AI-enabled dynamic capabilities for sustainability: structural boundary conditions of environmental value creation

Purpose Manufacturing firms increasingly invest in artificial intelligence (AI) to support digital transformation and industrial decarbonization, yet these investments do not consistently generate meaningful environmental improvements. Addressing this sustainable development problem, this study aims to examine how supplier and customer integration shape the relationship between AI capability and environmental performance. Design/methodology/approach Grounded in dynamic capabilities theory (DCT), this study tests a moderated configurational model using survey data from 426 Chinese manufacturing firms. Hierarchical regression and three-way interaction analysis examine the moderating and synergistic roles of supplier and customer integration. Robustness checks address non-response bias, common method bias and endogeneity through 2SLS. Findings AI capability is positively associated with environmental performance, and supplier integration strengthens this relationship. Customer integration has no stable standalone moderating effect, but its joint alignment with supplier integration produces a significant positive three-way interaction. AI therefore generates greater environmental value when embedded in a bidirectionally integrated supply chain. Practical implications The findings suggest that firms should not deploy AI systems in isolation but align them with upstream and downstream integration mechanisms. Strengthening supplier collaboration enhances structured data inputs for AI optimization, while coordinated customer integration helps align environmental innovation with market expectations. Managers should embed AI initiatives within closed-loop supply chain governance structures to maximize sustainability outcomes. Social implications The societal value of AI-enabled manufacturing depends on supply-chain arrangements that support reliable environmental information and coordinated action. Policies integrating digital upgrading with environmental data standards, responsible procurement and cross-organizational accountability can better advance sustainable industrialization, responsible production and climate action, thereby contributing to SDGs 9, 12 and 13. Originality/value This study conceptualizes AI capability as an analytical enabler of dynamic capabilities rather than a standalone technological resource. It refines the application of DCT by introducing a technology–structure synergy perspective, revealing the configurational and threshold conditions under which AI translates into environmental performance. By empirically identifying a three-way interaction effect, this study bridges digital capability research and sustainable operations management within a supply chain embeddedness framework.