AI-Based Workforce Skill Optimization System

The rapid advances in technology have radically changed the skills that organizations seek in their employees, thereby making the task of matching the skills of employees with the changing job requirements increasingly difficult. The conventional methods of workforce management systems involve the definition of fixed roles and manual evaluation of the skills possessed by the workers. This makes it difficult to perform the evaluation of skills dynamically and workforce planning. In this paper, we propose to use an AI-Based Workforce Skill Optimization System that will employ AI, ML and NLP for extracting the skills, performing dynamic skill gap analysis, and intelligently matching roles. The system will employ transformer-based semantic matching techniques for assessing the match between the employee skills and role requirements. Based on an analysis of multi-source data about employees such as resume, performance metrics, project history, etc., the system will perform an assessment of the skill set and generate rank scores of the employees according to their best fit. Predictive analytics will be used for assessing future skill requirements, while Explainable AI techniques will help increase transparency of decision making.