DATA-DRIVEN TALENT STRATEGY: NAVIGATING WORKFORCE OPTIMIZATION WITH AI AND ANALYTICS

Abstract [English]

In an era marked by rapid technological disruption, dynamic market conditions, and shifting workforce expectations, organizations are increasingly recognizing the strategic value of data-driven talent management. This paper explores the integration of Artificial Intelligence (AI) and advanced analytics into workforce strategy as a pivotal approach to achieving workforce optimization. While traditional talent strategies have relied heavily on intuition, experience, and retrospective performance reviews, the emergence of AI has introduced a proactive, predictive, and prescriptive dimension to managing human capital. The study delves into how data-driven frameworks can be leveraged to attract, retain, develop, and deploy talent more effectively in alignment with organizational goals. Drawing from contemporary industry practices, empirical data, and cross-sector case studies, this research demonstrates how AI tools such as machine learning algorithms, natural language processing, and sentiment analysis can unearth patterns from vast, unstructured datasets to enhance decision-making. Applications discussed include AI-driven candidate screening, predictive attrition models, employee engagement forecasting, skill gap identification, and real-time performance analytics. Moreover, the paper critically evaluates the ethical, legal, and organizational implications of AI integration in human resources. While the use of algorithms and predictive tools presents tremendous opportunities to reduce bias and improve transparency, it also raises questions about data privacy, algorithmic fairness, and the erosion of human oversight in workforce decisions. Through a balanced approach, the research underscores the importance of implementing responsible AI frameworks that align with regulatory norms and uphold employee trust. Findings suggest that organizations embracing AI and analytics in talent management experience greater agility, improved workforce planning accuracy, and increased return on human capital investments. However, the transformation is not solely technological; it demands a cultural shift within HR functions, necessitating new skillsets, interdisciplinary collaboration, and executive buy-in. The paper concludes by offering a strategic model for implementing data-driven talent strategies that combine AI capability with human insight, emphasizing a phased, scalable, and ethically grounded approach. This research contributes to the growing body of knowledge on strategic workforce management and offers actionable insights for HR leaders, policymakers, and business strategists seeking to future-proof their organizations through intelligent talent practices. In doing so, it positions AI and analytics not as replacements for human judgment but as vital instruments in enhancing the strategic role of HR in building resilient, data-literate, and performance-optimized workforces.