Business Intelligence Analytics for Workforce Productivity in the U.S.: Trends, Determinants, and Evidence from National Labor Market Data (2010–2025)

Workforce productivity is a strategic determinant of organizational competitiveness in the digital economy, with Business Intelligence (BI), workforce analytics, and artificial intelligence (AI) increasingly shaping evidence-based decision-making. This study systematically reviews the evolution of U.S. workforce productivity from 2010 to 2025 by synthesizing evidence from nationally representative labor market datasets and contemporary BI literature. Guided by Human Capital Theory and the Resource-Based View, the review draws on the U.S. Bureau of Labor Statistics (BLS) Labor Productivity Program, Current Population Survey (CPS), American Community Survey (ACS), Occupational Information Network (O*NET), Occupational Employment and Wage Statistics (OEWS), National Compensation Survey (NCS), Job Openings and Labor Turnover Survey (JOLTS), and peer-reviewed studies. Evidence was organized around five themes: productivity trends, industry differences, remote work, educational attainment, and BI and workforce analytics. Findings indicate that U.S. labor productivity improved overall but varied substantially across industries. Knowledge-intensive sectors consistently demonstrated higher productivity, supported by digital technologies, AI, and analytics-driven management. Educational attainment was positively associated with productivity, while remote and hybrid work effectiveness depended on organizational digital maturity and management capability. BI has evolved into a strategic capability supporting workforce planning, performance monitoring, predictive decision-making, and operational efficiency. The study concludes that sustainable productivity growth requires integrated investment in human capital, digital transformation, and analytical capabilities, offering implications for managers, policymakers, and researchers.

Business Management

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