Integrating AI With ESG Goals: A Framework For Sustainable Corporate Governance

Abstract

The global corporate landscape is undergoing a major transformation as businesses seek to align profit-making objectives with long-term sustainability commitments. Environmental, Social, and Governance (ESG) criteria have become critical indicators for investors, regulators, and stakeholders to evaluate corporate responsibility and resilience. However, conventional ESG reporting frameworks often struggle with challenges such as fragmented data sources, inconsistent metrics, and lack of predictive capability. This research introduces a comprehensive framework that integrates Artificial Intelligence (AI) into ESG implementation to strengthen corporate governance. The framework combines advanced Natural Language Processing (NLP) for automated extraction of ESG-related data, Machine Learning (ML) algorithms for performance quantification, and predictive analytics for forecasting compliance risks and resource optimization. The proposed system enables real-time tracking of ESG indicators, improves transparency, and supports evidence-based decision-making. A multi-year dataset from diverse industries is used to evaluate the approach, showing significant improvements in reporting accuracy, risk mitigation, and operational efficiency compared to traditional methods. The results demonstrate that AI-assisted ESG governance can minimize compliance gaps, boost stakeholder confidence, and create measurable long-term value. This work contributes to sustainable corporate governance research by presenting a scalable, data-driven approach that bridges the gap between technology adoption and ESG goal achievement.