Artificial Intelligence Adoption As An Institutional Process: Toward A Theoretical Framework Of Organizational Change

Abstract

Purpose: Artificial Intelligence (AI) adoption is often framed as a technological or efficiency-driven process. However, organizations do not adopt AI in a vacuum; they operate within institutional environments shaped by regulatory, normative, and cultural-cognitive pressures. This paper aims to reconceptualize AI adoption as an institutional process and to develop a theoretical framework explaining how institutional forces shape organizational change in the digital era. Methodology: This study adopts a conceptual and integrative literature review approach, drawing on neo-institutional theory and contemporary research on digital transformation and AI implementation. By synthesizing insights from institutional theory and organizational change literature, the paper develops a structured analytical framework linking institutional pressures (coercive, normative, and mimetic) to AI adoption dynamics and internal organizational transformation mechanisms. Results: The proposed framework highlights that AI adoption is not solely driven by technological rationality but also by legitimacy-seeking behavior and isomorphic pressures. It identifies key transformation dimensions, including shifts in governance structures, decision-making processes, and control systems. The model also emphasizes the emergence of algorithmic governance as a new institutionalized form of organizational regulation. Implications: This research contributes to the AI and digital innovation literature by moving beyond technological determinism and offering an institutional explanation of AI-driven organizational change. It provides a theoretical foundation for future empirical research and encourages policymakers and managers to consider institutional contexts when implementing AI strategies. The framework supports a more comprehensive and socially embedded understanding of digital transformation processes.