10 facts on the use of AI in Swiss companies (based on Study 2025)

Artificial intelligence (AI) has experienced a real hype thanks to technologies such as ChatGPT and is now at the center of many discussions. But how far have Swiss companies actually progressed in their use of AI? A recent study - the Swiss AI Report 2025 by CorpIn - provides empirical answers. It is one of the most comprehensive AI studies in Switzerland, with 1,338 managers from a wide range of industries surveyed. The digital AI maturity level was examined using a hexagon model consisting of six dimensions covering topics from data quality to culture. Below, we highlight 10 key facts from this study that show where Swiss companies stand when it comes to artificial intelligence - from progress and trends to hurdles and areas for action.

1. almost half of companies are already using AI

AI adoption in Switzerland is clearly picking up speed. According to the Swiss AI Report 2025, 48% of Swiss companies are already using AI in initial processes, and the trend is rising. This corresponds to an increase of around 10% compared to 2023 - an indication that the initial AI trend is now gaining traction across the economy. Conversely, however, this means that a good half of companies are not yet actively using AI in their day-to-day operations. Many companies are still in the experimental or planning phase. However, the trend clearly shows that artificial intelligence in Swiss companies has gone from hype to practical application. Companies that get on board now benefit from the experience gained by the pioneers - and avoid missing the boat in the long term.

2 AI has strategic priority - concrete goals are often missing

At management level, AI has long been considered a strategic topic for the future. 65% of companies have anchored AI in their long-term corporate strategy. However, there is a gap between aspiration and reality: only 13% work with clearly defined, measurable goals or KPIs for AI projects. In many cases, AI therefore remains a vague buzzword without concrete implementation steps. This lack of strategic anchoring makes the success of AI initiatives more difficult - if goals, responsibilities and metrics are unclear, it becomes difficult to evaluate progress and deploy resources in a targeted manner. The study shows that companies with a well-thought-out AI strategy (including KPIs) are significantly more successful in its implementation. Implication: Decision-makers should not just adopt AI as a trend, but should formulate clear goals and continuously measure progress in order to generate real added value.

3. insufficient data quality slows down AI projects

Data is the foundation of every AI application - and this is where a weakness of many Swiss companies is revealed. According to the study, only 8% of companies have fully consistent data structures, i.e. cleansed, integrated data of high quality. In contrast, over a third struggle with isolated data silos and fragmented data stocks. These gaps in data management make it difficult to conduct reliable analyses with AI and train models. Put simply: if the data basis is not right, even the smartest AI cannot deliver any added value. Many projects fail because data is missing, unstructured or incorrect. Implication: companies should invest in their data infrastructure - from cleansing and integration to data governance. Robust data quality is a prerequisite for AI to develop its full potential.

4 Technical integration as the greatest challenge

It is not the AI technology itself, but its integration into existing systems that is currently the biggest technical obstacle. 40% of companies lack end-to-end IT integration, i.e. specialized systems such as business intelligence (BI) or supply chain management (SCM) are not yet connected to AI solutions. It is therefore not surprising that 64% of companies cite the lack of integration of AI into existing systems as a key challenge. Disruptions in the system landscape prevent AI data and findings from flowing seamlessly into business processes. For example, AI analyses remain ineffective if they are not fed back into the ERP/CRM, or chatbot solutions that are not linked to the backend do not exploit their potential. Implication: A modern, flexible IT architecture is a basic prerequisite for AI success. Companies should check where interfaces are missing, modernize legacy systems and focus on integration - be it via APIs, middleware or new platforms - so that AI solutions can work company-wide.

5 Data protection and security remain critical success factors

The study shows that data protection and security concerns are preventing many companies from using AI more aggressively. 50% of companies have concerns about data protection and IT security in connection with AI. Regulated data in particular (customer, employee or health data) makes the introduction of AI complex. In addition, only around 20% of companies meet established security standards such as ISO 27001, which indicates a need to catch up in terms of compliance and IT security. Without clear guidelines, companies risk violating data protection laws or falling victim to cyberattacks through AI applications (such as cloud-based AI services). Implication: Security & compliance must be an integral part of any AI strategy. Companies in Switzerland - with strict data protection laws (GDPR/DSG) - should adapt their data policy at an early stage, create security concepts and sensitize employees to the correct use of AI tools. In this way, innovation and data security can be reconciled.

6. corporate culture: openness increases, but change management remains necessary

The acceptance of AI in the workforce is an often underestimated factor. Fortunately, only 3% of companies report that their employees are highly skeptical of AI initiatives. The majority of employees are at least open to new technologies or have a wait-and-see attitude. Nevertheless, internal reservations should not be ignored: 41% of companies cite resistance or reluctance within the team as a challenge in AI projects. This shows that although there is rarely open rejection, there are certainly fears, uncertainties and skills gaps that need to be addressed. For example, employees wonder whether AI will change their job or how they will cope with new AI-supported processes. Implication: Change management and cultural support are key factors for AI success. Transparent communication ("How will AI benefit our company and each individual?"), team involvement in pilot projects and training help to build trust. An open culture of innovation, in which questions and concerns are taken seriously, promotes a willingness to see AI as an opportunity - and not as a threat.

7. lack of AI skills: Further training and specialists wanted

Know-how is just as crucial for AI projects as data and tools. The study shows clear gaps here: 51% of companies do not offer regular AI training for their employees, and only 9% rely on mandatory, comprehensive AI training. In other words, knowledge is often only acquired on an ad-hoc basis or on the employee's own initiative. At the same time, there is often a lack of specialized experts - 39% of companies do not have an AI specialist on their team. This skills gap significantly slows down the implementation of AI: without internal expertise, potential cannot be recognized, projects cannot be managed properly and external service providers cannot be managed effectively. Some companies try to fill the gap with external partners or AI-as-a-service offerings, but this does not completely replace internal expertise. Implication: Companies should invest specifically in training and further education in the field of AI and promote talent. The recruitment of AI specialists or the further training of existing employees (e.g. data scientists, machine learning engineers) is also key to increasing digital maturity. Such a skills base pays off in the long term and makes you less dependent on external support.

8. top application areas: Process optimization and marketing at the forefront

Where are the pioneers already using AI? According to the study, process optimization and marketing are the leading fields of application. In practice, this means, for example, automated data analysis to increase efficiency in internal processes, intelligent chatbots in customer service or personalized campaigns and product recommendations in marketing. Swiss companies are using AI to automate routine tasks, answer customer inquiries around the clock and make better decisions through data analysis. These use cases promise an immediate return on investment, which explains their early success. Areas such as HR (e.g. CV screening), production (predictive maintenance) and finance (risk modeling) are also becoming increasingly important. Generative AI in particular - i.e. AI that can generate text, images or code - is increasingly being used experimentally in content creation, software development or product design. Implication: Companies should examine in which business areas AI solutions can deliver rapid added value. Experience shows that starting in a limited field of application (pilot projects) - for example in marketing or clearly definable processes - is a good way to get started. From there, AI can be gradually rolled out in other areas of the company.

9. procedure: Ready-made AI solutions dominate over in-house developments

One interesting finding of the study is the approach to AI projects. Most Swiss companies rely on proven AI tools and platforms from third-party providers instead of developing their own AI systems from scratch. Ready-made solutions dominate the use of AI, while in-house developments are still rare. There are several reasons for this approach: Ready-made (often cloud-based) AI services - from AI platforms from large tech providers to specialized industry solutions - allow a quick start without high initial costs. The out-of-the-box solution seems particularly attractive in light of limited internal AI resources. For example, companies can license existing machine learning models for image recognition, voice assistants or predictive analytics and use them immediately instead of spending months training their own model. However, this also creates a certain dependency on external providers and their development cycles. Implication: For many companies, the use of AI platforms (AI-as-a-Service) is the pragmatic way to benefit quickly from AI. In the medium to long term, however, companies should evaluate where their own AI expertise and individual solutions are worthwhile - for example, to differentiate themselves from competitors or to better cover specific processes. A healthy mix of purchased technology and in-house expertise maximizes flexibility and innovative strength.

10. investments in AI on the rise - focus shifts to digital maturity

The willingness to invest in AI has never been so high. More than half of Swiss companies are planning specific budgets for AI initiatives in the coming years. This means that AI is no longer just a topic of conversation, but is now a financial priority in corporate planning. This is driven by the visible success of initial pilot projects as well as competitive pressure: those who do not invest in AI tools, infrastructure and skills now could fall behind in terms of productivity and innovation. In addition to money, progressive companies are also investing time to better understand their own level of digital maturity. Maturity analyses are increasingly being carried out to identify strengths and weaknesses in the above-mentioned dimensions (data, technology, strategy, etc.). Such benchmarks - using CorpIn 's Hexagon model, for example - show companies where they stand in comparison to the market and where investments pay off the most. Implication: If you know the digital maturity of your company, you can make targeted AI investments where the greatest leverage exists. Rising budgets are a good sign - now it's time to use them strategically and wisely to turn AI trends into sustainable competitive advantages in 2025.

Conclusion: AI readiness is the key to success - take action now

The Swiss AI Report 2025 makes it clear that Swiss companies are making considerable progress in the use of AI - but still have some homework to do. Artificial intelligence in Switzerland is evolving from buzzword to concrete practice: around half of companies are already using AI, especially in areas with rapid benefits. At the same time, gaps in data quality, system integration, skills and strategy show that there is often still room for improvement in terms of digital maturity. For decision-makers - whether CEOs, innovation leaders or IT managers - this means: honestly assess your own AI maturity level now and plan the next steps.

Interested in conducting your own maturity analysis? CorpIn's Hexagon platform offers an innovative tool for measuring the digital maturity of your company in six dimensions and developing it further in a targeted manner. Based on this empirically founded model, you will receive concrete recommendations on how you can improve your AI readiness - from data strategy to change management. As one of the leading providers of AI strategies and digital solutions in Switzerland, CorpIn supports companies in exploiting their full AI potential and securing sustainable competitive advantages. Those who act now can proactively exploit the opportunities offered by artificial intelligence - and set the course for future AI excellence in their own company.

Download the full Swiss AI Report 2025 for free to get even more insights and best practices. Then start your own AI maturity analysis - and take the next step on the road to digital leadership. The future belongs to the companies that ask the right questions today and invest boldly in artificial intelligence. Use this opportunity to make your company fit for the future of AI.