The digital transformation has evolved into a fundamental AI transformation. Intelligent systems no longer merely provide support; they now shape processes, decisions, and organizational structures.
The research focuses on the question of how to design and reflect on high-performing and responsible human-AI ecosystems. Key areas of focus include agent-based AI architectures, AI-supported decision-making dynamics, and structural, communication, and evaluation issues related to AI transformation.
The research combines technical, economic, psychological, and educational perspectives.
The goal is to systematically examine the dynamics of hybrid human-AI constellations, the complexity of agent-based architectures and processes, and the resulting competency requirements—and to use these findings to develop a sound basis for decision-making within organizations.
Research Objectives
Research Topics
The research conducted by the Digital Transformation Expert Team focuses on key architectural, decision-making, and structural issues related to AI transformation.
The following subject areas provide an overview of our current areas of focus and research activities.
Agentic AI & Multi-Agent Systems
Analysis, design, and modeling of agent-based AI architectures—in particular, LLM-based multi-agent systems—taking into account technical structural principles, organizational embedding, and economic implications.
AI-Driven Decision-Making Dynamics
Investigation of interaction and decision-making logic in hybrid human-AI and multi-agent systems. Focus on responsibility, governance, acceptance, maturity models, and structural impacts on organizations.
Evaluation of AI Systems and Use Cases
Development and application of methodological frameworks for analyzing the performance, robustness, risks, and cost-effectiveness of AI applications, as well as for the structured evaluation of organizational implementations.
Issues Related to Expertise and Structure in AI Transformation
Analysis and systematization of competency requirements, role models, organizational structures, and communicative and narrative dynamics that shape the introduction and use of gener
a
more proactive and agent-based AI.
AI in Complex Application Contexts
Investigation and comparison of AI architectures and decision-making mechanisms in specific domains such as industry, healthcare, and security-related environments.
Research Team
Our work is highly interconnected: We conduct interdisciplinary research in collaboration with degree programs and departments at FERNFH, as well as with national and international partners.
Transfer in Education and Business
Our research doesn't end in the lab. It feeds directly into our teaching and into practice:
- Integration into Curricula and Teaching Formats
- Theses with a Practical Focus
- Innovation Projects with Companies
Students Get Involved in Research:
Through project work, internships, or theses, FERNFH students become part of current developments.
Collaborations & Partners
Together, we can achieve more. Our partners are from:
- Higher Education & Research
- SMEs & Startups
- Educational Initiatives & the Public Sector
We believe in collaboration among equals to gain new perspectives, bridge the gap between practice and science, and work together to develop innovative solutions.
Interested in meaningful research?
The expert team supervises theses in the aforementioned subject areas and is open to academic collaborations and project-based cooperation.
If you are interested in an exchange or collaboration, please feel free to contact us.
Contact
Funding
