Worker-Side Labor Matching Platforms: An AI Tool for Agri-Workers Employed by Agribusinesses in Northwest Cameroon

This study designed, developed, and evaluated a worker-side AI-powered labor matching platform for agricultural workers in post-conflict Northwest Cameroon, where the Anglophone Crisis has disrupted informal labor networks. While existing agricultural platforms prioritize farm managers or buyers, this study introduces a worker-centric approach placing agricultural workers as primary users and provides the first empirical evaluation of such a platform in a post-conflict African setting. A concurrent mixed-methods design was employed across 10 agribusinesses in Santa, Ndop, and Bamenda, tracking 350 workers (175 women, 175 men) over six months through surveys, interviews, focus groups, and usability testing. Difference-in-differences and panel regression analyses were used to assess impact. Voice-enabled, offline-capable platforms were significantly more accessible than text-based alternatives: 73% preferred voice interfaces and 87% required offline capability. Platform users experienced significant improvements: +1.0 additional days worked weekly, +2,400 CFA higher wages, and reduced wasted travel by 23 percentage points (p<0.001). Agribusinesses benefited through reduced labor shortages (-1.9 days monthly), lower hiring costs (-16,400 CFA), and decreased harvest waste (-7 percentage points). Worker-centric platforms can reduce information asymmetry when designed with voice interfaces, offline capability, multilingual support, and integration with informal networks. Policymakers and technology developers should prioritize inclusive design and rural onboarding.