AI Strategies for Regulated Industries: Insights From the ASC Switzerland Roundtable

AI is no longer a distant dream in Switzerland, but a necessity. Just a few years ago, the market was considered conservative and hesitant, but since the coronavirus pandemic, the tide has turned. The financial sector in particular has caught up in terms of cloud computing and is now on a par with international pioneers. This has laid the foundation for the meaningful use of AI. 

’Either I address the issue and take the lead in innovation, or I fall behind,’ said Gerald Kromer, summarising the urgency of the situation at the roundtable.

Especially in industries with high regulatory pressure, international competition and narrow profit margins, AI is no longer an option, but a minimum requirement for remaining competitive. 

The decisive factor here is not whether AI is used, but how quickly and how specifically this is done. Companies that start early gain valuable experience, continuously optimize their processes and thus secure a competitive edge – technologically, organizationally and culturally. 

Strict regulations can initially slow down the use of AI. In regulated industries such as finance, healthcare and insurance, requirements such as DORA, GDPR, and national data sovereignty are complex and resource-intensive. Projects progress slowly because approvals, regulatory reviews and security requirements must be taken into account comprehensively.  

However, it became clear during the roundtable discussion that regulation is not just an obstacle. Gerald Kromer emphasizes that clear framework conditions force companies to optimize their data quality, processes and security architecture in a targeted manner and align them for the future. The advantage: those who put their data and processes on a solid footing in the course of regulation create exactly the right conditions for using AI reliably and scalably. 

An example: Microsoft has invested 400 million in Swiss data centers in Zurich and Geneva. This ensures that sensitive data remains in the country and builds trust for AI projects in critical industries. As a Microsoft partner, ASC uses this foundation to develop customer solutions on a secure and future-proof basis. 

Starting small often helps you reach your goal faster. Successful AI implementations rarely begin with large-scale projects. Instead, companies that enjoy long-term success focus on manageable use cases that are tested, optimized and only then scaled up. 

‘It's not a one-time job. You have to continuously improve and optimize again and again,’ emphasized  Thomas Peyer.

Such pilot projects help to limit risks, promote internal acceptance and gain experience that paves the way for larger projects. 

Gerald Kromer made it clear that projects without clear business benefits often quickly lose support. It is therefore crucial to define measurable goals from the outset, whether these are increased efficiency, improved compliance or new service offerings. 

Bettina Huwiler also advocated pragmatism: standard technologies can form a solid basis, while individual adaptations are particularly useful where they create a real competitive advantage. 

The conclusion: start small, achieve measurable results, optimize in a targeted manner – and then grow step by step. Those who follow this path lay the foundation for a long-term successful AI strategy. 

AI is not a substitute for humans but rather supports them. It analyses large amounts of data, recognizes patterns and takes on repetitive tasks. Humans check the results, interpret them, and continue to bear responsibility. 

This collaboration is particularly crucial in regulated industries: AI can, for example, evaluate customer communications in their entirety or identify regulatory-relevant processes faster than would be possible manually. Bettina Huwiler emphasized in the roundtable discussion that it is about ‘finding ways in which technology and humans can work together and generate better results’. 

Whether as an assistance system in the background or as a specialized solution for a specific business process, the greatest benefit is achieved when technology and human judgment are consciously combined. 

Technology can only be effective if structures and culture keep pace. The introduction of AI changes processes, responsibilities and decision-making paths, and sometimes even requires new roles such as a Chief AI Officer, who provides strategic guidance and mediates between management, specialist departments and IT. 

Equally important is a culture that promotes openness to new ideas. AI projects inevitably involve a learning curve, and not every attempt leads to immediate success. Companies that embrace a culture of continuous improvement benefit twice as much: they learn faster and adapt more flexibly to new requirements. 

As the roundtable discussion made clear, people often need more time to change than technology does. Change management, training and cross-departmental collaboration are therefore not secondary issues, but crucial factors for long-term success. 

The ASC Roundtable shows that AI has long been a strategic success factor, especially for Swiss companies in regulated industries.  The five lessons learned show what matters now: from a clear view of regulation as an opportunity, to the gradual development of resilient use cases, to the right balance between technology, structures and culture. 

For managers and executives, this means that now is the right time to not only discuss AI, but to put it into practice.