Human-AI Hybrid Workplace Optimization and Productivity in Labor-Intensive Agricultural Operations in Centre Region of Cameroon: An Empirical Study

Abstract

The agricultural sector in Sub-Saharan Africa faces severe labor shortages and productivity stagnation, with Cameroon's Centre Region experiencing particular challenges including 15% workforce decline over five years and only 1.5% annual productivity growth. This study investigated the relationship between human-AI hybrid workplace optimization and productivity in labor-intensive agricultural operations in Cameroon's Centre Region. A cross-sectional survey of 138 agricultural operators across 15 enterprises examined how human-AI collaboration intensity influences operational productivity and how hybrid workplace optimization affects workforce outcomes. Findings revealed significant positive relationships between collaboration intensity and productivity, with AI-human task sharing showing the strongest correlation (r = 0.485, p < 0.001). Hybrid workplace optimization significantly predicted workforce outcomes (R² = 0.379), with training and skill development emerging as the strongest predictor (β = 0.242, p = 0.003). This study contributes empirically to Socio-Technical Systems Theory by demonstrating that human-AI collaboration intensity is a measurable predictor of productivity outcomes in Central African agricultural contexts, extending previous research primarily focused on developed economies. The findings provide practical guidance for Cameroonian agri-businesses, cooperatives, and extension services seeking to optimize human-AI collaboration through targeted investments in training, organizational support, and clear role definition, while acknowledging infrastructural and capacity-building challenges. Given the cross-sectional design, causal relationships cannot be definitively established; future longitudinal research is recommended.