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Author: Evgeniya Schwaar 

AI Marketing Study Switzerland

The AI Marketing Study Switzerland is a nationwide research initiative analysing how organisations in Switzerland adopt, use, and operationalise artificial intelligence in marketing, including strategy, data readiness, automation, AI governance, and performance measurement.

A national initiative assessing the integration of Artificial Intelligence within Swiss marketing teams.

A multi-partner consortium

The Launch of the AI Marketing Study Switzerland

Switzerland, December 2025 – Brandfinity, in collaboration with Swiss Marketing, HEG Fribourg, PME Magazine, and M&K (Markt & Kommunikation), has launched the AI Marketing Study Switzerland, a national research initiative examining how Artificial Intelligence (AI) is integrated within Swiss marketing organisations.

Rigor and representativeness

The study was conducted by a consortium combining:

Academic rigor

Industry expertise

Media reach

Methodological credibility

This collaborative approach guarantees an analysis that is both methodologically rigorous and representative of Swiss market realities, ensuring credibility and balance.

Strategic informational tool

A clear and factual assessment of AI adoption

The AI Marketing Study Switzerland serves as a strategic information and benchmarking tool for the profession.

Its goal is to provide objective, transparent, and comparable data, enabling marketing and sales leaders to better understand their AI maturity and strategically navigate technological acceleration. 

The objective of the study is to provide a rigorous and evidence-based overview of AI integration within Swiss marketing structures.

The results you may find below in dashboard and analysis.

What the Study Measures

AI adoption level across marketing teams
Volume of AI-generated content by sector
  Time savings achieved through AI and automation
Automation maturity within marketing processes
Sector-specific AI usage patterns
Practical challenges and operational barriers

By focusing on measurable and comparable data, the study establishes a reliable benchmark for the Swiss market.

Target audience and participation

The study was designed for marketing and sales leaders across all Switzerland.

Participation was voluntary and anonymous.

In return, participants receive a confidential and comparative assessment of their organisation’s AI maturity level. This personalised overview enables them to position themselves objectively against Swiss companies of similar size operating within the same sector.

The resulting data provides a valuable foundation for structuring future strategic plans.

Approximately 200 marketing and sales leaders from both French-speaking, Italian-speaking and German-speaking Switzerland contributed to the study, reinforcing the relevance and representativeness of the findings for the Swiss marketing community.

Publication and results

The results of the AI Marketing Study Switzerland – National Study Dec 2025- Mar 2026 are now available, offering key insights into how Swiss marketing professionals (C level) are responding to the evolution of Artificial Intelligence.

PS. Study is closing at the end of March 2026.

Dashboard – results of the AI study in Switzerland

To preview the results, please kindly navigate in the dashboard area and choose the TAB you wish to preview. Additionally you may fish to apply filters. 

Enjoy browsing the results

AI adoption in marketing

Detailed Analyse findings of the AI study in Switzerland

Daily AI Usage Within Marketing Teams

One of the key indicators of AI maturity is the extent to which marketing teams use AI tools on a daily basis.

In response to the question “What proportion of your marketing team uses AI tools every day?”, almost 80% of respondents in Switzerland reported daily usage within their marketing teams. This suggests that AI is no longer perceived only as an experimental technology, but is increasingly becoming part of everyday marketing operations.

Comparison by linguistic region

When looking at the results by linguistic region, some differences appear:

• German-speaking Switzerland: 85%
• French-speaking Switzerland: 71%
• Italian-speaking Switzerland: 50%

German-speaking Switzerland shows the highest level of daily AI usage, with 85% of respondents reporting that AI tools are used every day by their marketing teams. French-speaking Switzerland also shows a strong level of adoption, at 71%, while Italian-speaking Switzerland appears to be more cautious or less advanced in daily AI integration, with 50% of respondents reporting daily use.

This difference may indicate varying levels of AI adoption, access to tools, internal training, or organisational readiness across linguistic regions.

Comparison by industry

Daily AI usage also varies across sectors:

• Education: 87%
• Industry: 80%
• Healthcare: 75%
• Finance: 73%
• Commerce: 69%
• Automotive: 50%
• Others : 84%

The education sector shows the highest level of daily AI usage, with 87% of respondents indicating that their marketing teams use AI tools every day. This may reflect the growing need for content creation, student communication, admissions support, and personalised engagement.

The industrial sector also shows a high level of adoption, at 80%, followed by healthcare at 75% and finance at 73%. The result for finance is particularly interesting, as this sector is often perceived as more regulated and cautious. However, the data suggests that AI is already being used regularly in marketing activities within financial organisations.

Commerce follows with 69%, while the automotive sector shows the lowest level of daily usage, at 50%. This may suggest that AI adoption in automotive marketing is still developing, or that its use is more concentrated in specific functions rather than across the entire marketing team.

Proportion of Marketing Content Created with AI

Another important indicator of AI maturity is the proportion of marketing content that is already being created with the support of AI tools.

The results show that AI is actively contributing to content production in many Swiss marketing teams, but full AI-driven content creation remains limited.

Overall results

In response to the question about the proportion of content created with AI:

• 4% of respondents say that more than 91% of their content is created with AI
• 11% say that 61–90% of their content is created with AI
• 32% say that 31–60% of their content is created with AI
• 34% say that 10–30% of their content is created with AI
• 17% say that less than 10% of their content is created with AI

This shows that, for most respondents, AI is already part of the content creation process, but mainly as a support tool rather than a full replacement for human production.

The largest groups are those creating 10–30% of their content with AI (34% of respondents) and 31–60% of their content with AI (32% of respondents). Together, these two groups represent 66% of respondents, suggesting that AI is becoming a regular content-production assistant in Swiss marketing teams.

At the same time, only 4% of respondents report that more than 91% of their content is created with AI. This indicates that highly automated content production remains relatively rare.

Time savings generated by AI

The data also shows that AI is beginning to create measurable productivity gains:

• 19% of respondents save around 6 hours per week
• 16% save between 3 and 6 hours per week
• 15% save up to 3 hours per week
• 49% still do not measure the time saved

This is an important point. While many teams are already using AI for content creation, almost half of respondents do not yet measure the time saved. This suggests that AI adoption is happening faster than performance measurement.

From a maturity perspective, this is a key gap: companies may already be gaining productivity, but without structured measurement, they cannot fully evaluate the business impact of AI on marketing operations.

Differences by company size

Looking at company size, an interesting pattern appears.

For companies generating up to 60% of their content with AI, the strongest representation comes from larger organisations, especially:

• Companies with 10,000+ employees
• Companies with 250–999 employees
• Companies with 5,000–10,000 employees

This suggests that medium-sized and large organisations are increasingly integrating AI into content workflows, but often in a controlled and partial way.

However, when looking at companies where more than 90% of content is created with AI, the picture changes. This level of AI-generated content appears only in two company-size categories:

• 1–9 employees: 15%
• 50–249 employees: about 4%

This may indicate that very small companies are more likely to rely heavily on AI for content production, possibly because they have limited internal resources and need to produce marketing content more efficiently.

In contrast, larger companies may use AI extensively, but still maintain stronger human validation, brand governance, compliance, and approval processes.

AI Search Optimisation: SEO, GEO and Visibility in AI Engines

Another important dimension of AI maturity is how companies are adapting their SEO practices for AI-powered search engines and answer engines such as ChatGPT, Perplexity, Gemini or Copilot.

In response to the question “Do you adapt your content structure to improve visibility in AI-powered search engines?”, 61% of respondents say they are already adapting their SEO approach to improve visibility in AI search results.

This shows that a majority of Swiss marketing teams are aware that traditional SEO is evolving. Visibility is no longer limited to classic search engine results pages. Brands also need to think about how their content can be understood, cited, summarised and recommended by AI systems.

However, the data also shows a clear maturity gap: only 19% of teams apply this type of optimisation systematically.

This means that while many companies have started to experiment with AI search optimisation, only a minority have fully integrated it into their content and SEO processes.

Optimisation and team size

An interesting finding is that, for now, this practice does not appear to depend strongly on the size of the marketing team.

This indicates that AI search optimisation is not only a matter of resources or team capacity. Smaller teams may be just as likely as larger teams to start adapting their content for AI visibility, especially if they are agile and already experimenting with new tools.

At the same time, larger teams do not necessarily appear to be more advanced in applying this optimisation systematically. This may suggest that the market is still in an early phase, where practices are being tested but are not yet fully structured or governed.

AI in A/B Testing, Advertising, Personalisation and Customer Support

The data shows that the use of AI in testing, advertising optimisation, personalisation and customer support remains less mature than in areas such as content creation or daily AI usage.

For A/B testing and advertising, only around 14% of respondents say they actively use AI for A/B testing. A further 18% are currently in a testing or experimentation phase. The majority of the repondants (about 75%) do not adapt the campaigns in reel time, but about 44 % monitor the performance of the brand with AI and track the traffic sources AI (ChatGPT, Perplexity etc).

This suggests that AI is starting to enter campaign optimisation processes, but it is not yet widely embedded in structured marketing experimentation. Many teams may still rely on traditional A/B testing methods, manual campaign analysis, or platform-native optimisation tools rather than dedicated AI-supported testing workflows.

Personalisation remains underdeveloped

The use of AI for personalisation also appears to be relatively limited. Only 8% of respondents say they use AI for personalisation with measurable impact, while 29% report partial use.

This is an interesting finding, as personalisation is often presented as one of the strongest use cases for AI in marketing. However, the data suggests that many Swiss organisations have not yet reached the level of data readiness, CRM integration, or process maturity needed to personalise customer journeys at scale.

In other words, AI personalisation may be recognised as a promising opportunity, but its operational implementation remains limited.

AI agents in customer support

AI-powered customer support is also not yet strongly represented in the data.

Only 17% of respondents say they partially use AI agents for customer support, while just 4% report using fully autonomous AI agents.

This indicates that most companies are still cautious when it comes to delegating customer interactions to AI. The limited adoption may be linked to concerns around brand tone, accuracy, trust, data protection, escalation management, and the need to maintain a human relationship with customers.

AI Strategy and Governance

Despite the increasing use of AI in Swiss marketing teams, the data shows that strategy, governance, and formal training are still developing. This creates a clear contrast between the operational adoption of AI and the level of organisational structure surrounding it.

Formal training remains limited

In response to the question “Do you provide formal and recurring training on prompt engineering?”, almost 49% of respondents say that they do not currently offer this type of training.

At the same time:

• 23% say that formal training is planned
• 28% say that formal training is already in place

This shows that while AI tools are increasingly used in daily marketing activities, structured learning is not yet systematic. Many teams may be experimenting with AI, but without a recurring training framework to improve prompt quality, output consistency, and responsible usage.

From a maturity perspective, this is an important gap. The effectiveness of AI does not depend only on access to tools, but also on the ability of teams to use them correctly, consistently, and strategically.

Internal knowledge sharing is already emerging

The picture is more positive when looking at internal exchanges around AI usage.

Around 54% of respondents say that their teams have regular or occasional rituals to exchange on AI use cases. This suggests that many organisations are already creating informal spaces to share learnings, discuss tools, and identify practical applications.

However, the fact that these exchanges are not always formalised indicates that AI knowledge sharing is still often driven by individual initiative rather than by a structured company-wide approach.

Strong perceived impact on work and creativity

Almost 90% of respondents confirm that AI has an impact on their work and creativity. They also confirm that AI allows them to focus more on strategic tasks.

This is one of the strongest signals in the study. It shows that AI is not only perceived as a productivity tool, but also as a way to shift marketing teams toward higher-value activities.

Instead of only accelerating execution, AI appears to help teams dedicate more time to analysis, planning, strategic thinking, creativity, and decision-making.

Verification processes are becoming important

Governance is also visible in the way companies manage content validation before publication.

According to the data:

• 48% of respondents already have a mandatory verification process before any AI-supported content is published
• Around 16% plan to implement this step soon

This means that nearly two-thirds of respondents either already have or plan to introduce a verification process. This is a positive sign of growing awareness around quality control, brand consistency, accuracy, and risk management.

However, it also means that a significant share of companies still do not have a clear validation process in place. This can create risks, especially when AI is used for external communication, advertising, customer-facing content, or regulated sectors.

Management supervision is still a weak point

A more critical finding appears at management level.

Around 64% of management teams are not yet trained to supervise AI workflows. This shows that AI adoption is often progressing faster at the operational level than at the leadership and governance level.

At the same time, around 35% of respondents say that management is either already trained or planning to be trained in the supervision of AI workflows.

This suggests that companies are beginning to recognise the need for managerial AI literacy. Supervising AI workflows requires more than understanding the tools: it involves defining rules, managing risks, validating outputs, ensuring compliance, and aligning AI usage with business strategy.

Operational transformation is underway

A similar pattern appears in operational transformation.

The data shows that:

• 31% of respondents have already redesigned at least one key marketing workflow by integrating AI at its core
• Another 32% say this transformation is currently in progress

This is a strong signal that AI is starting to move beyond isolated use cases. For a growing number of companies, AI is no longer only an assistant for individual tasks, but is becoming part of the way marketing workflows are designed.

Examples may include content production workflows, campaign planning, lead qualification, CRM automation, SEO optimisation, reporting, or customer journey management.

Data Hygiene, Reliability and Reporting Automation

Data quality is one of the most important foundations of AI maturity. Without clean, structured and accessible data, companies cannot fully benefit from AI for personalisation, automation, reporting or customer journey optimisation.

The results show that many Swiss marketing teams are still facing significant challenges in this area.

Data quality remains a major barrier

In response to the question about whether company data ( such as CRM, transaction and web data ) is clean, standardised and accessible for AI use, 43% of respondents say that their data is not yet clean, normalised or easily accessible.

At the same time:

• 17% say their data is clean, standardised and accessible
• 24% say their data is clean, standardised and accessible for AI use partially
• 16% – do not know

This shows that, for many organisations, AI adoption is limited not by the tools themselves, but by the quality and accessibility of the data behind them.

From a maturity perspective, this is a critical point. AI performance depends strongly on the quality of the input data. If data is fragmented, incomplete, duplicated or poorly structured, AI outputs may be less reliable and harder to use for decision-making.

Unified customer data is still limited

The study also shows that only 13% of respondents have a unified view of customer data connecting web interactions, sales and media data.

A further 29% say they have this view only partially.

This means that the majority of organisations still do not have a complete and connected view of the customer journey. In practice, this can make it difficult to understand how users interact with a brand across different touchpoints, from advertising and website visits to sales conversations and transactions.

This lack of integration can also limit the potential of AI for segmentation, lead scoring, personalisation and performance analysis.

Rules around sensitive data are not yet systematic

Another important finding concerns data governance.

Only 51% of respondents say they have clear rules defining which data must never be shared with AI tools.

This is an important signal. While more than half of respondents have already introduced some level of control, almost half still appear to lack formal rules around data sharing.

This creates potential risks, especially when teams use public or external AI tools for tasks involving customer data, internal documents, CRM exports, commercial information or confidential business data.

For companies aiming to increase AI maturity, clear data-sharing rules are an essential governance step.

AI reliability audits remain uncommon

When it comes to audit and reliability, the results show that structured control remains limited:

• 14% of respondents regularly audit AI outputs or systems to ensure reliability
• 20% do so occasionally

This means that only about one third of respondents perform reliability checks at least from time to time.

The low level of regular audits suggests that many companies are still using AI without a structured process to evaluate accuracy, consistency, bias, brand alignment or business risk.

As AI becomes more embedded in marketing workflows, reliability audits will become increasingly important, especially for customer-facing communication, regulated industries, automated recommendations and data-based decision-making.

Reporting automation is progressing, but remains fragmented

The data also shows mixed levels of maturity in reporting automation.

Regarding reporting updates:

• 5% of respondents say their reporting is updated automatically
• 24% say reporting is partially automated

However, 71% of respondents say their dashboards are not updated automatically.

This apparent contrast may reflect different interpretations of “reporting” versus “dashboards”. Some teams may automate recurring reports or data exports, while their dashboards still require manual updates. Others may have partial automation depending on the data sources involved.

Why dashboard automation can vary

There may be different reasons why dashboards are not fully automated.

The level of automation often depends on the original data sources connected to the dashboard. When dashboards rely on common digital platforms such as Meta Ads, Google Ads, GA4, LinkedIn Ads or CRM systems, updates are generally easier to automate through connectors, APIs or data pipelines.

However, when dashboards include questionnaires, offline sales data, manually completed files, Excel sheets, event data or custom internal sources, automation can become more complex. In these cases, reporting may be only partially automated, or it may still require manual validation and data preparation.

This means that the absence of full automation does not always indicate a lack of maturity. In some cases, it reflects the complexity and diversity of the data ecosystem.

Participant Profile — AI Marketing Study Switzerland

The AI Marketing Study Switzerland is based on responses from 194 participants. The respondent profile shows a strong representation of small and medium-sized companies, as well as relatively compact marketing teams.

This is important for interpreting the results: the study does not only reflect the perspective of large corporations, but also the reality of many Swiss companies where marketing resources are limited and teams often need to manage several responsibilities at the same time.

Company size of participating organisations

The distribution by company size is as follows:

Analysis

The study is mainly composed of SMEs and mid-sized organisations.

Companies with fewer than 250 employees represent:

22% + 28% + 27% = 77% of respondents

This means that more than three quarters of participating organisations are small or medium-sized companies.

Larger organisations are also represented, but to a smaller extent:

• Companies with 250–999 employees represent 15%
• Companies with 1,000+ employees represent 8% in total

This distribution is particularly relevant in the Swiss market, where SMEs play a central role in the economy. It also helps explain some of the findings observed in the study: AI adoption is often pragmatic, tool-based, and operational, rather than fully governed through large-scale transformation programmes.

Size of marketing teams

The distribution by marketing team size is as follows:

Analysis

The results show that most participating organisations have relatively small marketing teams.

Teams of 1 to 5 people represent:

41% + 32% = 73% of respondents

This means that almost three quarters of respondents work in compact marketing teams.

Only 27% of respondents report having teams of 6 or more people.

This is an important insight for understanding AI maturity. In small marketing teams, AI can play a particularly important role because it helps compensate for limited resources. It can support content creation, campaign planning, SEO, reporting, automation, customer communication, and data analysis.

However, smaller teams may also face more difficulties in implementing structured governance, formal training, advanced data integration, or systematic AI performance measurement. This may explain why the study shows strong daily AI usage, but weaker maturity in areas such as data hygiene, workflow supervision, AI audits, and formal training.

Conclusion

Summary conclusion

The AI Marketing Study Switzerland shows that AI adoption is already well underway in Swiss marketing teams. AI is no longer only an experimental topic: it is increasingly used in daily work, especially for content creation, productivity support, SEO assistance and operational efficiency.

However, the study also reveals an important maturity gap. Many organisations use AI regularly, but they have not yet fully structured the foundations needed to scale it in a reliable and strategic way. Data hygiene, governance, training, workflow redesign, AI performance measurement and management supervision remain underdeveloped in many companies.

This means that the next stage of AI maturity in Switzerland will not depend only on using more AI tools. It will depend on how well organisations can integrate AI into their operating model, connect it to clean and reliable data, define clear usage rules, measure its impact, and ensure that human expertise remains central to quality, creativity and decision-making.

In short, Swiss marketing teams are moving from AI experimentation toward AI operationalisation.

The challenge now is to transform everyday AI usage into a structured, measurable and scalable approach.

Suggested next steps for AI maturity Move from tool usage to a structured AI roadmap

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