Paper Number
ICIS2025-1419
Paper Type
Short
Abstract
The rapid adoption of generative artificial intelligence tools, such as ChatGPT, has disrupted traditional technology deployment processes. Unlike established models, where formal plans precede user adaptation, generative AI is often taken up without governance or managerial oversight. This paper investigates the socio-technical features that inhibit organizational formalization of generative AI. Based on an interpretive field study at a large technology company, we identify a set of socio-technical features—openness, contextualization, functional generality, rapid evolution, and invisibility—that privilege individual use, adaptation, and situated interaction over centralized control. Together, these features constitute what we describe as a personal-first orientation that fosters decentralized, user-driven adoption while resisting integration into formalized organizational practices. By highlighting these barriers, we contribute to information systems research on how general-purpose AI tools challenge established models of adoption and governance.
Recommended Citation
Cohen, Maayan and Zalmanson, Lior, "Why Generative AI Isn’t Formalized (Yet): Socio-Technical Barriers to Top Down Organizational Implementation" (2025). ICIS 2025 Proceedings. 9.
https://aisel.aisnet.org/icis2025/gen_ai/gen_ai/9
Why Generative AI Isn’t Formalized (Yet): Socio-Technical Barriers to Top Down Organizational Implementation
The rapid adoption of generative artificial intelligence tools, such as ChatGPT, has disrupted traditional technology deployment processes. Unlike established models, where formal plans precede user adaptation, generative AI is often taken up without governance or managerial oversight. This paper investigates the socio-technical features that inhibit organizational formalization of generative AI. Based on an interpretive field study at a large technology company, we identify a set of socio-technical features—openness, contextualization, functional generality, rapid evolution, and invisibility—that privilege individual use, adaptation, and situated interaction over centralized control. Together, these features constitute what we describe as a personal-first orientation that fosters decentralized, user-driven adoption while resisting integration into formalized organizational practices. By highlighting these barriers, we contribute to information systems research on how general-purpose AI tools challenge established models of adoption and governance.
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