Universities play a key role in university-industry-government interactions and are important in innovation ecosystem studies. Universities are also expected to engage with industries and governments and contribute to economic development. In the age of artificial intelligence (AI), governments have introduced relevant policies regarding the AI-enabled innovation ecosystem in universities. Previous studies have not focused on the provision of a dynamic capabilities perspective on such an ecosystem based on policy analysis. This research work takes China as a case and provides a framework of AI-enabled dynamic capabilities to guide how universities should manage this based on China’s AI policy analysis. Drawing on two main concepts, which are the innovation ecosystem and dynamic capabilities, we analyzed the importance of the AI-enabled innovation ecosystem in universities with governance regulations, shedding light on the theoretical framework that is simultaneously analytical and normative, practical, and policy-relevant. We conducted a text analysis of policy instruments to illustrate the specificities of the AI innovation ecosystem in China’s universities. This allowed us to address the complexity of emerging environments of innovation and draw meaningful conclusions. The results show the broad adoption of AI in a favorable context, where talents and governance are boosting the advance of such an ecosystem in China’s universities.

The central government of China has intensively guided regional integration and policy coordination towards the development of digital governance in the last ten years. The Guangdong-Hong Kong-Macao Greater Bay was one of the most important regions of China expected to accelerate regional development through policy coordination and establishment of digital infrastructures. This article adopted the method of content analysis to explore the policy transitions of digital governance in the Greater Bay including policy contents (in terms of policy objectives and instruments) and policy networks. Based on our empirical analysis, we found that top-down guidance from the central government did not necessarily generate regional coordination. Different governments of the same region could start policy coordination from shared policy objectives and policy instruments and establish innovative governance frameworks to achieve consensus. Therefore, regional coordination could be fulfilled.