AI-Driven Human Capital Analytics for Enhancing Financial Decision- Making and Workforce Productivity in Modern Commercial Enterprises

Abstract

The use of sophisticated workforce analytics is shaping the future of human resource management (HRM), financial planning, and organizational performance (OP) in the domain of artificial intelligence (AI). The existing research is spread over human capital management, business intelligence, productivity, and financial decision-making, and there is a need for a more holistic perception of these. This review explores the potential of AI-powered human capital analytics in the context of financial decision-making, workforce productivity, and organizational competitiveness in today's commercial enterprises. Relevant literature on the topic of AI adoption,predictive analytics, workforce planning, employee engagement, financial forecasting, operational efficiency, and strategic management was synthesized using a structured narrative approach. The results show that AI systems can help reduce costs, allocate talent, train employees, forecast employee turnover, enhance employee engagement, and perform real-time financial analysis. It can also help in scenario planning, optimizing resources, and quicker decision-making from the manager's side. They are more likely to happen when reliable data, appropriate digital infrastructure, good leadership, and coordination between human resources and finance functions support AI systems. Data protection, algorithmic bias, lack of transparency, lack of causal evidence, and industry-specific adoption are all critical issues. In an open, transparent governance model characterized by human oversight and responsible organizational practices, AI-powered HC analytics can positively affect financial and workforce results.