The Convergence of Artificial Intelligence and Sustainability Reporting: A Systematic Review of Applications, Challenges and Future Directions

Citation

Mustafa F, Smolarski J & Elamer A (2025) The Convergence of Artificial Intelligence and Sustainability Reporting: A Systematic Review of Applications, Challenges and Future Directions. Business Strategy and the Environment, 34 (8), pp. 9761-9784. https://doi.org/10.1002/bse.70090

Abstract
This research examines the potential of artificial intelligence (AI) to improve sustainability reporting, particularly in relation to environmental, social and governance (ESG) issues. Despite growing interest in the field, the integration of AI in sustainability remains underexplored, especially in terms of its impact on data accuracy, transparency and sustainability reporting effectiveness. This study conducts a systematic literature review (SLR) of 135 peer-reviewed articles to identify significant research gaps and presents a comprehensive framework that integrates AI technologies, such as machine learning, Industry 4.0 innovations and decision support systems (DSS), with sustainability reporting practices. The findings support the need for stronger theoretical and practical frameworks to effectively leverage AI's capabilities in sustainability reporting. The originality of this study is found in its innovative approach to connecting AI technologies with sustainability reporting, a field characterised by fragmentation and underdevelopment in research. This study introduces a broad framework and takes a critical look at the unintended externalities of AI, such as increased inequality and environmental costs. It does this by challenging existing sustainability frameworks, like the GRI and SASB, to change with the times and keep up with new technologies. The emphasis on both the advantages and possible drawbacks of AI in sustainability reporting substantiates the study's publication, providing fresh insights into AI's role in enhancing ethical, transparent and effective ESG disclosures. The study offers recommendations for managers and policymakers aimed at improving the accuracy, transparency and credibility of ESG disclosures via AI-driven solutions, thereby promoting more effective sustainability practices. This paper provides a framework for future research and practical application of AI in sustainability reporting, with the goal of enhancing academic knowledge and real-world practices in the pursuit of sustainable development.

Keywords
artificial intelligence; decision support systems; enivronmental impact; innovation; machine learning; sustainability reporting

Journal
Business Strategy and the Environment: Volume 34, Issue 8

• Published Version