Artificial Intelligence–Driven ESG Strategy: Predictive Analytics for Sustainable Value Creation: A Narrative Review

Abstract

This narrative review examines the intersection of artificial intelligence, environmental, social and governance (ESG) strategies and sustainable value creation in contemporary business environments. As organisations face mounting pressure to demonstrate environmental stewardship, social responsibility and governance excellence, artificial intelligence and predictive analytics have emerged as transformative tools for enhancing ESG performance and generating measurable business value. This paper synthesises current literature on AI‐enabled ESG strategies, exploring how machine learning, predictive analytics, big data and other digital technologies enable organisations to optimise resource allocation, anticipate sustainability risks and make data‐driven decisions that align environmental and social objectives with financial performance. Drawing on stakeholder theory, resource‐based view and dynamic capabilities perspectives, we analyse the mechanisms through which AI‐driven ESG initiatives create value, including improved operational efficiency, enhanced transparency, reduced capital costs and strengthened competitive positioning. The review identifies key themes, including the role of AI in ESG data collection and reporting, predictive risk management, strategic decision optimisation and performance measurement. We also examine challenges organisations face in implementing AI‐powered ESG strategies, including data quality issues, technological barriers, organisational capabilities and financial constraints. The review also critically examines the risks associated with AI‐enabled ESG systems, including impacts on AI infrastructure, data privacy, social inequality and the digital divide. The findings suggest that while AI adoption and related digital capabilities are increasingly associated with improved ESG performance and contribute to long‐term value creation, the relationship is moderated by factors such as firm size, industry context, digital maturity and institutional environment. This review concludes by proposing future research directions that naturally emerge from the analysis, emphasising the need for causal investigations, contextual studies and the examination of societal impacts. Practical implications for managers and policymakers are discussed.