Transforming Business Models and Economic Performance: The Role of Machine Learning in the United States

Abstract

Machine learning is a disruptive force in changing the kinds of business models and economic performance in the United States. This study aims to research the broad spread of ML’s impact on industries in terms of productivity, the possibility of improving technological disparity by means of ML, and the competitive edges it can provide. Using the analysis of industry case studies, statistical trends, and policy frameworks, the study indicates that ML-driven strategies will account for a substantial part of global GDP growth, with a projected increase of $15.7 trillion by the year 2030. In the U.S., specific applications have shown a 31% reduction in operational costs in manufacturing and retail, and up to a 37% increase in labor productivity. While there is a rapid adoption of ML, fast adoption means that there is displacement of the workforce, algorithm bias, and privacy concerns. Based on that, I stress the necessity of proactive policy interventions to promote the upskilling of the workforce, ethical AI governance frameworks, as well as public-private partnerships to ensure equitable benefits distribution. This research synthesizes empirical data and actionable insights to provide a comprehensive understanding of the role of ML in enabling long-term economic resilience and innovation in the U.S.