AI-driven transformation in manufacturing SMEs: optimizing resource allocation and environmental management for enhanced performance and sustainability

Abstract

Purpose This study examines AI adoption in manufacturing SMEs and its impact on sustainability. It focuses on internal mechanisms, such as resource allocation efficiency and environmental management practices as mediators and highlights top management support as a moderator. The research shows AI fosters sustainability by improving operational efficiency and aligning sustainability goals. Design/methodology/approach Data were collected from 481 senior managers and employees in manufacturing SMEs in northern China. A structured questionnaire gathered responses, and PLS-SEM was used to analyze relationships among AI adoption, resource allocation efficiency, environmental management practices and sustainability. This approach thoroughly examines the complex dynamics between AI and manufacturing, enabling a comprehensive study of AI, manufacturing, and sustainability outcomes. Findings The results indicate that AI adoption is positively associated with sustainable performance, resource allocation efficiency and environmental management practices, which are key mediators. Top management support is crucial for maximizing AI's operational and environmental benefits. The study shows that AI supports efficiency and sustainability by enhancing resource management and environmental practices in SMEs. Originality/value This paper contributes to the literature on AI adoption and sustainable performance in manufacturing SMEs by proposing an integrated framework that links AI adoption to operational efficiency, environmental practices, and managerial support. By focusing on the internal mechanisms that enable AI to drive sustainability, the study fills a gap in understanding how AI can facilitate AI-driven transformation in SMEs. These findings provide valuable managerial insights for SMEs looking to leverage AI as a strategic tool to enhance sustainable performance.

The authors' abstract, as published at the source. Journal of Manufacturing Technology Management, 2026 · DOI ↗