Complexity Navigation and Dual Enablement: Business Model Innovation at the Intersection of AI and the Circular Economy

Abstract

The circular economy (CE) has emerged as a promising paradigm to address environmental challenges through resource efficiency, product life cycle transformation, and business model innovation. Yet, implementing circular business models remains challenging due to the complexity of coordinating stakeholders and managing large volumes of data, especially across the different life cycle phases. Artificial intelligence (AI) offers great potential to address these challenges. However, prior research has predominantly focused on a generic and undifferentiated application of AI within the CE, neglecting the complexity and diversity across life cycle phases. Our study seeks to address this. Drawing on the external enablement framework, we conduct a qualitative study comprising 57 semi‐structured interviews with domain experts and AI‐based ventures operating in the CE. In demonstrating how AI facilitates business model innovation by enabling navigation through circular product life cycles, our findings advance three key areas. We extend the external enablement framework by showing that external enablers can interact synergistically. Specifically, we develop a model that illustrates how the underlying dynamics of the CE and AI jointly enable business model innovation and subsequent new venture creation through complementary mechanisms, a dynamic we term dual enablement . We show how AI enhances life cycle efficiency, fosters systems interconnectivity, and supports holistic decision‐making, engendering two novel categories of business models, which we coin AI‐based business models for complexity navigation and dual enablement .