Abstract
The growing integration of artificial intelligence (AI) into corporate ecosystems has reshaped the Environmental, Social, and Governance (ESG) landscape. While AI offers powerful tools for sustainability optimization, social equity advancements, and governance automation, it also introduces significant challenges such as high energy consumption, bias in algorithmic decision-making, and regulatory uncertainties. Existing research often isolates these aspects rather than examining AI’s role within an interconnected ESG framework. This paper presents a conceptual framework—the AI-ESG Integration Model—for systematically analyzing AI’s dual impact on ESG initiatives. The model categorizes AI’s influence into three pillars: 1) AI’s role in environmental sustainability (E), addressing energy-intensive AI infrastructure and resource management; 2) AI’s contribution to social responsibility (S), particularly in bias mitigation, workforce impact, and ethical AI deployment; and 3) AI’s governance implications (G), emphasizing compliance, transparency, and regulatory oversight. By mapping AI’s impact onto this structured framework, this study provides ESG practitioners, policymakers, and AI developers with a strategic foundation for aligning AI integration with sustainability goals. Rather than offering broad generalizations, this paper delivers targeted insights and actionable recommendations for AI-driven ESG strategies. ESG professionals can leverage this framework to refine sustainability reporting, implement ethical AI policies, and navigate the complexities of AI governance. By synthesizing contemporary research and industry applications, this study underscores the necessity of a sector-specific, responsibility-driven approach to harness AI’s potential while mitigating its risks.
Recommended Citation
Domingo, Mario Marlito R.
(2025)
"The Impact of Artificial Intelligence on ESG: A Conceptual Framework for Practitioners and Policymakers,"
Journal of Management for Global Sustainability: Vol. 13:
Iss.
1, Article 2.
DOI: https://doi.org/10.13185/2244-6893.1269
Available at:
https://archium.ateneo.edu/jmgs/vol13/iss1/2
