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Enhancing Business Intelligence Through AI-Driven Integration of Sustainability Metrics via ESG Factors
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Author(s): Shanmuga Pria (University of Technology and Applied Sciencce, Oman), Iman Al Rubaie (University of Technology and Applied Sciencce, Oman)and Venkatavara Prasad (University of Technology and Applied Sciencce, Oman)
Copyright: 2024
Pages: 33
Source title:
Risks and Challenges of AI-Driven Finance: Bias, Ethics, and Security
Source Author(s)/Editor(s): Siraj Kariyilaparambu Kunjumuhammed (Modern College of Business and Science, Oman), Hisham Madi (Modern College of Business and Science, Oman)and Mahmoud Abouraia (Modern College of Business and Science, Oman)
DOI: 10.4018/979-8-3693-2185-0.ch004
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Abstract
In recent years, the imperative for businesses to integrate Environmental, Social, and Governance (ESG) factors into their decision-making processes has become increasingly evident, reflecting a broader societal shift towards sustainable practices. This transition is driven by a recognition of the interconnectedness between business operations and environmental and social impacts, to create long-term value for all stakeholders. The framework underpinning AI-driven integration elucidates how machine learning algorithms and natural language p To address these challenges, the framework offers recommendations for policymakers and regulatory bodies to promote the adoption of AI-driven integration for ESG factors. By fostering an enabling environment that incentivizes sustainability-oriented decision-making, policymakers can accelerate the transition towards a more sustainable and resilient economy. By embracing AI technologies, organizations can navigate the complexity of ESG factors
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