AI Powered Recruitment Tools and Operational Efficiency in Agri Based SMEs in Central Region, Cameroon

Purpose: This study examines the impact of AI powered recruitment tools on the operational efficiency of agri based small and medium enterprises (SMEs) in the Central Region of Cameroon. It investigates how digital recruitment technologies influence hiring timelines, cost per hire, and workforce productivity, and whether organizational size and digital infrastructure moderate these effects. 
Research Methodology: A quantitative survey design was employed, collecting data from 312 managers and human resource personnel in agri based SMEs in Yaoundé and surrounding areas. Structured questionnaires were administered using purposive and stratified sampling. Data were analyzed with descriptive statistics, correlation analysis, and hierarchical regression in SPSS version 27. 
Results: AI powered recruitment tools significantly enhanced operational efficiency (β=0.341, p<0.001). Among enterprises with high adoption, cost per hire fell by 28.4 percent, time to hire decreased by 41.2 percent, and workforce productivity improved by 19.7 percent. Organizational size and digital infrastructure moderated these relationships, individually and jointly. 
Conclusions: AI powered recruitment tools substantially improve operational efficiency in agri based SMEs, although the benefits depend on organizational readiness and infrastructure. Strategic investment in digital recruitment capabilities yields measurable efficiency gains. 
Limitations: The study is confined to the Central Region of Cameroon, uses cross sectional data that preclude definitive causal claims, and relies on self reported efficiency measures. 
Contributions: The study provides empirical evidence on AI adoption in African agricultural SMEs and offers practical insights for managers, policymakers, and technology providers.