Why Swiss companies still have some catching up to do when it comes to AI

Many Swiss companies recognize the potential of artificial intelligence (AI), but there are still considerable gaps in practice, especially in SMEs. A new empirical study - the Swiss AI Report 2025 by CorpIn - surveyed over 1,300 Swiss decision-makers and revealed significant deficits. The study is based on the six-dimensional hexagon model and is considered one of the most comprehensive AI studies in Switzerland. It focuses on six fields of action that determine the organizational AI maturity level: Data foundation, Strategic anchoring, Technical requirements, Security & privacy, Cultural dimension and Awareness & competence. All six areas show why Swiss companies still have some catching up to do when it comes to AI - from insufficient data quality and a lack of strategic anchoring to gaps in training. Below, we examine each dimension, present key findings from the study and highlight industry-specific challenges and structural deficits. The aim is to paint an objective picture of the situation - and to show ways in which companies can increase their AI readiness.

Data foundation: Lack of a consistent database slows down AI

A robust database is the foundation of any successful AI initiative. However, this is precisely where many Swiss companies are lacking. According to the Swiss AI Report 2025, only 8% of companies have a fully consistent data structure - meaning the foundation for a successful AI strategy is often missing. A lack of data quality and scattered data islands make it difficult to reliably train and use AI models. For example, 35% of companies cite insufficient data quality or availability as a key obstacle to AI projects. Data is often trapped in silos or not sufficiently integrated, which leads to inconsistencies and high manual effort. In Swiss SMEs, older IT systems and established structures can also make data integration more difficult. Different sectors are affected differently: In the finance and insurance sector, for example, large amounts of data are available, but strict data protection regulations limit their use; in industrial companies, on the other hand, large amounts of production and sensor data are generated, but a lack of standards in processing can slow down their value creation. The study clearly shows that data foundation and AI success are directly linked - without clean, available data, the use of AI remains piecemeal. Companies should therefore first invest in data management and integration before striving for AI excellence.

(Internal tip: A structured inventory of your own data landscape - for example as part of an AI maturity check - can help to uncover and prioritize weak points in the data foundation).

Strategic anchoring: AI without clear goals and responsibilities

AI projects only have an impact if they are strategically anchored - with clear goals, key figures and responsibilities. However, the Swiss corporate sector shows clear deficits here. Although 65% of the companies surveyed stated that they see AI as part of their long-term strategy, only 13% work with clearly defined, measurable AI goals. In other words, many companies talk about AI without having a concrete roadmap. There is often a lack of a formal AI strategy that defines where and how AI should be used. It is therefore not surprising that more than half (51%) do not currently measure the success of their AI initiatives at all - there is a lack of suitable KPIs (key performance indicators) and monitoring processes.

This strategic vacuum often leads to unclear responsibilities: Who is actually responsible for AI in the company? In many SMEs, there is no person responsible for AI or no dedicated team to drive AI development forward. Without clear ownership, projects can easily come to nothing or get stuck at departmental level. The Swiss AI Report 2025 study underlines that there is a lack of top management support and cross-departmental coordination - many AI initiatives fail because there is a lack of commitment from the top and a lack of anchoring in the corporate strategy. Industries with a high level of regulation (e.g. pharmaceuticals, healthcare) find it particularly difficult, as additional strategic coordination is often required to bring AI projects into line with compliance requirements.

As a result, AI remains an experimental side issue in many places instead of being strategically managed as a matter for the boss. Companies should therefore check whether their AI ambitions are firmly anchored in their business strategy - including clear objectives, resource planning and defined responsibilities. A first step can be to strategically classify the current level of AI maturity. The Hexagon model developed by CorpIn offers a structured approach for anchoring AI goals in the corporate strategy and identifying gaps between target and actual.

Technical requirements: Integration gaps in the IT infrastructure

Without the right technical prerequisites, AI projects fall flat - even the best data and strategy are of little help if there is a lack of modern IT and integration. The reality in Swiss companies: 40% of companies lack end-to-end IT system integration, and specialized tools such as business intelligence or supply chain systems are often not connected to AI applications. This silo mentality is a massive hindrance to scalable AI solutions. In fact, 64% of companies in the study stated that the lack of integration of AI into existing systems is one of the biggest challenges.

Medium-sized companies in particular often come up against established IT landscapes with outdated legacy systems that can only be adapted to a limited extent. Such incompatibilities make it difficult to embed AI models seamlessly into business processes. For example, manufacturing companies may have problems linking AI algorithms for predictive maintenance with their existing ERP systems if interfaces are missing. SMEs in the service sector, on the other hand, often lack cloud infrastructure or automation of data flows between tools, which keeps AI pilots laboriously manual.

The study emphasizes that a flexible, cloud-capable IT infrastructure is a basic prerequisite for AI success. Many companies are not yet focusing on standardization and scalability here: cloud solutions and modern data platforms are sometimes introduced hesitantly. The result is media disruptions and isolated data pools that hinder the holistic use of AI. Anyone who wants to use AI support on a large scale must therefore clean up their IT architecture: create interfaces, replace or integrate legacy systems and invest in high-performance infrastructure. On a positive note, the Swiss AI Report 2025 notes that 48% of companies are now using AI in at least individual processes (an increase of ~10% compared to 2023). This increase shows: The technical basis is gradually improving, but in order to roll out AI across the board, integration gaps need to be closed.

Security & privacy: concerns about data protection and compliance

In a country like Switzerland, which has high data protection standards and strict regulations, security & privacy is a crucial dimension for AI projects. Many companies have concerns here, which also leads to delays. According to the study, data protection and security concerns are among the biggest AI hurdles for 50% of companies. Concerns about compliance with laws (such as GDPR/DSG) and industry-specific requirements (e.g. in the financial or healthcare sector) are omnipresent. Some companies are reluctant to use sensitive data for AI models for fear of data leaks or legal consequences for automated decisions.

Interestingly, many Swiss companies already meet basic standards: a majority of respondents state that they at least comply with the General Data Protection Regulation (GDPR) and the Swiss Data Protection Act (DSG), in some cases even with ISO/IEC 27001 certifications. However, compliance on paper does not automatically mean that there is trust in AI. Cloud-based AI solutions in particular raise questions about where data is stored and processed. Companies also fear reputational damage if AI systems make the wrong decisions - e.g. when granting loans or recruiting staff - and thus violate ethical or legal principles.

The study makes it clear that security and privacy aspects must be integrated into AI projects at an early stage in order to create acceptance. Companies should develop internal guidelines for the responsible use of AI, including clear rules for data protection, model monitoring and contingency plans in the event of incidents. Industries with sensitive data - such as medicine or government - often require additional rounds of coordination, which can slow down AI initiatives. Nevertheless, security should not be used as an excuse to block innovation. With the right protective measures (encryption, access controls, anonymization), the majority of risks can be managed. According to the report, Swiss companies must learn to balance security and innovation in order not to fall behind in the AI race.

Cultural dimension: acceptance, change management and fear of the new

Technology alone is not enough - the culture within the company plays a key role in determining whether AI initiatives are successful. Although there is a general openness towards AI in many Swiss companies, there is often a lack of change management and communication to dispel fears and prejudices. According to the Swiss AI Report 2025, most employees are positive or at least open to AI, but a lack of communication and feedback structures can increase uncertainty. In other words, the workforce is quite willing to use AI, but does not feel involved enough. For example, 41% of companies report resistance or skepticism among employees as a challenge in AI projects. Especially when new AI systems are introduced, fears can easily arise - for example, the concern of losing importance through automation or having to pay for AI errors.

Many Swiss companies are already responding to isolated reservations, but only a few are practicing consistent change management. Only 9% of companies offer mandatory, regular AI training for their employees - skills development remains patchy, which tends to increase uncertainty. There is often a lack of a clear vision that communicates to employees how AI supports their work rather than replacing it. Best practices, such as small-scale pilot projects with a subsequent feedback round or success stories from internal AI users, are still too rarely used to generate enthusiasm.

From an industry-specific perspective, it is clear that the cultural hurdle can be higher in traditional industries (e.g. mechanical engineering or public administration) than in young tech companies, where openness to new tools is practically part of everyday life. But whether in industry or the service sector, without the active involvement of employees, AI projects risk getting stuck halfway. The report recommends appointing change agents and incorporating regular feedback loops to address concerns at an early stage. Ultimately, it is the corporate culture that determines whether new technologies are accepted in the long term and can exploit their potential.

Awareness & competence: gaps in AI know-how and further training

Last but not least: awareness & competence - i.e. knowledge about AI and the skills to deal with it. Here, the study reveals glaring skills gaps in Swiss companies. Only one in eleven companies (9%) provide their employees with regular and mandatory training on AI topics. This means that the overwhelming majority either rely on informal learning or do not offer any specific AI training at all. Given the rapid development of AI technologies (think of the boost from Generative AI such as ChatGPT), companies run the risk of their staff not being familiar with the latest possibilities.

It is therefore not surprising that 39% of companies cite a lack of internal AI expertise and specialists as a key problem. SMEs in particular are struggling to recruit AI talent because they are in competition with large corporations and tech companies. The few specialists available are often overworked and act as lone wolves without broad team support. In some sectors - such as the skilled trades or smaller service companies - there are still hardly any points of contact with AI, meaning that expertise is practically starting from scratch.

The Swiss AI Report 2025 study recommends that companies promote a culture of awareness: Managers should lead by example and build AI skills, e.g. through targeted training programs or exchanges with external experts. In addition, an "AI pilot" could be appointed in the company - a person or unit that acts as a central point of contact and distributes know-how. It is important that employees at all levels understand what AI can (and cannot) be used for. A lack of knowledge in turn fuels uncertainty and rejection. This is where it pays off to invest in training, workshops and pilot projects to make the workforce fit for the future of AI. The area of awareness & competence is closely interlinked with the cultural dimension: only if companies actively build skills can genuine AI adoption succeed.

Conclusion: AI readiness as a location factor - and how you can test your maturity level

The results of the Swiss AI Report 2025 paint a clear picture: Swiss companies - especially SMEs - still have some catching up to do when it comes to AI integration, across all six dimensions of the Hexagon model. From data inconsistencies, a lack of strategic target anchoring and integration problems to skills deficits and cultural barriers, there are a wide range of construction sites. At the same time, initial progress - such as increasing usage rates of AI in processes - shows that change is underway. Companies that honestly assess their AI maturity now and address gaps can secure competitive advantages. After all, AI is rapidly becoming a location factor: those that are well positioned can adopt innovations faster and optimize productivity, customer experience and business models.

The key lies in a holistic approach. CorpIn's Hexagon model offers a practicable framework for this, as it covers all relevant fields of action and enables companies to determine where they stand. Companies can use the Hexagon platform to measure their AI maturity level based on data - and then receive industry-specific benchmarks and specific recommendations for action. This evidence-based self-analysis, underpinned by the findings of the study, is a unique way of moving from gut feeling to solid decisions.

Take advantage of this opportunity to check the status quo of your AI readiness. Test your company's AI maturity level on the Hexagon platform now and find out where you stand - the first step towards introducing targeted improvement measures and not missing the boat when it comes to AI excellence. The Swiss AI Report 2025 serves as a valuable benchmark and shows that catching up is necessary, but worthwhile: with a systematic data basis, a clear strategy, solid technology and trained and involved employees, the Swiss economy can successfully shape the AI age.

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