Paper Type
Complete
Abstract
Generative Artificial Intelligence (generative AI) is reshaping digital platforms by embedding non-deterministic outputs and adaptive capabilities into platform cores and architecture. While generative AI capabilities enhance business opportunities and efficiency gains, they introduce new governance complexities across multiple architectural layers and reshape ecosystem dynamics. Drawing on the systematic literature review, this study conceptualizes the governance of generative AI-enabled platforms as a multi-layer architecture approach, encompassing: (1) governing AI contributors, (2) governing the AI core modules, and (3) governing complementors through generative AI capabilities. Across these layers, challenges arise related to the established governance dimensions of control mechanisms, decision rights, and incentive structure. This paper highlights two main results: first, the study conceptualized interrelated perspectives aligned with the platform architecture layers to investigate emerging governance challenges across distinct layers. Second, the study outlines future research directions that involve the platform architecture layers and theoretical governance dimensions for enabling generative AI platforms.
Paper Number
1623
Recommended Citation
Ubonsiri, Thanyalak, "A Layered Governance Perspective on Generative AI–enabled Platforms" (2026). AMCIS 2026 Proceedings. 9.
https://aisel.aisnet.org/amcis2026/sig_dspe/sig_dspe/9
A Layered Governance Perspective on Generative AI–enabled Platforms
Generative Artificial Intelligence (generative AI) is reshaping digital platforms by embedding non-deterministic outputs and adaptive capabilities into platform cores and architecture. While generative AI capabilities enhance business opportunities and efficiency gains, they introduce new governance complexities across multiple architectural layers and reshape ecosystem dynamics. Drawing on the systematic literature review, this study conceptualizes the governance of generative AI-enabled platforms as a multi-layer architecture approach, encompassing: (1) governing AI contributors, (2) governing the AI core modules, and (3) governing complementors through generative AI capabilities. Across these layers, challenges arise related to the established governance dimensions of control mechanisms, decision rights, and incentive structure. This paper highlights two main results: first, the study conceptualized interrelated perspectives aligned with the platform architecture layers to investigate emerging governance challenges across distinct layers. Second, the study outlines future research directions that involve the platform architecture layers and theoretical governance dimensions for enabling generative AI platforms.
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