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In a world of increasing volatility, uncertainty, complexity, and ambiguity (VUCA), traditional change management models, such as Lewin’s “unfreeze, change, refreeze” model and Kotter’s eight-step process, hardly meet the needs of modern organizations. Despite years of theoretical development, approximately 70% of change initiatives still fail (Kotter, 1995; Bankins, & Formosa, 2023). This is largely due to an underestimation of human dynamics, a lack of immediate feedback, and an inability to respond effectively to constantly evolving situations. This article presents the AI-driven Adaptive Simulation Framework (AI-ASF), a novel model that combines artificial intelligence, predictive analytics, and real-time simulation to improve the success and sustainability of transformational change. Unlike static and linear models, the AI-driven Adaptive Simulation Framework (AI-ASF) is dynamic and context-aware. This model uses machine learning, natural language processing (NLP), and multi-agent models to assess employee motivation, predict resistance, and simulate tailored interventions before implementation. By proactively managing risks, strengthening stakeholder engagement, and ensuring the alignment of interventions with organizational objectives, AI-ASF facilitates the link between the design and implementation of change processes. Drawing on a qualitative multiple-case study methodology, this research examines the use of AI-ASF in various sectors, including healthcare, finance, and technology, providing valuable insights into its scalability and real-world applications. Early results demonstrate that AI-ASF can significantly reduce failure rates. It provides leaders with real-time, data-driven insights and fosters more sustainable, human-centered transformations. As organizations accelerate their digital transformation efforts, AI-ASF not only makes a theoretical contribution to the study of change management but also offers a practical guide for leading resilient and adaptive change. It redefines change management as a continuous, intelligent, and ethical process that helps leaders manage complexity with insight, flexibility, and precision.
Published in: Scientific Societal & Behavioral Research Journal
Volume 2, Issue 1, pp. 92-92
DOI: 10.63329/av3nz12327