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arXiv · 2607.19733

AI-Increased Talent Retention Strategies: Fostering Long-Term Employee Engagement and Development in Talent Management

Jay Barach
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Abstract

The integration of AI in Talent Management is a change in the way that organizations are designing their strategies for Talent Retention (TR), engagement, and future strategy. New and innovative tools such as predictive models, sentiment analysis, and personalized career planning have come up, and they offer better ways of addressing retention issues, workforce engagement, and, in general, sustainability. Through the application of predictive analytics, organizations can determine employees' likelihood of leaving the organization, who is likely to leave, and when to act, thus minimizing the costs and time associated with the recruitment process and improving performance. Furthermore, AI solutions help the development of individualized learning plans that help to define employees' professional goals and link them with the organization's strategy to encourage the employees' continuous growth. This paper explained how AI is impacting the retention process and how it can be used to decrease attrition rates, create a loyal workforce, and promote sustainable management of human and other resources. Furthermore, the author discusses the ethical issues, such as privacy and fairness of algorithms, that are involved in the implementation of AI systems. Thus, the above challenges can be solved by developing a sustainable and inclusive ecosystem that can help in the development of the future workforce. Based on a systematic review of literature, the study presents a framework that can help organizations improve their talent management practices with the help of AI to support long-term sustainable growth. It can be used to help industry professionals and decision-makers understand the new technological shifts that are occurring.

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BibTeXRIS

Jay Barach. 2026-07-22. AI-Increased Talent Retention Strategies: Fostering Long-Term Employee Engagement and Development in Talent Management. https://arxiv.org/abs/2607.19733

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