Management

Artificial intelligence (AI) offers a wide range of possibilities in industrial production, but it also presents challenges regarding employee acceptance. AI demonstrators are therefore of central importance, as they enable hands-on experience with AI. However, there has been a lack of systematically identified requirements for demonstrators that specifically promote acceptance and address negative emotions. Using a multi-stage research design, 69 requirements were identified, structured into functional requirements, quality requirements, and boundary conditions.

The use of experiential knowledge is a key success factor for companies. Based on four corporate case studies, this article analyzes the technical, organizational, and personnel challenges associated with the use of retrieval-augmented generation (RAG) systems. The results show that the success of such systems depends on the strategic development and maintenance of the knowledge base, as well as on employee engagement.

Companies are increasingly challenged to address shortages of skilled workers while meeting rising demands for productivity, flexibility, and innovation. Because labor supply can only be expanded to a limited extent, there is a growing focus on designing work systems with productivity in mind. Autonomous systems offer significant potential in this regard. Their implementation requires not only technical adjustments but, above all, changes in organization, skills, and work design. This article analyzes empirically grounded change requirements in existing work systems as well as associated economic potential.

MTM methods are a proven standard for analyzing and designing human work processes. However, work planners continue to face challenges in applying these methods correctly and efficiently. Artificial intelligence — particularly in the case of large language models — holds significant potential for combatting these challenges. However, the use of AI raises legitimate questions regarding the reliability and traceability of the results. The approach presented here combines LLMs with a rule-based algorithm to extract the information required for MTM analyses from textual process descriptions such as work instructions. This information is then translated into MTM analyses in accordance with the MTM methodology. This approach ensures that the resulting analyses can be transparently traced back to the original input data by the user.

As Industry 4.0 and artificial intelligence reshape organizational capabilities, traditional training systems struggle to keep pace with evolving skill requirements. This paper introduces Trendiation—a structured methodology for translating emerging trends into actionable strategies—as a systematic approach to this challenge. Through a workshop-based application examining Edutainment, Human-Centered Design, and Workforce Transformation, we demonstrate how organizations can move from abstract trend identification to concrete qualification requirements and prioritized training initiatives. The method produces a traceable artifact chain spanning trend framing, capability-gap assessment, and implementation roadmaps. Participant evaluations indicate high perceived clarity and practical utility. By bridging foresight analysis with participatory design, Trendiation enables organizations to proactively cultivate adaptive capabilities and build learning cultures aligned with future work ...

Integrating artificial intelligence (AI) into workplace processes promises significant efficiency gains, yet organizations face numerous ethical challenges that stakeholders are often initially unaware of—from opacity in decision-making to algorithmic bias and premature automation risks. This paper presents the design and validation of tachAId, an interactive advisory tool aimed at embedding human-centered ethical considerations into the development of AI solutions. It reports on a validation study conducted across two distinct industrial AI applications with varying AI maturity. tachAId successfully directs attention to critical ethical considerations across the AI solution lifecycle that might be overlooked in technically-focused development. However, the findings also reveal a central tension: while effective in raising awareness, the tool’s non-linear design creates significant usability challenges, indicating a user preference for more structured, linear guidance, especially ...

As part of the regional competence center humAIne, funded by the Federal Ministry of Research, Technology, and Space (BMFTR), a process was developed using co-determination dialogues to establish a common understanding of the challenges involved in the introduction of artificial intelligence (AI) between management, employees, and interest groups. Experiences from project partner companies such as Doncasters Precision Castings in Bochum GmbH (DPC) exemplify how co-determination dialogues not only help to develop legally binding regulations for manageable, operationally anchored, sustainable AI use but also initiate continuous qualification processes for all stakeholder groups in accordance with Articles 4 and 5 of the EU AI Act.

The introduction of artificial intelligence (AI) in companies poses new challenges for regulation and co-determination. Binding requirements have been in force since the 2025 EU AI Act, which must be linked nationally with the Works Constitution Act (BetrVG). The regional competence center humAine has developed a model works agreement on AI (MBV KI) in accordance with Section 77 BetrVG, which strengthens co-determination rights in companies and implements European regulations in a practical way. Flanked by co-determination dialogues, the MBV KI enables company-specific adaptation for responsible and human-centered AI use. Using selected parts of the MBV KI as examples, this article shows how a framework works agreement on AI can be designed and discusses its transferability to companies without a works council. The MBV KI presented here contributes to the sustainable, socially secure design of the digital transformation.

The governance of generative artificial intelligence (GenAI) usage is often described as a formalized reporting system. This neglects the early-stage mechanisms of coping with ethical challenges during the GenAI implementation period. Exploratory empirical findings from the Ruhr area reveal that managerial communicative practices serve as a substitute for missing institutional structures, particularly at an early stage of GenAI implementation in SMEs.