Paper Type
Complete
Abstract
As organizations increasingly deploy agentic artificial intelligence (AI) systems capable of autonomous action, existing research continues to conceptualize AI primarily as a tool or decision aid. This framing limits the theoretical understanding of how agentic AI becomes organizationally embedded over time. To address this gap, this paper develops a process theory explaining how agentic AI evolves from a peripheral support tool into an organizationally embedded actor within socio-technical systems. Drawing on conceptual theorizing grounded in qualitative research, practitioner insight, and established organizational theory, we propose a four-stage process model of agentic AI embedding and theorize the mechanisms driving transitions across stages. The study advances a process-oriented understanding of AI-driven organizational transformation by shifting attention from outcomes to organizational embedding. For practitioners, the model provides a diagnostic lens for anticipating how roles, authority, and governance must evolve as AI systems become increasingly agentic.
Paper Number
1173
Recommended Citation
Sekar, Aravindh and Tech, Deb, "Agentic AI as a System within Systems: A Process Model of Organizational Embedding" (2026). AMCIS 2026 Proceedings. 2.
https://aisel.aisnet.org/amcis2026/sig_osra/sig_osra/2
Agentic AI as a System within Systems: A Process Model of Organizational Embedding
As organizations increasingly deploy agentic artificial intelligence (AI) systems capable of autonomous action, existing research continues to conceptualize AI primarily as a tool or decision aid. This framing limits the theoretical understanding of how agentic AI becomes organizationally embedded over time. To address this gap, this paper develops a process theory explaining how agentic AI evolves from a peripheral support tool into an organizationally embedded actor within socio-technical systems. Drawing on conceptual theorizing grounded in qualitative research, practitioner insight, and established organizational theory, we propose a four-stage process model of agentic AI embedding and theorize the mechanisms driving transitions across stages. The study advances a process-oriented understanding of AI-driven organizational transformation by shifting attention from outcomes to organizational embedding. For practitioners, the model provides a diagnostic lens for anticipating how roles, authority, and governance must evolve as AI systems become increasingly agentic.
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