Conference Agenda
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Please note that all times are shown in the time zone of the conference. The current conference time is: 18th Apr 2026, 04:03:26pm CEST
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Agenda Overview |
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D322: ADOPTION OF GENERATIVE AI IN ENGINEERING DESIGN CHALLENGES AND PRACTICES
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Challenges hindering the application of GenAI methods in engineering design and the product development process: a meta-analysis 1Dresden University of Technology, Germany; 2MAN Truck & Bus SE, Germany While studies on generative artificial intelligence for product development have gained momentum, they consistently report recurring challenges. To synthesize these obstacles, we surveyed 1074 papers, resulting in a taxonomy of 27 distinct barriers. The study analyzes their frequency, discusses their interrelations, and contextualizes their root causes. Our findings show that model capability, output validity, and user trust are the most dominant obstacles, while aspects like environmental concerns are often overlooked. The study concludes with recommendations for research and practitioners. How are professional practices adopting generative AI? The case of an engineering design and product development team 1Swinburne University of Technology, Australia; 2Universidad EAFIT, Colombia Generative Artificial Intelligence (GenAI) is transforming design practice yet research lacks empirical insights into adoption in real-world design organisations. Through the case study of a European automotive OEM, we found that GenAI could accelerate ideation, but adoption was limited due to critical concerns regarding intellectual property, data security, originality, and the risk of skill atrophy. Thus, organisational capabilities like workflow specific training, transparent governance of data protection policies, and cohesive toolchains are needed for successful GenAI integration. What designers need from agentic AI: case of circularity and CMF design 1Chalmers University of Technology, Sweden; 2Intended Future, Sweden; 3Hochschule Niederrhein University of Applied Sciences, Germany; 4Royal College of Art, United Kingdom Colour, Material and Finish (CMF) designers face rising circularity demands but lack tools that combine reliable data, traceable reasoning and creative control. This paper reports a case study with automotive CMF designers, identifying pain points in data access, evaluation of circular options, authorship and trust in AI. We propose design requirements and a conceptual model for agentic AI systems that support circular CMF work while preserving designer agency, accountability, and confidence in material decisions. Still no smart service? A review of technical barriers to smart service adoption in the GenAI era 1Fraunhofer IEM, Germany; 2Chair for Advanced Systems Engineering, Heinz Nixdorf Institute, Paderborn University, Germany OEMs are shifting from product-centric offerings toward smart services, but adoption is still hindered by technical development barriers. We conduct a systematic literature review of peer-reviewed studies with original industrial evidence and identify eight barrier categories across data, semantics, integration, governance and modularity. We map them onto a smart-service architecture and key analytics roles, and relate them to GenAI building blocks such as LLMs and knowledge graphs, outlining a research agenda for overcoming technical barriers towards scalable OEM smart service development. | ||
