The AI-Enabled Circular Economy: Extracting and Synthesizing Design Knowledge

Artificial intelligence (AI) increasingly enables circular economy (CE) practices like sustainable material design, robotic disassembly, and waste sorting. However, studies on AI-enabled artifacts rarely articulate design knowledge; prescriptive insights remain implicit through performance comparisons, trade-off analyses, or context-specific recommendations, limiting theoretical accumulation and practical guidance for organizations adopting AI for CE transitions. We conduct a meta-synthesis across four databases and abstract 99 empirically grounded design principles. Using structured coding and an AI-assisted extraction pipeline, we generalize one design principle per study and synthesize them into four meta-principles (MPs): MPs 1–3 capture physical-technical mechanisms that operationalize CE strategies of slowing (predictive insight), closing (material sensing and actuation), and narrowing (hybrid AI optimization) resource loops; MP4 represents socio-technical orchestration capabilities that enables organizations to sense, seize, and reconfigure circular opportunities. The synthesis links technical optimization with organizational sustainability practices, contributing cumulative, transferable design knowledge for AI-enabled circularity in information systems research.