Artificial Intelligence-Driven Talent Intelligence and Employee Retention: A Theoretical Framework of Personalised Career Development and Internal Career Mobility

Abstract

The invention of new technologies allows organisations to better analyse their employees, be aware of their future requirements, make learning programs more personal and create instruments for identifying career possibilities within organisations. Although it is evident that AI contributes much to employee retention, its effectiveness is determined by how data analysis results are used when forming the career of a worker. This paper provides a theoretical model demonstrating how talent intelligence based on AI might influence the level of employee retention through personalisation of the career development process and provision of career opportunities to workers.

The model uses concepts of Human Capital Theory, Social Exchange Theory, Job Embeddedness Theory and Resource-Based View Theory to interpret talent intelligence as a capability of the organisation to connect employee development with workforce information and deployment. Personalisation of the development process acts as a way of building the capability and internal mobility does this job of being a realisation of opportunities. The concept of perceived fairness of career opportunity is regarded as a contextual factor influencing whether employees find AI-powered career systems fair and trustworthy. This study elaborates on the career-transformational approach, which is based on the premise that talent intelligence proves to be effective in retaining employees only in the case where analytical information leads to meaningful training, training results in available internal job openings, and employees believe that the entire process is fair. The model encompasses technology, career development, internal labour markets, and employee retention and provides recommendations for management and research perspectives.