The role of AI and Generative AI in advancing sustainability accounting and ESG assurance: a systematic literature review

Originality: This study offers one of the first systematic overviews of how AI and Gen AI are shaping sustainability accounting and ESG assurance. It brings together a previously fragmented body of research and maps the intellectual landscape of this emerging field. By identifying key gaps and trends, the study provides a structured agenda to support future academic research and guide practical innovation in the responsible use of AI for ESG governance.

Purpose: This study explores how Artificial Intelligence (AI), and Generative AI (Gen AI) are transforming Environmental, Social, and Governance (ESG) reporting and sustainability assurance. It examines how organizations use these technologies to improve data accuracy and increase reporting efficiency. It also explains ongoing concerns around potential risks. Design and methodology approach: This research conducted a systematic literature review (SLR) using the SPAR-4-SLR protocol and based on the TCCM framework (Theories, Contexts, Characteristics, and Methods) to ensure a structured and transparent process. A total of 67 peer-reviewed journal articles were retrieved from the Web of Science database. Bibliometric analysis was also conducted to identify influential authors, journals, and thematic trends. Findings: The review of 67 peer-reviewed studies from 39 journals reveals that AI and Gen AI are increasingly leveraged to improve ESG reporting. There are various tools in this transformation including machine learning, natural language processing, and large language models. Most of the reviewed studies rely on quantitative methods, and just a few numbers of studies use qualitative or mixed methods. On the theoretical side, many papers lack a strong conceptual foundation, and only a few studies explicitly engage with well-established frameworks. Contextually, literature tends to focus on organizational-level analysis, and it leads to ignoring individual and market level analysis. In addition, most studies have been conducted in advanced countries. These trends suggest that future research should place greater emphasis on theory development, diversify its methodological approaches, and explore a wider range of geographical and analytical contexts. Originality: This study offers one of the first systematic overviews of how AI and Gen AI are shaping sustainability accounting and ESG assurance. It brings together a previously fragmented body of research and maps the intellectual landscape of this emerging field. By identifying key gaps and trends, the study provides a structured agenda to support future academic research and guide practical innovation in the responsible use of AI for ESG governance.