AI and ESG Performance : An Empirical Study of the High-Tech Sector

Purpose : This study aimed to identify and prioritize the benefits of AI integration through such technologies as machine learning, natural language processing, and robotic process automation for improving environmental, social, and governance (ESG) performance in the high-tech sector. Methodology : The items (benefits) were generated through a comprehensive literature review and qualitative interviews with industry practitioners involved in AI integration for ESG. Qualitative thematic analysis categorized the items into relevant themes. Subsequently, survey data were collected from 170 respondents, including business heads, managers, AI/ESG consultants, and specialists. Exploratory factor analysis (EFA) was employed to identify the factors representing AI’s benefits for ESG. Furthermore, Pareto analysis identified the factors that significantly impacted the ESG parameters due to AI integration. Findings : EFA resulted in a six-factor structure representing the benefits of AI: proactive governance (PG), environmental preservation (EP), risk management (RM), data management (DM), operational optimization (OO), and stakeholder engagement (SE). Pareto analysis indicated that PG, EP, and RM represented the most impactful areas. Practical Implications : The study offered empirical evidence for improving the high-tech sector’s ESG performance. It guided industry practitioners to leverage AI for better positioning in ever-evolving competitive markets strategically. The validated framework will enable decision-makers to focus their investments in areas with the highest impact. Originality : This research empirically validated the benefits of linking artificial intelligence to sustainability performance in the high-tech sector, contributing to academic literature and practical implementation.