Resource dependence theory (RDT) posits that organizations rely on their external environments for critical resources. This reliance creates dependencies that organizations strategically manage through approaches such as partial and full absorption as well as influencing and cooptation tactics. However, some have questioned whether RDT has limited explanatory power to contemporary phenomena given today’s technologically advanced business landscape. To address this question, this paper develops theory to explain how the advent of artificial intelligence (AI) fundamentally alters dependency relationships between organizations and their environments. AI acts as a system-level force, transforming industry practices and enabling sophisticated decision-making processes. We integrate AI into RDT core principles, demonstrating how traditional assertions shift as AI creates larger system-level changes and evolves from point solutions to system solutions. This shift necessitates reconceptualizing resource dependence dynamics, as AI has the potential to introduce new dependencies. We also suggest that traditional dependence managing strategies may become less effective, requiring more dynamic and anticipatory approaches to dependence management. Our integration of AI into RDT core principles has the potential to enhance RDT’s contemporary relevance, offering a framework for understanding how organizations navigate the AI-driven landscape of the Fourth Industrial Revolution.
