<p>The advent of Generative AI (Gen-AI) has precipitated an epistemological crisis in strategic management, challenging the foundational assumptions of enterprise digital transformation. Traditional frameworks, operating under a “Paradigm of Rational Mastery”, view technology as a predictable, manageable tool. This paper argues that the unique technical characteristics of Gen-AI—specifically its “Emergent Abilities” and “Black-Box Nature”—have given rise to a novel strategic condition termed “Foundational Uncertainty”, rendering these established models obsolete. This study critiques the prevalent “Two-Layer Model” of digital transformation, which is predicated on predictable technological advancement and planned “enablement”. In its place, this paper develops and proposes a “Dynamic-Adaptive Model” designed for the Gen-AI era. This new model reconceptualizes the firm-technology relationship as a co-evolutionary process. It posits that firms must shift from planned implementation to continuous “Adaptive Sensemaking”, an action-driven process of experimentation and learning to navigate the technology’s unpredictability. At the enterprise level, strategy must become an “Evolutionary Process” that emerges from this learning. The model’s central mechanism is a cyclical interplay of “Contention &amp; Adaptation”—a generative tension between top-down managerial control (exploitation) and bottom-up, technology-driven learning (exploration) that propels organizational evolution. Consequently, the source of sustained competitive advantage shifts from possessing static, inimitable (VRIN) resources to cultivating a higher-order “Dynamic Adaptive Capability”—the organizational meta-capability for reconfiguring its own learning processes to thrive amidst unknowable change. The paper concludes by outlining the model’s significant theoretical contributions to the literature on digital transformation, Dynamic Capabilities, and the Resource-Based View, providing a new framework for navigating the Gen-AI frontier.</p>