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Beyond Blueprints: Adoption of AI in Architecture

Ström, Marcus LU and Friman, Hugo (2024) INTM01 20241
Innovation Engineering
Abstract
This thesis investigates the adoption of artificial intelligence (AI) solutions among
architects in Sweden. Architects are currently experiencing headwinds and
decreased demands for their services as increasing interest rates and a weak
economy have contributed to a decline in construction activity.
Simultaneously, advancements in AI have introduced a range of innovative
technologies to the field of architecture. One notable application of AI is the
generation of planning proposals based on user-defined parameters. The term
"Generative Planning AI" (GPAI) has been coined by the authors to describe this
particular use of AI in architecture. GPAI stands to transform architectural practices,
although it has yet to be widely adopted.
... (More)
This thesis investigates the adoption of artificial intelligence (AI) solutions among
architects in Sweden. Architects are currently experiencing headwinds and
decreased demands for their services as increasing interest rates and a weak
economy have contributed to a decline in construction activity.
Simultaneously, advancements in AI have introduced a range of innovative
technologies to the field of architecture. One notable application of AI is the
generation of planning proposals based on user-defined parameters. The term
"Generative Planning AI" (GPAI) has been coined by the authors to describe this
particular use of AI in architecture. GPAI stands to transform architectural practices,
although it has yet to be widely adopted.
The objective of this research is to identify and analyze the critical factors that drive
or hinder the adoption of GPAI technologies. As well as how these factors depend
on several variables such as the size of the firm, the client base, and previous
technological experience. The research methodology consists of a review of existing
literature on innovation adoption and a multi-case study involving 20 architectural
firms.
The results indicate that the primary drivers for adopting GPAI are improved time
efficiency, enhanced design quality, and expanded capabilities. On the other hand,
the principal barriers include concerns about the quality of AI-generated outputs and
a general lack of technological expertise within architectural firms. This study offers
valuable insights into how these drivers and barriers differ among various types
of architectural firms, providing a deeper understanding of factors affecting the
adoption of GPAI in the architecture industry. (Less)
Please use this url to cite or link to this publication:
author
Ström, Marcus LU and Friman, Hugo
supervisor
organization
alternative title
Arkitektonisk Intelligens: AI och Designprocessen
course
INTM01 20241
year
type
H2 - Master's Degree (Two Years)
subject
keywords
AI, Architecture, Generative Planning AI, Diffusion of Innovation, Technology Acceptance Model
language
English
id
9170625
date added to LUP
2024-08-08 08:45:15
date last changed
2024-08-08 08:45:15
@misc{9170625,
  abstract     = {{This thesis investigates the adoption of artificial intelligence (AI) solutions among
architects in Sweden. Architects are currently experiencing headwinds and
decreased demands for their services as increasing interest rates and a weak
economy have contributed to a decline in construction activity.
Simultaneously, advancements in AI have introduced a range of innovative
technologies to the field of architecture. One notable application of AI is the
generation of planning proposals based on user-defined parameters. The term
"Generative Planning AI" (GPAI) has been coined by the authors to describe this
particular use of AI in architecture. GPAI stands to transform architectural practices,
although it has yet to be widely adopted.
The objective of this research is to identify and analyze the critical factors that drive
or hinder the adoption of GPAI technologies. As well as how these factors depend
on several variables such as the size of the firm, the client base, and previous
technological experience. The research methodology consists of a review of existing
literature on innovation adoption and a multi-case study involving 20 architectural
firms.
The results indicate that the primary drivers for adopting GPAI are improved time
efficiency, enhanced design quality, and expanded capabilities. On the other hand,
the principal barriers include concerns about the quality of AI-generated outputs and
a general lack of technological expertise within architectural firms. This study offers
valuable insights into how these drivers and barriers differ among various types
of architectural firms, providing a deeper understanding of factors affecting the
adoption of GPAI in the architecture industry.}},
  author       = {{Ström, Marcus and Friman, Hugo}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Beyond Blueprints: Adoption of AI in Architecture}},
  year         = {{2024}},
}