AI-Driven Transformation of Environmental, Social, and Governance (ESG): A Systematic Review, Gap Analysis, and Future Research Agenda

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:The review synthesizes existing AI-ESG literature and outlines a clear research agenda, making it a useful reference for scholars in sustainability and AI governance.

🏢実務担当者:The paper identifies practical barriers to AI adoption in ESG reporting and suggests paths toward more reliable AI systems, relevant for corporate sustainability teams evaluating AI tools.

🏛政策担当者:The analysis of regulatory fragmentation and data standardization gaps offers insights for policymakers working on harmonizing ESG disclosure requirements.

Environmental, Social, and Governance (ESG) considerations have become a central framework for corporate accountability, regulatory compliance, and sustainable value creation amid growing environmental challenges, stakeholder expectations, and institutional pressures. At the same time, advances in Artificial Intelligence (AI) are transforming ESG systems by enabling large-scale data integration, predictive sustainability analytics, automated reporting, and real-time governance intelligence. These developments are accelerating the digitalization of sustainability management and reshaping how organizations measure, disclose, and govern ESG performance. This study conducts a systematic literature review to synthesize and critically evaluate the emerging body of research on AI-driven ESG transformation. Adopting a socio-technical governance perspective, the review conceptualizes AI-enabled ESG integration as a transformative process in which algorithmic systems increasingly influence sustainability assessment, disclosure, monitoring, and decision-making across organizational and institutional contexts. The findings reveal that despite significant technological progress, several barriers continue to constrain the effective application of AI in ESG. These include fragmented and inconsistent ESG data infrastructures, algorithmic bias and opacity, regulatory fragmentation, weak explainability and auditability mechanisms, and unresolved ethical, social, and environmental risks. The review also identifies important gaps related to cross-country regulatory evidence, lifecycle assessments of AI technologies, stakeholder trust, and mechanisms for enhancing ESG disclosure credibility while mitigating greenwashing and machine-washing risks. Based on these findings, the study proposes a future research agenda emphasizing ESG data standardization, trustworthy and explainable AI, regulatory harmonization, governance integration, and interdisciplinary approaches to understanding the broader sustainability implications of AI-enabled ESG systems.

• semanticscholar https://doi.org/10.55578/jift.2606.007first seen 2026-06-26 05:37:10