Prioritizing barriers to artificial intelligence adoption in circular economy–oriented smart cities: analytic hierarchy process

Purpose This study aims to systematically identify, categorize and prioritize barriers to the adoption of artificial intelligence (AI) in circularity-driven smart cities. By framing the analysis through the lens of the circular economy (CE), the research addresses the knowledge gap in understanding the technological, governance, socio-technical and infrastructural barriers that hinder AI integration in sustainable urban contexts. Design/methodology/approach A picture fuzzy Z-analytic hierarchy process (PF Z-AHP) approach is employed to capture expert judgments under uncertainty and prioritize the identified barriers. The methodology integrates picture fuzzy sets with the analytic hierarchy process within a multi-expert decision-making framework to systematically evaluate and rank barriers related to digital infra-structure, governance and policy, socio-technical engagement and urban technological capacity. Findings The results show that socio-technical and community engagement barriers hold the highest overall category weight, followed by circularity-enabling digital and data infrastructure barriers. At the individual level, skills gap and resistance to change emerges as the most critical barrier, highlighting the importance of human capital readiness in facilitating artificial intelligence–enabled circular transitions in smart cities. Originality/value This research contributes to the literature by integrating fuzzy-based multi-criteria decision-making to examine AI adoption in circular economy-oriented smart cities. The study presents a novel application of PF Z-AHP for evaluating barriers under uncertainty, offering a replicable methodological framework for advancing sustainable, AI-driven circular urban transformations.