Article
From Algorithms to Talent Outcomes: How AI-Enabled HR Analytics Enhances Decision Quality, HR Efficiency and Talent Management under Human Oversight.
The rapid diffusion of artificial intelligence (AI) into human resource management has expanded organizational capacity to analyse workforce data, automate information-intensive processes and generate predictive insights. However, the organizational value of AI-enabled HR analytics cannot be inferred from technological adoption alone. An important unresolved question is whether AI translates into superior talent management outcomes directly or through improvements in the quality and efficiency of HR decision processes, and whether such benefits depend on responsible governance and meaningful human oversight. Drawing on the Resource-Based View and Socio-Technical Systems Theory, this study develops and tests an integrated model linking AI-enabled HR analytics (AIHRA), HR decision quality (DQ), HR process efficiency (HPE), talent management outcomes (TMO), and AI governance and human oversight (AIGHO).
A quantitative cross-sectional study was conducted using survey responses from 400 HR professionals and managers working in organizations using AI-enabled or advanced HR analytics applications in India. The proposed relationships were assessed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The measurement model demonstrated satisfactory reliability and convergent and discriminant validity. The structural results reveal that AI-enabled HR analytics significantly improves HR decision quality (β = 0.414, p < 0.001), HR process efficiency (β = 0.502, p < 0.001), and talent management outcomes (β = 0.231, p < 0.001). Both HR decision quality (β = 0.298, p < 0.001) and HR process efficiency (β = 0.320, p < 0.001) significantly enhance talent management outcomes. Furthermore, decision quality (indirect effect = 0.132, 95% CI [0.091, 0.179]) and process efficiency (indirect effect = 0.160, 95% CI [0.111, 0.213]) significantly mediate the AIHRA–talent management relationship. AI governance and human oversight additionally strengthen the positive relationship between AI-enabled HR analytics and decision quality (β = 0.116, p = 0.009). The model explains 43.6% of the variance in talent management outcomes.
The study contributes to AI-HRM scholarship by demonstrating that the strategic value of AI-enabled HR analytics operates through identifiable organizational mechanisms rather than through technological adoption alone. It further establishes responsible AI governance and human oversight as complementary organizational capabilities that strengthen the decision value of algorithmic analytics. The findings provide implications for organizations seeking to combine analytical sophistication, HR efficiency and human judgement in responsible AI-enabled talent management.