AI-DRIVEN EXTRACTION OF ESG METRICS FROM UNSTRUCTURED DATA: IMPLICATIONS FOR ASSURANCE AND INVESTOR CONFIDENCE

Abstract

Investors increasingly demand transparent, data-driven ESG disclosures, but traditional reporting frameworks (e.g. GRI, SASB) rely on manual, qualitative processes that are slow and inconsistent. Artificial intelligence – especially natural language processing (NLP) and computer vision – promises to augment ESG measurement by automatically extracting metrics from unstructured sources like corporate reports and satellite imagery. This study uses a systematic literature review and case studies (Morningstar Sustainalytics and Truvalue Labs) to evaluate how such AI tools work and how reliable they are. We assess model performance (precision and recall) in text and image analysis, compare AIderived metrics with conventional scores, and sketch how auditors could integrate AI outputs into assurance workflows. Our findings suggest that while modern AI models substantially improve data coverage and timeliness, issues of explainability, bias, and auditability remain.