While there is growing interest in the use of AI in numerous sectors, the human dimension of AI’s transformation has largely not been theorized in change management literature. Previous research on AI adoption ultimately relies on technology acceptance perspectives that emphasize individual cognition (perceived usefulness, ease of use) relative to comparatively less attention on leadership behaviors, communication practices, and resource dynamics that can shape how a workforce feels about AI adoption as threatening or empowering. This conceptual paper fills this void by proposing a framework for an integrative approach to AI-adapted change leadership, based on the Conservation of Resources (COR) theory and classical change management theory, that puts AI-adapted change leadership at the center of the success of digital transformation. The framework recognizes techno-uncertainty and employee AI-change readiness as parallel mechanisms of mediation and employee resilience and organizational AI maturity as boundary conditions, which influence these relationships. Six testable propositions are presented that will direct empirical research in the future, especially across technology-intensive and software organizations where AI implementation will be widespread and impactful on the professional identity of people working in the field. The paper extends the COR theory to algorithmic change, thereby making a contribution to theory, and provides a framework for algorithmic change for practitioners to sequence the leadership interventions during AI implementations. Empirical validation limitations and directions are discussed, such as using variance-based structural equation modeling.