@inproceedings{61949,
  abstract     = {{<jats:title>ABSTRACT:</jats:title><jats:p>Facing increasingly dynamic market environments and global challenges such as climate change and resource scarcity, companies are under constant pressure to innovate and remain competitive. As technology is a key enabler, companies need to understand the drivers of technological change. Technology Foresight systematically identifies and analyzes emerging technologies to support engineering design decisions. However, the growing volume of data is outpacing manual processing capabilities. This research explores the integration of Generative AI to enhance Technology Foresight by automating technology analysis and information synthesis. This paper presents a comprehensive problem analysis, reviews existing solutions, and proposes a framework that demonstrates the potential of Large Language Models combined with a Retrieval Augmented Generation architecture to transform Technology Foresight.</jats:p>}},
  author       = {{Ellermann, Kai and Seidenberg, Tobias and Asmar, Laban and Knepler, Jonas and Dumitrescu, Roman}},
  booktitle    = {{Proceedings of the Design Society}},
  issn         = {{2732-527X}},
  pages        = {{2221--2230}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{Leveraging GenAI for technology foresight}}},
  doi          = {{10.1017/pds.2025.10236}},
  volume       = {{5}},
  year         = {{2025}},
}