Artificial Intelligence as a Transformational driver in Change Management: a wake-up call
As AI technologies, particularly Large Language Models (LLMs), are embedded in business processes, they drive a profound, meta-redefinition of how change is conceived, executed, and managed. This paper examines the revolutionary and transformative role of Artificial Intelligence (AI) in Organizational Change Management. In recent years, organizations across all sectors have faced unprecedented digital disruption, driven by the convergence of automation, data analytics, and Artificial Intelligence (AI). As diverse organizations navigate this complex transformation landscape, understanding how AI reshapes traditional Change Management practices has become increasingly important. In this remit, this study contributes to the academic debate by systematically exploring the intersection of AI technologies and Change Management (CM), aiming to clarify how emerging digital tools influence established instances of organizational transformation. The authors are particularly interested in exploring the emergent dynamics between traditional Change Management and AI, including key challenges and opportunities, and in identifying a critical gap: the absence of standardized approaches to integrating AI into the methodologies used by Change Management practitioners. Consequently, this paper offers Researchers and SMEs (Subject Matter Experts) a starting point to critically interpret the disconnect between AI capabilities and traditional Change Management practices. To promote responsible adoption, this study advocates a fundamental shift in perspective: Artificial Intelligence should be viewed not just as a technical tool, but as a strategic partner in the co-design of organizational change processes. Keywords: AI; Change Management; LLMs; Artificial Intelligence DOI: 10.7176/EJBM/17-10-02 Publication date: December 30th 2025
Authors
- Luca Caiulo
- Laura Campoy
Publication Details
- Journal
- Journals & Books Hosting (International Knowledge Sharing Platform)
- Published
- 2025-12-27
- Primary Topic
- Corporate Management and Leadership
- Type
- article
- Field-Weighted Citation Impact
- 0.00