Type of Publication: Article in Journal

An Affordance Perspective on Generative AI in Enterprise Architecture

Author(s):

Banh, Leonardo; Tran, Tuan Khang; Strobel, Gero

Title of Journal:

IEEE Access

Volume (Publication Date):

13 (2025)

pages:

192711-192730

Digital Object Identifier (DOI):

doi:10.1109/ACCESS.2025.3631323

Citation:

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Abstract

Generative artificial intelligence (GenAI) has emerged as a transformative technology capable of creating novel content across multiple formats. Its rapid advancement fuels innovations and implications for various fields, including the unexplored domain of enterprise architecture. This study investigates the potential applications of generative AI in enterprise architecture through semi-structured expert interviews, analyzed using the theoretical lens of affordance theory. The research identifies four key affordances that generative AI offers enterprise architects: information research and synthesis, text generation and refinement, insight generation and decision support, and architectural content creation. The findings reveal that generative AI functions as a collaborative partner for enterprise architects, providing information and inspiration that, when combined with professional knowledge and expertise, enables the creation of high-quality and innovative enterprise architecture content. This technology shows promise in enhancing productivity, efficiency, and effectiveness in enterprise architecture practices.