialised roles and stronger governance.

What this means for a B2B SME:

• Prioritise use cases close to revenue: automatic lead qualification, intent‑driven content, and CRM automations.
• Avoid big‑bang rollouts at first. Start with a focused pilot for a single product or segment.
• Measure what matters: conversion to SQL, cost per MQL and speed‑to‑lead from first contact to human follow‑up.

Practical example (industrial SME, Aragon, Spain)

1. Goal: increase sales‑qualified opportunities for one machinery line. 2) Data: connect web forms and inbound calls to the CRM. 3) AI: use language generation for proposal drafting and text analysis to classify inbound queries by intent (support vs purchase). 4) Flow: a chatbot captures key fields; a rules‑based score prioritises strategic accounts; an automation books demos.

Practical example (large enterprise, Catalonia, Spain)

1. Build a content hub with taxonomy by sector and use case. 2) Recommendation engine for ABM to personalise messages and assets by account. 3) Sales integration: alerts to SDRs when decision‑makers engage with high‑intent assets. 4) Governance: an AI committee with marketing, sales and legal.

SpainEU-27

1. 10–49 staff4.6 %5.8 %
2. 50–249 staff10.5 %10.8 %
3. 250+ staff22.6 %20.9 %
4. All (10+)6.0 %6.9 %

Source: Eurostat (isoc_eb_ai), 2025

Which AI technologies are powering marketing and sales

Broad AI usage is much higher than explicit marketing/sales use. In 2025, 20.3% of Spanish firms use at least one AI technology (EU‑27: 20.0%). For tasks directly applicable to the B2B funnel, 8.9% use AI that generates written or spoken language or code (EU‑27: 8.8%), and 10.1% use AI for analysis of written language (text mining) versus 11.8% in the EU‑27. Source: Eurostat, isoc_eb_ai (enterprises 10+ employees, excluding finance).

Spain’s evolution: language generation rises from 2.3% (2023) to 4.3% (2024) and 8.9% (2025); text mining grows from 3.1% (2023) to 5.1% (2024) and 10.1% (2025). These two families underpin high‑impact go‑to‑market work: intent‑aligned content creation and adaptation, automatic classification of leads and queries, and context‑aware chatbot responses.

Practical implications:

• With language generation you can accelerate bottom‑of‑funnel content (use cases, comparisons, recovery emails) while keeping editorial control.
• With text mining you can tag leads by intent and industry, surface active accounts, and spot frequent objections to equip sales.
• Combined, chatbots move from “FAQ” to assistants that capture buying signals and update the CRM.

Recommendation: inventory what is already live (for instance, if engineering or support teams use text AI) and extend it into the commercial cycle, with strict data quality and human oversight at the critical funnel touchpoints.

Content and SEO for AI: capturing B2B demand

Language generation (8.9% usage in Spain, Eurostat 2025) and text mining (10.1%) enable more precise, intent‑led content. Beyond traditional SEO, it pays to optimise for how generative AIs and assistants select and cite sources. That means clear entities, concise summaries and evidence that can be verifiably quoted.

Recommended steps:

1. Intent research: cluster buyer questions by stage (discovery, evaluation, purchase) using text analysis. 2) Assisted briefs: draft outlines and FAQs with AI, then have experts review for accuracy and claims. 3) Technical refresh: improve load speed, schema and internal linking to raise the probability of being referenced by assistants.

SEO for AI in practice:

• Adapt content to conversational phrasing and entity coverage.
• Provide executive summaries and crisp bullets; assistants prefer them for synthesis.
• Publish verifiable assets (implementation guides, glossaries, procedures) that models can cite.

Example (industrial SME in the Valencian Community, Spain):

• Phase 1: audit conversational queries and buyer questions.
• Phase 2: publish six use‑case pages by sector and three solution comparisons.
• Phase 3: build “How it works” pages for decision‑makers and review structured data.

Related real case: in Abujardar: a website twice as fast, 2× traffic in a month · ARTIC, technical improvements and SEO focus doubled traffic. Those foundations also boost the chances of being surfaced and cited by AI assistants. If you need help, see our AI SEO agency for B2B and the GEO SEO service for generative assistants.

AI chatbots: capture and qualify automatically

With 8.9% of firms in Spain using language generation (2025) and 10.1% using text analysis, chatbots can become a reliable capture and qualification layer. A well‑trained assistant can greet by context (page/product), collect key fields, ask about budget/timeline/role, create the lead in the CRM and propose a demo.

How to deploy on a B2B website:

1. Intent library: “pricing”, “integration”, “timelines”, “use cases”. 2) Qualification tree: identify decision‑maker, sector and urgency. 3) CRM integration: log each conversation with intent and product tags. 4) Human hand‑off: offer to switch to a person when high‑intent signals appear. 5) Continuous improvement: review logs and train responses.

Example (B2B software in Barcelona, Spain):

• The chatbot detects the intent “migrate from [tool]”.
• If the visitor is a decision‑maker with a near‑term project, it offers a demo and books a meeting.
• If the intent is early‑stage research, it shares a sector use case and asks for a corporate email.

Quality checklist: professional tone, disclaimers when context is missing, transparent logging of decisions, and periodic reviews of failure cases. An effective chatbot reduces response times and increases conversion to MQL/SQL when aligned with your scoring and routing rules. To implement it on web or WhatsApp, explore our AI chatbots for business built on Claude.

Lead scoring and CRM automation: from MQL to revenue

Automation coordinates a smooth handover from MQL to SQL. Use text analysis to classify queries, and business rules to score fit (industry, size, role, urgency). Qualified leads are routed to the right owner, nurtured with stage‑specific content, and accompanied by tasks and reminders.

Practical steps:

1. Define your ICP: industries, firm sizes and pain signals. 2) Map events: form submissions, use‑case downloads, chatbot interactions, pricing visits. 3) Weight signals: high intent (demos), medium (comparisons), low (blog reads). 4) Playbooks: timely follow‑up, proposal drafting with AI assistance, and SDR cadences. 5) Quality assurance: monthly audits of the scoring model with sales.

Real case: Robots de Trabajo: 844 B2B leads at €4.40 via Meta Ads · ARTIC shows how paid capture combined with CRM orchestration can scale lead generation efficiently. The same logic becomes even more effective when AI classifies and prioritises leads in real time.

To connect marketing and sales workflows, see our AI process automation for B2B companies. ARTIC can help align content, chatbots and CRM rules so that qualified demand reaches the right salesperson with full context.

Advertising in assistants: making the most of ChatGPT Ads

Conversational environments open a high‑intent channel. When you activate ads in assistants, your messages appear while users ask comparative or purchase questions. The key is to match the conversational context, offer genuinely useful assets (calculators, demos, comparisons) and define prompts or segments by industry and role.

Quick guide for a B2B manufacturer in Madrid, Spain:

1. Target intents: “alternatives to…”, “price of…”, “best for [industry]”. 2) Assets: sector use‑cases, on‑demand demo and an implementation guide. 3) Creatives: concise copy with clear bullets and a call to action that fits the question. 4) Measurement: CRM events (MQL/SQL) and cohorts by intent. 5) Iteration: adjust prompts and offers based on response rate and lead quality.

Implementation tips:

• Ensure a tight match between the ad and the assistant’s response, linking to resources that precisely answer the stated intent.
• Prepare answers to common objections (cost, integration, security) to speed up meeting booking.
• Coordinate with sales on a fast follow‑up play for high‑intent leads.

If you want to explore this channel, visit our ChatGPT Ads agency: advertising on ChatGPT in Spain. Well‑structured content and a connected CRM make conversational assistants a natural extension of the B2B funnel.

Measuring the return: metrics, attribution and board pack

No ROI without measurement. Build a dashboard that connects campaigns, content and automations to revenue. Track MQL/SQL volumes, stage‑by‑stage conversion rates, cost per MQL/SQL, speed‑to‑lead, pipeline created and sales cycle. For attribution, blend tactics: an intent‑aware model (for high‑intent touchpoints) and a contribution model (multi‑touch or data‑driven) so you do not undervalue discovery content.

Data checklist:

• Single source of truth: the CRM as the system of record for MQLs, SQLs, opportunities and closed‑won.
• Consistent tagging of campaigns and intents (UTMs plus CRM fields).
• Uniform events across chatbot, forms and downloadable content.
• Monthly reviews of data quality (duplicates, personal domains, inactive leads).

To estimate your return, use this calculator and test different scenarios by changing conversion rates, average deal value and sales cycle assumptions.

Estimate the return of your B2B lead generation

Enter your numbers. The values shown are just an example to get started.

Monthly marketing investment (€)Cost per qualified lead (€)Leads that become opportunities (%)Opportunities you win (%)Average deal value (€)Gross margin (%)

Leads per month

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New clients per year

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Estimated yearly sales

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Return on investment

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Indicative estimate: real results depend on the industry, the offer and sales capacity.

Get an estimate with my data

Board‑level reporting works best when it highlights pipeline impact (created and won), incremental cost per SQL, the key levers (chatbot, BOFU content, recovery emails) and the next experiments. ARTIC typically helps clients keep costs, assumptions and risks transparent so leadership can scale what works responsibly. For end‑to‑end enablement, see our AI agency for businesses.

A practical rollout plan for a B2B SME

A phased plan helps capture value quickly without over‑engineering the project. Focus on a single product or segment, connect the core data sources and ship the first customer‑facing improvements early.

Phase 1 (foundations):

• Goal: lift sales‑qualified opportunities for one flagship offer.
• Data: connect website, chatbot and forms to the CRM.
• Content: ship two bottom‑of‑funnel assets (a comparison and a use case) plus a clearer pricing page.

Phase 2 (automation and capture):

• Chatbot: intents, qualification and human hand‑off for high‑intent visitors.
• Scoring and routing: rules to assign accounts to SDRs/AEs and start nurturing.
• Paid discoverability: test conversational queries with offers that match the question.

Phase 3 (optimisation and reporting):

• Iterate prompts and content based on observed intent signals.
• Review data quality and follow‑up responsiveness; refine playbooks with sales.
• Board pack: pipeline created, cost per SQL and the next experiments.

Example (industrial services firm in Seville, Spain): select one core service and two priority sectors, activate the chatbot and scoring, publish the BOFU assets and improve pricing clarity, open a test for conversational queries, and report pipeline impact with clear recommendations. The ARTIC team can coordinate content, chatbots and automation with your CRM so you learn fast and scale confidently.

Frequently asked questions

What share of companies use AI for marketing and sales in Spain?

In 2025, 6.0% of Spanish companies (10+ employees, excluding finance) use AI for marketing or sales, according to Eurostat (dataset isoc_eb_ai; updated 2026‑06‑15). The EU‑27 average is 6.9%.

How does adoption vary by company size?

In Spain (2025), adoption is 4.6% among 10–49 employees, 10.5% in 50–249 and 22.6% in 250+. In the EU‑27, rates are 5.8%, 10.8% and 20.9% respectively (Eurostat, isoc_eb_ai).

Which AI use cases work for B2B demand generation?

Intent‑led content creation, SEO for generative assistants, AI chatbots that capture and qualify leads, CRM lead scoring and automated nurturing. These directly connect to MQL/SQL creation and pipeline.

Where should a resource‑constrained SME start?

Choose one product/segment and a clear outcome (more SQL). Connect the website and chatbot to the CRM, publish 2–3 bottom‑of‑funnel assets, and define simple scoring and routing. Review impact before scaling.

Does AI replace the marketing and sales teams?

No. AI speeds up tasks such as drafting, classification and responses and improves prioritisation, but commercial judgement, customer knowledge and closing remain human. The combination is what creates ROI.

How do I measure the ROI of AI in marketing and sales?

Attribute each lead and opportunity to its origin (content, chatbot, campaign), monitor MQL/SQL, pipeline created and revenue. Use an intent‑aware and multi‑touch approach, and review data quality monthly.

Which EU countries stand out in adoption for marketing and sales?

In 2025, the Netherlands (12.3%) and Germany (9.1%) sit above the EU‑27 average (6.9%), while Portugal (4.3%) and France (1.0%, 2023) are below, according to Eurostat.

Sources and methodology

• Eurostat, survey on ICT usage and e-commerce in enterprises: companies with 10 or more persons employed, excluding the financial sector (database).
• The linked client examples are real ARTIC projects. Calculators are indicative.