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A survey of generative AI adoption amongst industrial design experts

Abrahamsen, Trym and Sjödell, Charlotte LU (2026) Design reserach society
Abstract
This paper is based on interviews with 20 expert industrial designers with 8-40+ years of experience about how they are adopting generative AI into their workflows. Using thematic analysis, the study reveals how top-down pressure for speed and efficiency drives rapid, bottom-up adoption of generative AI tools. The core finding is that in this scramble for adoption, designers and design businesses risk cognitive offloading and significant loss of control over micro-decisions in key parts of the design process. By critically examining the potential loss of control and reflection, the inquiry explores how the value of industrial design work extends far beyond generating contextless output and what consequences this has for generative AI... (More)
This paper is based on interviews with 20 expert industrial designers with 8-40+ years of experience about how they are adopting generative AI into their workflows. Using thematic analysis, the study reveals how top-down pressure for speed and efficiency drives rapid, bottom-up adoption of generative AI tools. The core finding is that in this scramble for adoption, designers and design businesses risk cognitive offloading and significant loss of control over micro-decisions in key parts of the design process. By critically examining the potential loss of control and reflection, the inquiry explores how the value of industrial design work extends far beyond generating contextless output and what consequences this has for generative AI implementation. The investigation concludes with an actionable roadmap for decision-makers in industrial design businesses, considering when to adopt generative AI. (Less)
Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
AI and Design, industrial design
host publication
DRS Biennial Conference Series : DRS2026 - DRS2026
editor
Simeone, L ; Gray, C.M. ; Verhoeven, A. ; de Götzen, A. ; Bakırlıoğlu, Y. ; Zohar, H. ; Stead, M. and Buwert, P.
pages
15 pages
publisher
Design Research Society
conference name
Design reserach society
conference location
Edinburgh, United Kingdom
conference dates
2026-06-08 - 2026-06-18
external identifiers
  • scopus:105040634578
DOI
10.21606/drs.2026.2166
language
English
LU publication?
yes
id
285dc69d-9395-4ba3-a365-40907d94861a
date added to LUP
2026-06-15 20:53:45
date last changed
2026-09-15 10:51:55
@inproceedings{285dc69d-9395-4ba3-a365-40907d94861a,
  abstract     = {{This paper is based on interviews with 20 expert industrial designers with 8-40+ years of experience about how they are adopting generative AI into their workflows. Using thematic analysis, the study reveals how top-down pressure for speed and efficiency drives rapid, bottom-up adoption of generative AI tools. The core finding is that in this scramble for adoption, designers and design businesses risk cognitive offloading and significant loss of control over micro-decisions in key parts of the design process. By critically examining the potential loss of control and reflection, the inquiry explores how the value of industrial design work extends far beyond generating contextless output and what consequences this has for generative AI implementation. The investigation concludes with an actionable roadmap for decision-makers in industrial design businesses, considering when to adopt generative AI.}},
  author       = {{Abrahamsen, Trym and Sjödell, Charlotte}},
  booktitle    = {{DRS Biennial Conference Series : DRS2026}},
  editor       = {{Simeone, L and Gray, C.M. and Verhoeven, A. and de Götzen, A. and Bakırlıoğlu, Y. and Zohar, H. and Stead, M. and Buwert, P.}},
  keywords     = {{AI and Design; industrial design}},
  language     = {{eng}},
  month        = {{06}},
  publisher    = {{Design Research Society}},
  title        = {{A survey of generative AI adoption amongst industrial design experts}},
  url          = {{http://dx.doi.org/10.21606/drs.2026.2166}},
  doi          = {{10.21606/drs.2026.2166}},
  year         = {{2026}},
}