AI-Enhanced Labor Market Analytics to Predict Workforce Shifts and Support Policy Decisions in the U.S. Economy

Abstract

The fast-moving process of artificial intelligence (AI) and automation technologies being integrated is transforming the organization of the U.S. labor market, and new difficulties of anticipating shifts in the workforce and developing corresponding policies are being noticed. The study uses labor market analytics, which are enhanced with AI, to predict occupational changes and automation vulnerability in a data-intensive manner. This study is based on the Kaggle dataset Occupation, Salary and Likelihood of Automation, which is based on employment statistics in the United States and the model of automation probabilities through the model of Frey and Osborne (2017). The analysis determines the essential variables that affect job vulnerability with the help of sophisticated machine learning models, including the Random Forest Regression and Artificial Neural Networks, which can be discussed as salary range, industry sector, and geographic distribution. The predictive models will be trained to predict the risk of workforce displacement and the possible regional effects of automation in the U.S. states. Findings show that repetitive or routine jobs have high automation potential especially in manufacturing, retail, and administration fields, whereas jobs with high levels of knowledge and technologies are found to resist. Moreover, one of the policies suggested in the study involves relying on predictive analytics and interventions to workforce development to create a policy-support framework that can help policymakers focus on reskilling initiatives and educational investments in high-risk areas. The results highlight how AI-based insights can be used to reinforce the labor policy-making process, economic resiliency, and national workforce preparedness for technological change. To sum up, the study will be useful in ensuring sustainable governance that aims to bring intelligence in the labor market to meet adaptive, data-driven future work policy in the U.S. economy.