This study investigates the relationship between artificial intelligence (AI) adoption and environmental, social, and governance (ESG) performance among 100 listed Saudi Arabian firms over the period 2015–2024. Drawing on panel data regression techniques, including fixed effects models with Driscoll–Kraay standard errors, pooled OLS with industry and year controls, and dynamic panel estimations using system GMM, the analysis reveals a significant and positive association between AI implementation and overall ESG scores. Disaggregated analysis shows that AI adoption is particularly associated with improvements in the environmental and social dimensions, with a more moderate relationship to governance practices. To address potential issues of cross-sectional dependence and heterogeneity, the study applies the Common Correlated Effects Mean Group (CCEMG) and Mean Group (MG) estimators as robustness checks, which confirm the consistency of the main findings. In addition, the Dumitrescu–Hurlin panel Granger causality test indicates that AI adoption Granger-causes ESG performance—especially in the environmental and social dimensions—while no reverse causality is observed. The results suggest that AI technologies are positively linked to firms’ sustainability strategies and performance, supporting the integration of digital transformation into national and corporate ESG agendas, particularly in emerging markets like Saudi Arabia.
Amina Hamdouni (Wed,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: