AI-Enabled Governance in Traditional Chinese Medicine Enterprise Clusters: A Lifecycle and Stakeholder Perspective for Sustainable Development

Abstract

Artificial Intelligence (AI) has emerged as a transformative tool for enhancing governance in enterprise clusters, improving competitiveness, and advancing sustainable development goals (SDGs). Despite its potential, the effective application of AI-enabled governance in Traditional Chinese Medicine (TCM) enterprise clusters remains underexplored, particularly given the unique challenges posed by their traditional practices, regulatory frameworks, and diverse stakeholder dynamics. Addressing this gap, this study investigates how AI-enabled governance can optimize the integration of external enterprises into TCM enterprise clusters across different stages of the lifecycle, thereby contributing to sustainable regional development. This study employs a lifecycle model to analyze the integration of external enterprises into TCM clusters, focusing on factors such as unit product costs, policy incentives, and joint action benefits. By comparing cost dynamics before and after cluster entry, this research maps the lifecycle processes of external enterprises and identifies AI-enabled governance tailored to the adaptation, maturity, and decline stages. The findings reveal that proactive and adaptive AI-enabled governance enhances sustainability by reducing costs, improving efficiency, and fostering collaboration among stakeholders. This study provides a dynamic perspective on cluster integration, extending the theoretical framework of organizational research. It offers policymakers and cluster managers actionable insights for leveraging AI-enabled governance to achieve sustainable regional development, foster innovation (SDG 9), promote economic growth (SDG 8).