The Impact of AI-Driven Decision-Making on Corporate Environmental Responsibility
Department of Management, Guru Kashi University, Talwandi Sabo, India.
* Corresponding Author
ORCID Details
Fahmida Akand: https://orcid.org/0009-0002-0078-2930
Hemant Kumar Watts: https://orcid.org/0009-0008-8133-4657
Research Article
Comprehensive Research and Reviews in Engineering and Technology, 2026, 03(02), 015–029.
Article DOI: 10.57219/crret.2026.3.2.0023
Publication history:
Received on 04 June 2026; revised on 29 August 2026; accepted on 31 August 2026
Abstract:
Background: The integration of artificial intelligence (AI) into corporate environmental responsibility has emerged as a transformative approach for addressing escalating environmental challenges. Organizations worldwide are increasingly adopting AI-driven systems to optimize environmental performance, yet comprehensive understanding of implementation effectiveness and organizational factors remains limited. This study explores how AI applications impact corporate environmental decision-making and identifies critical barriers to successful implementation.
Methods: A mixed-methods research design was employed, combining systematic literature review of 125 peer-reviewed studies (2020-2025) with quantitative meta-analysis. In the research, effectiveness of AI was considered in four areas of environment monitoring, sustainability reporting, optimizing carbon footprint as well as predictive analogy. The success factors of implementation and the organizational barriers were evaluated statistically with the help of SPSS and R software packages.
Results: The interpretation of the results is that the environmental effect of AI implementation is significant, with an average of 22.3 percent (95 percent confidence interval of 18.7-25.9 percent) reduction in energy use, 19.7 percent (95 percent confidence interval of 16.2-23.2 percent) decreased carbon emissions, and 87.3 percent accuracy (95 percent confidence interval of 84.1-90.5 percent) to accomplish environmental risk prediction. The major implementation obstacles, however, remain to be the paradox of energy consumption of the AI system, interdisciplinary skills gaps, regulatory uncertainty and organizational resistance. Companies that have implemented well-developed environmental strategies based on AI are characterized by 45% more successful performance than their conventional counterparts (p <0.001).
Conclusion: There is a high capacity of AI in improving the environmental responsibility of companies, however, achieving successful application might imply combating the trade-off in the process of energy consumption, interdisciplinary knowledge development, and the provision of favorable regulatory contexts. A strategic and holistic nature involving technology, environmental and business strategies is also necessary to achieve the maximum environmental benefits with minimum negative effects.
Keywords:
Artificial Intelligence, Environmental Management, Corporate Sustainability, Machine Learning, Carbon Footprint, Environmental Decision-Making
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Copyright © 2026 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0