Generative AI has developed enormous momentum in a short period of time. Chatbots, code assistants, text-to-image systems and automatic analysis of customer feedback are no longer visions of the future. But the more this technology penetrates productive business processes, the more urgent a central question becomes:
How can AI be used securely, flexibly and in a way that is specific to the company?
The answer lies in a paradigm shift. Instead of cloud-first and closed source, open source models, on-premises infrastructures and multimodal architectures are increasingly coming to the fore. These not only enable technical sovereignty – they also create trust, efficiency and genuine added value.
Open AI models: More than just an alternative
While commercial models such as GPT-4 or Gemini demonstrate enormous capabilities, one problem remains for many companies: they have no control over model architecture, training data or response logic. This is a deal-breaker, especially in regulated or research-intensive industries.
Why open source is becoming increasingly relevant:
- Individual adaptability: Models such as Mistral, LLaMA or Falcon can be retrained (fine-tuned) with your own data and optimised for specific subject areas.
- Full transparency: Disclosed model weights, licences and architectures enable a well-founded risk and compliance assessment.
- Independence from providers: No vendor lock-ins, full cost control and long-term planning.
Practical example: Mechanical engineering & predictive maintenance
A medium-sized manufacturing company relies on AI-supported maintenance: sensors on machines continuously record temperature, vibration and speed. These data are used to identify patterns that indicate impending failures. Since this sensor data cannot be transferred to external cloud environments, the company opts for an open-source model that is operated locally and fine-tuned with historical machine data. The result: fewer failures, more predictable maintenance intervals – without any risk to data security.
On-premises: When data protection is non-negotiable
Many AI services run in US data centres by default – even if they are operated in an “EU region”. For companies that work with particularly sensitive data, this is a risk they cannot and must not take.
On-premises solutions offer:
- Maximum data sovereignty: data does not leave the company – all processing procedures remain traceable and under control.
- Legal certainty & compliance: Particularly important in GDPR-regulated industries or for works councils with a high degree of co-determination.
- Technical integration: Local models can be integrated more deeply into existing IT landscapes, e.g. with internal APIs, intranets or protected databases.
Example: Hospital information systems (HIS) & medical history support
A municipal hospital wants to use AI to reduce the workload of its medical staff. Specifically, an AI model based on doctors’ notes will automatically generate initial drafts of medical reports. Since this involves personal health data, transferring it to cloud services is not legally permissible – nor is it ethically justifiable. The hospital decides to operate an open-source language model on-premises. The model is trained with anonymised sample data, hosted locally and integrated directly into the hospital information system. This keeps the entire process under control while reducing the workload for specialist staff.
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Multimodal models: When language alone is not enough
The first generation of generative AI was primarily text-based. However, with the increasing complexity of real-world use cases, there is a growing need for multimodal intelligence – i.e. models that can work with text, images, videos and structured data simultaneously.
Advantages of multimodal AI:
- Better contextual understanding: Information from multiple sources is combined – e.g. a scanned contract and the associated email correspondence.
- New fields of application: From visual quality control in manufacturing to automated evaluation of video conferences.
- User-centred communication: Chatbots that interpret screenshots or explain a user interface are no longer a thing of the future.
Example: Technical field service in industry
Ein Unternehmen aus dem Energiesektor betreibt deutschlandweit Trafostationen. Die Außendiensttechniker melden Störungen über eine mobile App – oft inklusive Foto des defekten Bauteils und kurzer Sprachnotiz. Eine multimodale KI wertet das Foto aus, erkennt das Bauteil, analysiert die Beschreibung und schlägt passende Ersatzteile und Handlungsschritte vor. Gleichzeitig wird eine E-Mail an den Kunden formuliert, die erklärt, wie lange die Reparatur dauern wird. So wird die gesamte Servicekommunikation beschleunigt, Fehlentscheidungen werden reduziert – und die KI arbeitet nicht isoliert, sondern im praktischen Zusammenspiel mehrerer Modalitäten.
Conclusion: Sovereign AI requires new technological principles
Companies that want to use generative AI in a long-term, scalable and responsible manner need solutions that offer more than short-term efficiency gains. The next steps require strategic decisions:
Three cornerstones for sustainable AI:
- Open source – for transparency, adaptability and freedom of innovation
- On-premises – for data protection, control and seamless integration
- Multimodality – for intelligent linking of language, image and structure
