Beyond Job Titles: AI and Machine Learning Architectures Enabling Skills-Based Workforce Intelligence in Enterprise Organizations

Abstract

Traditional workforce models built on job titles are struggling to keep pace with the speed of technological change. Organizations that define talent through fixed roles risk losing sight of the actual capabilities their people hold and the dynamic skills their business needs. This article reviews advances in AI and ML frameworks that can enable enterprise organizations to capitalize on skills-based workforce intelligence. This article also reviews empirical studies and applies a number of ML-centric metrics to the state of the art. In particular, the Skills Embedding Similarity Score, Skills Gap Index, Weighted Workforce Readiness Score, Talent Recommendation Precision, Internal Mobility Efficiency Rate, and Skills Coverage Index were applied and described. The metrics were run through three simulated enterprise scenarios to represent small, mid-market and large companies. In the simulated cases, internal mobility rates rise by up to 37 percentage points, talent recommendation accuracy increases to 0.79, and workforce readiness scores rise between 18 and 31 percent over 12 months from implementation. The results revealed that research related to NLP, graph neural networks, and LLMs in HR technology has the potential to improve the precision and effectiveness of talent acquisition, internal mobility, and skills gap reduction efforts. The implications of these findings, including ethical considerations and future directions for AI-based workforce intelligence, are also discussed.