AI-Powered Workforce Analytics Forecasting Labor Market Trends and Skill Gaps for U.S. Economic Competitiveness

Abstract

This paper discusses how AI-enabled analytics are used to detect emerging relative skill shortages, track labor market patterns. It improves the competitiveness of the economy in the United States. The intense use of Artificial Intelligence in workforce analytics has revolutionized how governments and industries forecast the labor market needs the study throws light on the role of real-time data variables and prediction modelling in making workforce development meet changing industry demands. The research project has adopted quantitative research design. A systematic questionnaire was sent to a sampling of 300 participants comprising HR analysts, labor economists and policymakers in different industries of the U.S. Variables that were measured included the AI Integration Level, Labor Market Responsiveness, Real-Time Data Utilization, Predictive Accuracy and the dependent variable, Economic Competitiveness. The findings showed that there were significant correlations among AI integration (r = 0.71, p < 0.01), predictive accuracy (r = 0.68, p < 0.01), and economic competitiveness, which are significant. Regression outcome showed that AIL and PA were the most powerful determinants of EC (R² = 0.61). AI-based analytics in the establishment would promote not only labor market predictions but also boost the strategic position of the U.S. in the global economy. The government and business in scaling the use of AI. It is ensuring the training programs reflect the areas of skill shortage and fostering the development of data infrastructure.