AI Driven Talent Management and the Performance of Agri Based Enterprises in Central Region Cameroon

Purpose: This study examines the relationship between artificial intelligence (AI) driven talent management practices and the performance of agri based enterprises in the Central Region of Cameroon, focusing on the mediating roles of workforce productivity and employee resilience. Research Methodology: A quantitative cross sectional survey design was employed, targeting 250 managers and human resource professionals. Data were collected with a structured questionnaire and analyzed with SPSS and AMOS through descriptive statistics, Pearson correlation, and mediation analysis with 5.000 bootstrap resamples. Results: AI driven talent management was positively and significantly related to enterprise performance (r=0.523, p<0.01). Workforce productivity (β=0.18) and employee resilience (β=0.14) significantly mediated this relationship. Both the total indirect effect (β=0.32, p<0.01) and the direct effect (β=0.29, p<0.01) were significant, indicating partial mediation, with about 52 percent of the total effect transmitted through the two mediators. Conclusions: AI driven talent management enhances the performance of agri based enterprises both directly and indirectly through improved workforce productivity and stronger employee resilience. Enterprises should integrate AI strategically into talent management and support employees through change management. Limitations: The cross sectional design, self reported data, purposive sampling, and focus on a single region limit causal inference and generalizability. Contributions: The study provides empirical evidence from an underresearched African agricultural context and clarifies the mechanisms through which AI driven talent management translates into enterprise performance.