Against the backdrop of the advancement of global sustainable development strategies and the upgrading of corporate ESG (Environmental, Social, and Governance) governance needs, AI technology, leveraging its core capabilities such as big data processing, machine learning, and IoT collaboration, has become a key enabling tool to address issues in corporate ESG governance, including data fragmentation, inaccurate evaluation, and low decision-making efficiency. This study employs literature research, case study, and data analysis methods to systematically sort out the typical application scenarios of AI technology in corporate ESG governance, analyze its value creation mechanism, and examine the practical dilemmas arising from the implementation of the “AI-ESG” integration. The research finds that AI can penetrate multiple scenarios in the environmental, social, and governance dimensions, and realize value creation through four dimensions: improving ESG data quality, enhancing analysis accuracy, optimizing decision-making efficiency, and reducing governance costs. However, during the integration process, it also faces dilemmas such as insufficient technological adaptability, data security risks, algorithmic ethical biases, and talent shortages. Based on this, the study proposes four measures: constructing an “AI-ESG” collaborative system, improving the data governance mechanism, cultivating interdisciplinary talents, and strengthening policy supervision and guidance. This study aims to enrich the theoretical system of interdisciplinary research on AI and corporate ESG governance, provide practical guidance for the digital transformation of corporate ESG, and contribute to the achievement of sustainable and green development goals.
Ge et al. (Thu,) studied this question.
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