Exploring The Impact Of GenAI On Talent Management In A Large Hyperscaler Company

This dissertation explores the impact of Generative Artificial Intelligence (GenAI) on talent management within a large hyperscaler technology company. GenAI, a branch of natural language processing (NLP), enables content generation from user prompts, thereby enhancing communication, collaboration, and task execution. Employing a single-case study design, the research integrates thematic and univariate analyses of managerial perspectives across multiple business units. The study is theoretically grounded in the Resource-Based View (RBV) and Systems Theory, offering a dual-lens framework to examine how GenAI integration can create strategic value in talent management processes. The findings indicate that, while GenAI improves individual-level effectiveness, its broader organizational impact depends on strategic alignment with business goals, digital infrastructure, and workforce capability. Four key themes emerged: (1) Legacy Practices and Transitional Tensions, highlighting how historical systems and human dependency shape perceptions of new technologies; (2) Operational Complexity and Process Fragmentation, revealing how disconnected systems and manual processes hinder GenAI integration; (3) Organizational Readiness and Strategic Alignment, emphasizing the role of leadership engagement and coordinated learning in realizing GenAI’s potential; and (4) Lack of Trust and Technological Confidence, underscoring concerns about GenAI maturity, data reliability, and implementation clarity. The study contributes four key insights: (1) a reconfiguration of the traditional five-stage talent management model into a streamlined three-stage framework comprised of Plan, Develop, and Transition, better suited for AI-enabled environments; (2) a strategic emphasis on cultivating a human/AI culture of practice, supported by transparent communication and structured feedback; (3) the identification of trust as a critical adoption factor, necessitating robust change management, system reliability, and ethical safeguards; and (4) a recognition of the disparities in GenAI effectiveness, with recommendations for continuous evaluation and adaptive implementation. By addressing legacy inefficiencies, investing in adaptive infrastructure, and prioritizing human-centered GenAI design, organizations can foster environments conducive to effective human/AI collaboration.