The Impact of Artificial Intelligence on Organizational Change Processes: A New Change Management Paradigm?

Artificial intelligence (AI) is no longer merely used as a new technological tool in organizations; it is becoming one of the constitutive elements of decision-making, coordination, control, knowledge production, work design, and employee experience. This development raises the question of whether AI, beyond being an object of organizational change, is transforming how change emerges, by whom it is directed, and over what time horizon it is managed. The aim of this chapter is to critically evaluate the explanatory power of established change management approaches in the age of AI and to develop an integrated conceptual framework for explaining AI-mediated organizational change. Lewin’s planned change approach, Kotter’s model, organizational development, continuous change, organizational learning, dynamic capabilities, socio-technical systems, and technology acceptance approaches are compared in the context of AI’s learnability, partial autonomy, opacity, and scalability. The conceptual synthesis indicates that the mechanisms of leadership, participation, communication, trust, and institutionalization emphasized by established approaches remain valid; however, assumptions of linear processes, a clearly defined end state, exclusively human agency, and one-off implementation are inadequate. The “double-loop AI change governance” model proposed in this chapter integrates the redesign of organizational task and decision architectures with monitoring the AI system throughout its life cycle in terms of performance, errors, fairness, and employee impacts. In conclusion, it is argued that it is premature to declare that AI constitutes an entirely new paradigm; nevertheless, it necessitates a substantial paradigm extension that reconceptualizes change management as a continuous, distributed, data-informed, and socio-technical governance capability.