The Lake Constance region is one of Europe's industrially strongest regions. Mechanical and plant engineering, the energy industry, medical technology and a dense network of specialised suppliers shape its economic structure. Many companies are technologically leading, internationally active and deeply embedded in complex value chains. The region's strength lies in specialised competence clusters that typically generate large volumes of data. Yet precisely in the operation of complex plants or in quality control, practice shows that the potential of data-driven innovation has so far only been partly realised.
Artificial intelligence is often named as a key technology, but its productive use depends less on algorithms than on the availability, quality and interconnection of data. This is exactly where a large and so far barely used opportunity lies for the region.
The consultancy McKinsey estimates that connected data and AI can enable productivity gains of 15 to 20 per cent in manufacturing and operational processes. Those are substantial revenue opportunities and enormous potential for cost reduction — but for that, the data has to leave the silo.
Machines, plants and medical devices continuously generate operational and condition data. This telemetry data is valuable because it allows conclusions about efficiency, quality, failure risks and optimisation potential. In many companies, however, it is used primarily for internal operations and remains in isolated systems.
AI without a data foundation is theory
International studies show that data-driven cooperation along value chains can shorten innovation cycles and increase productivity. The effect does not come from more data alone, but from combining it. When data from development, operations, maintenance and use is brought together, new insights emerge that no single company could obtain in isolation.
For a region with many hidden champions, this means that the greatest leverage arises where data is not only optimised internally but made usable across company boundaries. The EU Data Act has recently provided a clear legal framework for this.
Connected possibilities: where is the potential for the region?
The industrial structure of the Lake Constance region is characterised by strong specialisation and close technological interdependencies. Machine builders work with energy suppliers, suppliers with medical technology manufacturers, software providers with production companies. These existing relationships form an ideal basis for data-based cooperation.
Connected data platforms could, for example, combine operational data from machines with energy data, environmental data or quality data. Such links enable not only efficiency gains but also new business models, such as service-based offerings, performance-based maintenance contracts or joint optimisation services for end customers.
Regional networking is not an end in itself. It reduces complexity, creates trust through physical proximity and makes it easier to establish common standards. Mid-sized companies in particular benefit, because they can achieve economies of scale without giving up their independence.
The challenges: technology, data and sector logic
Despite the potential, the path to connected AI use is demanding. The first hurdle is technical. Much of the data exists in heterogeneous formats, comes from different generations of machines or IT systems and is not readily compatible. Without clean data architectures, reliable timestamps and clearly defined interfaces, every AI application remains error-prone.
Then there is the question of which sectors can genuinely cooperate in a meaningful way. Not every data source can be used without context. Successful networking requires an understanding of which data complements which, and which processes are comparable. Mechanical engineering and the energy industry, for instance, share similar requirements for availability, security and maintenance. Medical technology and the pharmaceutical industry, in turn, combine high regulatory requirements with data-intensive quality control.
Beyond technology, trust and governance play a central role. Companies must be able to rely on sensitive information staying protected and on the economic benefit being shared fairly. Without clear rules on data use and commercialisation, cooperation quickly becomes a theoretical idea.
Predictive maintenance: the prime example
A mid-sized plant engineering company operates several hundred machines at customer sites worldwide. The plants deliver sensor data on temperature, pressure and vibration. The problem is that different formats and transmission paths are used for this data depending on the machine generation.
For predictive maintenance it is not enough to analyse individual measurement series. Only by harmonising the data, linking it with maintenance and operational information and assigning it cleanly in time does a robust basis emerge for forecasting failure probabilities and remaining useful life. According to Deloitte, data-driven maintenance strategies can reduce unplanned downtime by up to 25 per cent.
The same applies to supplier networks: shared platforms that bring together production, warehouse and transport data enable better planning and faster responses to disruptions — without individual companies having to give up their independence.
A shared competitive advantage
Data engineering is not an optional infrastructure measure but a precondition. Only once these foundations are in place can applications such as predictive maintenance or digital twins be operated sensibly.
Data engineering is the systematic construction of data pipelines and platforms that ingest, clean and structure raw data from different sources and make it usable for analysis. Digital twins, for example, which represent the behaviour of machines or plants virtually, need not only current sensor data but also historical operating states and contextual information. Without a clean data architecture, their value remains limited.
The Lake Constance region has everything it takes to use artificial intelligence successfully: industrial substance, technical know-how and a high density of specialised companies. The decisive step is to break open data silos and to understand cooperation as a factor of innovation. AI does not take effect in isolated use, but where data flows and is used jointly. For the region, that could be exactly where a lasting competitive advantage lies.
The technological substance is there, and so is the process knowledge and the proximity to real industrial applications. AI can make a genuine contribution here if it is understood not as an isolated innovation but as the further development of existing systems. The key is to look for the entry point not in the hype but in the fundamentals. Anyone prepared to invest in data quality, architecture and accountability can use AI as an economically effective tool.
About control-f. control-f GmbH is a values-driven AI company based in Konstanz. Since 2022, the data boutique has been building big data platforms for industrial telemetry data and helping companies in German-speaking Europe make complex data landscapes usable. Its clients include large corporations and mid-sized companies from plant engineering, automotive and the energy industry. Managing directors Simon Deussen (machine learning engineer and founder) and Daniel Tremer (formerly Specialist Data Science & AI Projects at Porsche AG) focus on building stable data architectures as the foundation for analytics, software solutions and AI applications such as predictive maintenance.
Kategorie / Category: Blogposts