From raw materials company to AI platform: The transformation of the Dorfner Group
From raw materials company to AI-driven innovation engine: The transformation of the Dorfner Group
The use of artificial intelligence is often associated with large tech companies or digital platforms. However, the story of the Dorfner Group impressively demonstrates that even a more than 100-year-old industrial company from the Upper Palatinate can realign its business model with AI. In the latest episode of the podcast "Hope is not a strategy," Christian Underwood and Prof. Dr. Jürgen Weigand talk to CEO Mirko Mondan about how a traditional raw materials company is becoming a data-driven engine of innovation.
When data becomes a strategic treasure
Many companies have data—but only a few really use it strategically. At Dorfner, this is precisely where the key to transformation lies. Over decades, raw materials, minerals, and chemical properties have been documented in great detail in the laboratory. In some cases, a single raw material has been described with up to 50 parameters.
What was once merely precise documentation has developed into a real competitive advantage thanks to increasing computing power and new AI methods. Today, historical laboratory records form the basis for a proprietary AI system that can identify complex relationships between materials, properties, and applications. This has transformed a seemingly traditional data set into a strategic treasure trove of data.
Strategy before technology
A key point that Mirko Mondan emphasizes in the interview is that the use of AI did not begin with technology—it began with strategy. The initial question was how the company should develop in the long term. As a raw materials company, Dorfner faces a natural limitation: raw materials are finite. A linear business model based exclusively on increased extraction would have jeopardized the company's own foundation in the long term.
The strategic challenge was therefore: How can the company grow without simultaneously depleting its resources more quickly? The answer lay in its own core competence—raw material analysis and material formulation. This is precisely where the AI initiative came in.
From experiment to platform
With the help of machine learning, the company began to rethink its laboratory processes. Previously, numerous experiments had to be carried out to find out how different combinations of raw materials behaved in specific applications.
Today, most of these experiments can be simulated digitally. The effect is enormous: test series that used to take several weeks or even months can now be carried out in some cases within a single day. This new speed is not only changing internal processes, but also the value proposition to customers. Formulas, adjustments, or new material combinations can be developed and tested much more quickly.
Sustainability through better decisions
The impact of the transformation goes far beyond efficiency. By better understanding the properties of the material, Dorfner was able to reduce its annual extraction volume by around 40 percent while extending the life of the raw material deposits by around 20 years. This shows that digitalization and sustainability are not mutually exclusive. On the contrary, the combination of data, AI, and strategic clarity can help to use resources more efficiently while tapping into new economic potential.
Empowering people instead of replacing them
Another important aspect of the transformation is how employees are treated.
Instead of viewing AI primarily as an automation tool, the company decided to take a different approach: people should be empowered to work with the new technology. To this end, an internal program was launched in which employees from different departments spent several months building up AI skills – from programming to data-driven work. This resulted in the creation of an interdisciplinary team that is actively shaping the transformation. One particularly impressive example: a master painter from the company developed into a talented programmer during the course of the program and is now driving forward new ideas in the field of AI.
Small and medium-sized enterprises and their greatest opportunity
The history of the Dorfner Group illustrates an important principle for German SMEs: the greatest competitive advantage often lies not in new technologies, but in one's own knowledge. Many SMEs have decades of experience, deep industry knowledge, and unique data sets. When this knowledge is combined with modern technology, the result is solutions that are difficult for outsiders to replicate. AI does not replace expertise—it amplifies it.
Transformation begins in the mind
In the end, the most important insight from this transformation may not be technological in nature. The change began with a clear strategic question, the courage to experiment, and the willingness to actively involve employees. Technology was a tool—not the starting point. Or as Mirko Mondan puts it in the interview: Strategy comes before AI. For many companies, this is precisely the crucial difference between hype and genuine transformation.
SHOWNOTES
Mirko Mondan https://www.linkedin.com/in/mirko-mondan-305245193/
Christian Underwood https://www.linkedin.com/in/christianunderwood/
Prof. Dr. Jürgen Weigand https://www.linkedin.com/in/j%C3%BCrgen-weigand/
StrategySummit 2026 https://www.strategyframe.ai/strategysummit2026
All links https://linktr.ee/strategyframe