Artificial Intelligence in Talent Development for Proactive Retention Strategies

Abstract

In today's competitive business environment, effective talent management is crucial for organizational success. Traditional approaches to talent retention and development often fail to address the complexities of modern workforce dynamics, leading to high employee turnover and disengagement. This study explores the integration of Artificial Intelligence (AI) into talent retention and development strategies, focusing on predictive analytics, personalized learning, sentiment analysis, and engagement tools. AI-driven models, such as logistic regression, clustering algorithms, and sentiment analysis, are employed to assess turnover risk, design customized development programs, and enhance employee engagement. Simulated data from 1,000 employees is used to demonstrate how these models can identify at-risk employees, reduce turnover, and improve engagement. The results indicate that AI-driven talent management strategies significantly improve retention, particularly in high-turnover departments, and create more personalized development opportunities for employees. The study also discusses the limitations of relying on simulated data and calls for future research to validate these findings in real-world settings. This paper provides organizations with actionable insights into how AI can be implemented to optimize workforce management and foster long-term employee retention and development.