Technological enthusiasm meets factory floor reality

At the Hannover Messe trade fair, enthusiasm for technology meets the sober reality of the factory floor. Between humanoid robots and hyperscale data centers, it's easy to overlook the fact that progress is not measured by the largest GPU cluster, but by the architecture of thinking—by the interaction of many small, sensibly networked units. This is Europe's strength: engineering, systems thinking, and reason. In the fair's leitmotif – “Industrial Transformation – Make it Real” – this means not bigger, but smarter.

From monolith to modularity: agentic SLMs in industry

Large language models (LLMs) have achieved amazing things – universal language skills, creative synthesis, adaptive problem solving. But they are energy-hungry, complex, and often too large for real production environments. The counter-movement is small language models (SLMs): compact, specialized models that can run locally, be retrained, and combined in agentic architectures [1]. They speak data, sensor technology, and language at the same time – ideal for manufacturing, energy, logistics, and service.

Sustainability as a system principle: A new family of KPIs

Huge sums of money are being poured into AI infrastructure worldwide. But true industrial intelligence is not about maximizing, but optimizing. Leading analyses are raising the bar: Simplicity, energy efficiency, reusability, explainability, and resilience are becoming primary KPIs [2]—not parameter counts or GPU hours. This not only makes ecological sense, but is also robust from a business perspective. These new primary KPIs for industrial AI are, in particular:

Skills shortage, knowledge drain – and the value of centralized & decentralized knowledge

The bottleneck lies not only in computing power, but also in experiential knowledge. Demographic change, staff turnover, and project changes cause implicit knowledge to seep away. Agentic systems can stabilize: they preserve process contexts, decisions, and learning paths – a digital memory of the organization. The rule here is that decentralized knowledge (situational experiential knowledge at the point of action) complements centralized knowledge (consolidated standards, guidelines, best practices). Together, both increase resilience and the speed of implementation in production.

At the same time, the flow of talent is shifting. Trend data shows an increase in the immigration of highly qualified AI talent to Germany since 2022 [7][8][9] – driven in part by reforms and active university/ecosystems. This is not a panacea for the shortage of skilled workers, but it is an encouraging sign for the location and research.

Industry 4.1 – From digital twins to cognitive systems

The themes of Hannover Messe 2025 – resilience, sustainability, sovereignty – converge in the concept of the “cognitive industry.” Hybrid architectures combining physical processes, digital twins, and learning (artificial) intelligence are emerging everywhere. Maintenance, production planning, quality testing, or energy optimization: SLM agents bring calm to processes instead of just increasing data volumes. Companies report fewer alarm floods, shorter start-up times, and clearer responsibilities.

The silent revolution in hardware: analog, photonics, bio & quantum

While software is becoming more decentralized, hardware is undergoing a radical change. Analog and in-memory computing are shifting calculations to where the data is located. Photonic architectures compute with light; biological substrates show that physical media can serve as reservoir computers. Fortunately, German players in particular are shaping this development: Q.ANT (photonics), Akhetonics (all-optical XPU), Semron (Mem-Cap In-Memory), BrainScaleS/EBRAINS (analog-neuromorphic), as well as planqc, eleQtron, HQS, and Munich Quantum Valley in the quantum ecosystem [13], to name just a few.

Bio-computing is also gaining momentum: Mycelium networks as bio-memristive substrates (“shiitake computers,” [4]) illustrate how organic structures can efficiently solve tasks as reservoir systems. Spiking neural networks complement this with temporal coding and high energy efficiency.

Cooperation as a law of nature: Prigogine, hypercycles, and agentic AI

Systems theory provides the foundation for cooperative architectures. Ilya Prigogine showed that order arises from fluctuation (Nobel Prize 1977)[5]. Manfred Eigen described the hypercycle: a reciprocal reinforcement loop in which cooperation increases system fitness (Nobel Prize 1967)[6]. Applied to AI, this means that many small units with strong identities—I like to call them cognitons—stabilize each other instead of accumulating errors.

Government levers & learning ecosystems: SPRIND, ministries, Open edX

The Federal Agency for Breakthrough Innovations (SPRIND)[10] finances radical approaches – from photonics and bio-computing to novel AI systems. The BMI and BMDV are driving forward administrative AI and digital infrastructure [12]. Open edX is an example of open, scalable continuing education: decentralized, modular, practical [11]. For industry and small and medium-sized enterprises, this means a bridge between research, qualification, and implementation.

Recommendations for decision-makers

1) Architecture before use case: System design & KPIs first – then illustrative applications.

2) SLMs at the edge [3]: LLMs selectively (audit, synthesis), SLMs operationally close to the process.

3) Build knowledge repositories: Agentic AI + retention metrics; standard interfaces; reuse as a requirement.

4) Hardware roadmap: Pilot analog/IMC/photonics early, define OPEX path 12–18 months.

5) Talent path: open learning ecosystems (Open edX), Blue Card/opportunity card, dual programs with universities.

Conclusion – Intelligence with moderation and balance

Europe does not need to build the largest models, but rather the most sensible systems. Agential AI, sustainable hardware, and cooperative architectures are reconnecting industry and knowledge – economically stable, ecologically sustainable, and humanly understandable. The Hannover Messe trade fair shows that networked intelligence is no longer a vision – it is already happening.

Note on creation: This article was created in interaction with many committed people and the environment and was supported in selected areas by the use of modern AI tools.

License notice: Unless otherwise indicated, text and visuals are available for editorial purposes under an open, non-exclusive usage model; for individual images, see source references.

Bibliography

1. McKinsey & Company (2025). Industrial AI Resilience Index 2025.

2. KPMG (2025). AI Value Beyond ROI: Human-Centric KPIs. Global Insights Series.

3. LaRocco, J., et al. (2025). Bio-Memristive Shiitake Substrates for Neuromorphic Computing. Nature Electronics, Vol. 8, Issue 2.

4. Prigogine, I. (1977). Self-Organization in Nonequilibrium Systems. Wiley.

5. Eigen, M. (1971). The Hypercycle: A Principle of Natural Self-Organization. Naturwissenschaften, 58(10), 465–523.

6. EIT Deep Tech Talent Initiative (2025). Europe’s AI Workforce Report. European Institute of Innovation & Technology.

7. OECD (2025). AI Talent Migration Dataset 2025. OECD Digital Economy Papers.

8. ZEW Mannheim (2024). Skilled Labor Shortage and AI Skills in Germany. ZEW Discussion Paper.

9. SPRIND (2025). Breakthrough Innovations in Photonics, Quantum & AI. Federal Agency for Breakthrough Innovations, Leipzig.

10. Open edX (2025). Global Open Learning Ecosystem – Annual Report 2025. edX.org.

11. BMWK / BMDS (2025). Germany's Digital Strategy 2025: Administration, Infrastructure, AI.

12. Q.ANT GmbH, Akhetonics GmbH, Semron GmbH, BrainScaleS / EBRAINS, planqc GmbH, eleQtron GmbH, HQS Quantum Simulations GmbH (2024–2025). Public project and press releases.

About the author:

Dr. Johannes Nagele | Head of Innovation | Alexander Thamm GmbH

Dr. Johannes Nagele is an expert in artificial intelligence with over 15 years of experience in data science, software development, and machine learning. With his scientific background in biophysics and brain research, he is responsible for AI innovation at Alexander Thamm [at] and plays a key role in driving research, technological developments, and customer projects. His focus is on multi-agent systems, AI memory, unsupervised learning, and human-AI co-learning. As a consultant, coach, speaker, and host of the podcast “Nagele mit Köpfchen” (Nagele with brains), he conveys complex content with vision, practical relevance, and unconventional approaches.