Workforce productivity is a critical determinant of operational efficiency and service quality in U.S. service organizations, particularly in sectors such as banking, healthcare, retail, and customer support. Traditional workforce management approaches rely heavily on historical reporting and manual performance evaluation, which often fail to capture dynamic behavioral patterns and future productivity risks. This paper proposes an AI-driven workforce productivity optimization framework that leverages Key Performance Indicator (KPI) based predictive analytics to enhance decision making and resource allocation. The proposed framework integrates machine learning models with real time operational data to predict employee performance trends, identify productivity bottlenecks, and recommend proactive interventions. By combining supervised learning, time series forecasting, and anomaly detection techniques, the system enables managers to anticipate workforce challenges rather than react to them. Experimental analysis using simulated service sector datasets demonstrates measurable improvements in task completion rates, service response times, and workforce utilization. The findings highlight the potential of AI-enabled analytics to support data driven human capital strategies while maintaining transparency and fairness. This research contributes a scalable, explainable, and KPI aligned approach for sustainable workforce productivity optimization in modern service organizations.
Tahamina Akter (Sun,) studied this question.