Artificial Intelligence (AI) in the Circular Economy: A Systematic Literature Review

Abstract

The transition from linear production and consumption to a circular economy (CE) requires organizations to manage complex information about products, materials, waste streams, and supply-chain activities. Artificial intelligence (AI) may support this transition, but research on its role across business and organizational contexts remains fragmented. This study systematically reviews how AI is applied to CE practices and identifies the main thematic areas in the literature. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement, Scopus was searched for English-language studies published up to 2025. After screening 301 records and assessing 56 full texts, 25 studies were included and analyzed through qualitative content analysis and thematic narrative synthesis. Four clusters emerged: AI-enabled circular operations and supply-chain transformation; resource recirculation, recovery, and loop closure; organizational and ecosystem capabilities for circular innovation and business-model transformation; and green marketing, stakeholder engagement, and corporate disclosure. Across these clusters, AI is reported primarily to improve visibility, prediction, classification, optimization, and coordination. Evidence is strongest for operational efficiency and waste-related applications, while support for higher-value circular strategies and system-level loop closure remains limited. AI should therefore be regarded as a conditional enabler rather than a sufficient driver of circular transformation; its contribution depends on circular objectives, organizational capabilities, human expertise, infrastructure, financing, governance, and interorganizational cooperation.