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In data-intensive markets shaped by exponential growth in digital signals, artificial intelligence, and real-time analytics, enterprises can no longer rely on static strategies. Instead, they must function as recursive adaptive systems that continuously sense, learn, and renew through data-driven mechanisms. This conceptual article synthesizes dynamic capabilities, organizational learning, and strategic renewal literatures to conceptualize the digital enterprise as an integrated adaptive architecture. It introduces the RADAR Framework—the Recursive Adaptive Digital Architecture for Renewal—a novel five-layer model centered on organizational learning that converts environmental data into sustained strategic reconfiguration. The framework explicates how sensing and data intake, interpretive learning, strategic prioritization, operational reconfiguration, and renewal outcomes interact through recursive feedback loops, enabling perpetual adaptation amid technological turbulence and market volatility. Organizational learning serves as the pivotal hub, transforming raw data into double-loop insights that fuel resource recombination and business model evolution. The paper demonstrates that higher data intensity accelerates cycle velocity and enhances adaptive capacity. The RADAR Framework extends dynamic capabilities theory into explicitly data-centric and recursive domains while offering executives a blueprint for designing learning architectures that institutionalize continuous renewal. Theoretical contributions and managerial implications underscore the shift from episodic transformation to embedded, feedback-driven adaptation. Future research directions for empirical testing across sectors are outlined.