A Strategic Management Framework for Sustainable Machine Learning With Eco-AI and Digital Innovation

Abstract

Although machine learning and artificial intelligence (AI) have transformed many industries, concerns have been raised about how long these technologies can continue to exist without endangering the environment due to their rapid development. As the widespread use of AI models for training and deployment results in a significant carbon footprint and high computing resources, concerns about the environmental impact of these models are growing. In order to lessen the environmental impact of machine learning, this chapter introduces Eco-AI, a green computing paradigm that uses sustainable model training methods, optimizes hardware utilization, and integrates energy-saving algorithms. The authors test the paradigm using a variety of machine learning models, including Transformers, Random Forest, and Convolutional Neural Networks (CNN), in both standard and Eco-AI optimized configurations. According to the results, the eco-AI approach cuts energy and carbon emissions in half while improving training efficiency without sacrificing model accuracy. According to the study, by leveraging technologies like GPU partitioning, edge computing, and context-aware model selection, AI innovation can be integrated with global sustainability goals. Eco-AI emphasizes the need for ethical and responsible AI practices and promotes a future in which environmental preservation and technological advancement coexist.