Exploring configurational pathways to AI-powered regenerative practices in the cultural and creative industries

Abstract

Abstract Recent advances in artificial intelligence (AI) have accelerated organizational experimentation with new forms of value creation across cultural and creative industries. While prior research has examined AI adoption, creative augmentation, and ethical concerns, limited attention has been paid to how AI contributes to regenerative forms of value creation. Addressing this gap, this study investigates how regenerative practices emerge from different configurations of AI-enabled organizational capabilities in cultural and creative industries. Drawing on survey data from 110 European creative organizations and applying fuzzy-set qualitative comparative analysis (fsQCA), the study identifies one dominant pathway to regenerative practices, centered on the joint presence of adaptive learning and collaborative engagement, alongside supplementary configurations with limited empirical coverage. The findings reveal that regenerative practices do not arise from AI adoption or human-centric design alone, but from specific combinations of resource efficiency, environmental impact reduction, adaptive learning, and collaborative engagement. The results further outline causal asymmetry, indicating that the absence of regeneration cannot be explained as the simple inverse of its presence. This study contributes to research on AI, regenerative business models in cultural and creative industries, offering theoretical insights into AI-enabled regeneration and practical guidance for managing human–AI collaboration toward regenerative outcomes.