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BARRIERS TO THE USE OF ARTIFICIAL INTELLIGENCE IN THE PRODUCT DEVELOPMENT – A SURVEY OF DIMENSIONS INVOLVED

Published online by Cambridge University Press:  19 June 2023

Benedikt Müller ,
Daniel Roth  and
Matthias Kreimeyer
Benedikt Müller*
Affiliation:
University of Stuttgart
Daniel Roth
Affiliation:
University of Stuttgart
Matthias Kreimeyer
Affiliation:
University of Stuttgart
*
Müller, Benedikt, University of Stuttgart, Germany, benedikt.mueller@iktd.uni-stuttgart.de

Abstract

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Artificial intelligence (AI) is seen as a great opportunity to secure future competitiveness in many corporate sectors. Potential for its use also exists in product development (PD) activities due to the amount of data generated and processed. Nevertheless, there are problems in applying the technology. This paper addresses current challenges based on a literature review, considering three disciplines that are necessary for the scope of this paper as a minimum: AI itself, information technology infrastructures (ITI) in context of digital transformation (DT), and PD as an application area. Building on the basic considerations of the state of the art, a link between the domains is established by outlining a possible reference framework towards the utilization of AI applications in PD. This enables an expanded interdisciplinary understanding. Key obstacles appear specifically to be difficult collaboration conditions between the disciplines of PD and AI applications development due to communication problems. Reasons for this include:

  • – Meta models of PD do not provide a sufficient information base

  • – Lack of standardized process models for the deployment of AI

Keywords

Artificial intelligenceDesign methodologyMachine learningMulti- / Cross- / Trans-disciplinary processesSmall and medium-sized enterprises
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 757 - 766
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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