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Cited by 51Corporate Social Responsibility ReportingImpact of AI and Big Data on Business and SocietyFinancial Reporting and XBRL

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

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Mustafa, Fairouz, Smolarski, Jan, Elamer, Ahmed A.·2025·Business Strategy and the Environment
DOI 10.1002/bse.70090
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Abstract

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.

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95 of 95 references verified0 citation errors · 0 not checkable

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  • Sustainable decision-making supported by AI is highly dependent on access to reliable, high-quality, and interoperable data across environmental, social, and economic dimensions.

    “Sustainable decision-making supported by AI is highly dependent on access to reliable, high-quality, and interoperable data across environmental, social, and economic dimensions.”

    From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [57]

    Section 5.3 (Decision Support Systems; page unavailable) says AI-integrated systems consolidate fragmented datasets to support informed sustainability decisions. The Introduction discusses inconsistent reporting across economic, environmental, and social dimensions. The paper does not establish that AI-supported decisions are highly dependent on access to reliable, high-quality, interoperable data across all three dimensions. (LLM, conf=0.82)

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  • In practice, many organizations face fragmented data infrastructures and inconsistent sustainability metrics, which may bias AI-supported decisions or reduce their strategic relevance.

    “In practice, many organizations face fragmented data infrastructures and inconsistent sustainability metrics, which may bias AI-supported decisions or reduce their strategic relevance.”

    From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [57]

    The Introduction describes businesses struggling with fragmented and inconsistent sustainability reporting, and §5.3 discusses fragmented data and the potential for biased decisions from decision-support systems (page numbers are not available in our copy). The paper does not establish that fragmented data or inconsistent metrics cause that bias, nor does it say they reduce the strategic relevance of AI-supported decisions. (LLM, conf=0.82)

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Research works printed but not linked: 2

  1. de Villiers, C., S. Kuruppu, and D. Dissanayake. 2021. "A (New) Role Forbusiness-Promoting the United Nations' Sustainable Developmentgoals Through the Internet-of-Things and Blockchain Technology." Journal of Business Research 131: 598-609. https:// doi. org/ 10. 1016/j. jbusr es. 2020. 11. 0661164 · DOI
  2. Nassar, A., and M. Kamal. 2021. "Ethical Dilemmas in AI-Powered Decision-Making: A Deep Dive Into Big Data-Driven Ethical Considerations." International Journal of Responsible Artificial Intelligence 11, no. 8: 1-11.

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Authors
Mustafa, F., Smolarski, J., & Elamer, A. A.
Affiliations
  • University of Stirling (GB)
  • Alfaisal University (SA)
Corresponding author
Elamer, Ahmed A.
ORCID
Venue
Business Strategy and the Environment
Publisher
Wiley
Published
2025-07-24
Volume
34
Issue
8
Pages
9761-9784
ISSN
  • 0964-4733 (ISSN-L)
  • 1099-0836
DOI
10.1002/bse.70090
OpenAlex
W4412926801
Cited by
51
References
95
Citations by year
2026: 47 · 2025: 4
FWCI
13.34
Type
journal article
Language
en
Open access
Licence
Locations
Business Strategy and the Environment (publishedVersion, cc-by)
Topics
  • Social Sciences › Business, Management and Accounting › Strategy and Management › Corporate Social Responsibility Reporting
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Financial Reporting and XBRL
Keywords
artificial intelligence, sustainability reporting, ESG disclosure, machine learning, Industry 4.0, decision support system, GRI framework, SASB, disclosure transparency, data accuracy, sustainable development, research framework

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