Model Construction and Practical Exploration of Intelligent Transformation of Corporate Governance from the Perspective of AI Empowerment

Junnan Fan; Yujun Tang; Chuhuan Cai

doi:10.71222/mbszr155

Authors

Junnan Fan Medical Department, Sichuan Second Traditional Chinese Medicine Hospital, Chengdu, China Author
Yujun Tang Medical Department, Sichuan Second Traditional Chinese Medicine Hospital, Chengdu, China Author
Chuhuan Cai School of Logistics Management and Engineering, Zhuhai College of Science and Technology, Zhuhai, China Author

DOI:

https://doi.org/10.71222/mbszr155

Keywords:

artificial intelligence, corporate governance, digital transformation, organizational strategy, business competitiveness, management innovation

Abstract

With the rapid development and widespread application of artificial intelligence (AI), corporate governance is facing unprecedented opportunities and challenges, and intelligent transformation has become a key pathway for enhancing corporate competitiveness and resilience. This paper examines the core issues of the intelligent transformation of corporate governance from the perspective of AI empowerment. First, it clarifies the research background and significance, reviews the current state of domestic and international research, and outlines the research methods and main innovations adopted in this study. Second, it analyzes the theoretical foundations of AI and intelligent corporate governance, including the core concepts, functional scope, and development trends of AI, as well as governance theories related to decision-making, risk control, and organizational coordination. On this basis, the paper constructs a conceptual model in which AI empowers the intelligent transformation of corporate governance, detailing the mechanisms through which AI supports information processing, monitoring, strategic analysis, and performance evaluation. Through practical case analysis, the study extracts typical patterns and experience in implementing AI-driven governance transformation. Furthermore, it identifies key challenges such as data security, algorithmic bias, organizational inertia, and regulatory constraints, and proposes corresponding governance and management strategies. Finally, the paper summarizes the main findings and limitations, and puts forward future research directions, aiming to provide both theoretical reference and practical guidance for enterprises seeking to achieve intelligent governance transformation.

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