A smart factory uses interconnected systems and machines to generate data, often in real time, to improve production processes.
The primary objective is to help machine operators, line supervisors, engineers, and managers make better decisions to improve responsiveness and support industrial performance. Born from the concept of Industry 4.0, this networking of machines equipped with sensors and an internet connection is certainly no longer new, but it has taken a significant step forward in the last two or three years with the integration of artificial intelligence and machine learning for data analysis. Between 2024 and 2025, the proportion of SMEs that have integrated this tool rose from 22% to 34%, according to a study on the job market. Nearly half of Swiss companies now see AI as an asset to their business, an increase of 10% over one year. The proportion of companies with a negative view has fallen by 7%. The main reason for this approval is increased efficiency in many departments within companies.
Fears of a negative impact on employment also seem to be receding. The productivity gains achieved following the introduction of AI have led only 2% of the companies surveyed to reduce their workforce. Some 10% of them claim, on the contrary, that AI has created new jobs. This technological shift therefore has less of an impact on the number of jobs than on the skills that employers are now looking for. It is important to be aware that this evolution is transforming traditional professions, giving rise to new professional profiles: we are talking here about AI specialists for industrial automation, data analysts for predictive maintenance, and coordinators between humans and production machines.
Training strongly influenced
The emergence of these new technologies and changing economic requirements are having a direct impact on the development of vocational training in Switzerland. In 2025, the State Secretariat for Education, Research and Innovation (SERI) approved or established regulations for 43 occupations that have been revised or newly created. Twenty-two requirements concern initial vocational training and twenty-one concern higher vocational training. As a sign that this development affects companies at all levels, these requirements include traditional training courses such as polymechanic, automation engineer, mechanical technician and automated machine operator. These "traditional" professions are set to evolve, particularly mechanics, who must now maintain AI-controlled machines, engineers, who must understand algorithms, and managers, who must make decisions based on large amounts of data provided by interconnected production tools. An additional challenge is that these technologies are evolving rapidly, so what we learn today may not be entirely valid tomorrow. Flexibility, adaptability and continuous learning will gradually become the norm and will be decisive factors.
What is the situation on the ground?
The recruitment firm Robert Walters recently conducted a survey and sought to identify the skills that will be most sought after by business leaders over the next two years. The conclusion is that technical skills remain essential, but that understanding data and human interaction will become increasingly intertwined. In other words, employers will favour professionals who are able to understand data, but above all to transform it into strategic decisions and communicate it clearly in increasingly complex organisations. With AI no longer considered a futuristic concept but rather an everyday reality, four out of ten executives expect their employees to have a certain level of proficiency in this field as well as in machine learning. AI literacy has become a basic skill, so to speak. Employees are not expected to be data scientists, but they do need to understand how AI works, its possibilities and its limitations. Understanding how algorithms work, the assumptions they make in order to make decisions, and the risks involved allows AI to be used strategically.
From the employees’ point of view, the picture is slightly different and currently shows a gap between ambitions and reality. While 80% of them say they are comfortable with AI and mentally ready for change, they nevertheless find that transforming interest into structured use is less easy than expected. The main issue is the lack of training opportunities. One in two employees believes that their company does not do enough, or even anything at all, in this area.
The challenges
Like any (r)evolution, the one brought about by AI raises many questions and is still often a source of uncertainty. Companies that intend to make this technology a driver of sustainable growth will have to take a multi-pronged approach in order to allay fears as much as possible. This will involve offering targeted training, making structural investments in talent, establishing multidisciplinary collaboration and, crucially, providing leadership capable of bridging the gap between human intelligence and technology. Employees must be encouraged to view AI as a partner rather than a competitor or, worse still, a replacement. To achieve this, companies will need to communicate transparently about how they intend to bring about this change, the ultimate goals and the pace at which it will happen.
