The convergence of Artificial Intelligence (AI) and corporate sustainability represents a paradigm shift in modern business strategy. This paper explores how AI technologies—including machine learning, predictive analytics, and IoT integration—facilitate environmental, social, and economic sustainability. While AI offers unprecedented opportunities for resource optimization and waste reduction, it also introduces significant challenges, such as high energy consumption for model training and ethical dilemmas regarding data privacy. Through a qualitative synthesis of current literature, this study identifies the dual role of AI as both a solution for and a potential burden to global sustainability goals. The integration of Artificial Intelligence (AI) into corporate frameworks has evolved from a tool for operational efficiency to a cornerstone of Environmental, Social, and Governance (ESG) strategies. This paper investigates the dual-natured impact of AI on business sustainability. It highlights how predictive analytics and machine learning optimize resource allocation and carbon footprint tracking while addressing the critical challenges of "black box" algorithms and the high energy requirements of data centers. The findings suggest that while AI provides the "brain" for sustainable transition, success depends on transparent governance and energy-efficient computational practices.
Miss. Samiksha Dileep Mhatre (Fri,) studied this question.