generative artificial intelligence

Guidelines for the Fair Use of Generative AI

Guidelines for the Fair Use of Generative AI

Practical examples from production management and social welfare
Anja Gerlmaier, Paul-Fiete Kramer ORCID Icon, Dirk Marrenbach ORCID Icon, René Wenzel ORCID Icon
With the rapid spread of assistive AI tools such as ChatGPT, Gemini, and Copilot, companies are being challenged to address the opportunities and challenges of artificial intelligence. Based on two practical examples, this article provides insight into how companies can use company-specific risk and potential analyses to develop guidelines for the fair and responsible use of AI.
Industry 4.0 Science | Volume 42 | 2026 | Edition 1 | Pages 22-28 | DOI 10.30844/I4SE.26.1.22
Potentials, Premises, Perspectives

Potentials, Premises, Perspectives

Using LLMs to reinterpret corporate knowledge management
Vanessa Kuks ORCID Icon, Pius Finkel ORCID Icon, Peter Wurster ORCID Icon
Demographic change is exacerbating the shortage of labor and skilled workers in the manufacturing industry, making knowledge management an increasingly important issue in many companies. Collecting and preserving tacit knowledge poses a particular challenge. This study examines the extent to which large language models (LLMs) can provide meaningful support in knowledge gathering through expert interviews. Three experts test and evaluate a personalized chatbot that has been developed using ChatGPT-5. The results of the interview are promising, but the summary shows room for improvement.
Industry 4.0 Science | Volume 41 | Edition 6 | Pages 48-56 | DOI 10.30844/I4SE.25.6.48
Generative Artificial Intelligence – New Horizons for Technology Management?

Generative Artificial Intelligence – New Horizons for Technology Management?

A case study from the manufacturing industry
Günther Schuh ORCID Icon, Leonard Cassel, Bastian Thanhäuser, Thomas Scheuer
While generative artificial intelligence has gained more visibility and achieved initial successes, it is largely unused in the industry context. In contrast, its development and versatility point to a promising application for industrial manufacturing – especially in cases where complex challenges such as decisionmaking or process optimization are present. Showcasing the various development horizons and several example case studies provides a particularly illuminating illustration of its potential for the field of technology management.
Industry 4.0 Science | Volume 40 | 2024 | Edition 3 | Pages 6-13