AI-Enabled Optimization of After-Sales Service Performance: Evidence from a Quasi-Experimental Study

Intensifying market competition has elevated after-sales service as a critical source of competitive differentiation, yet many service organizations continue to face operational inefficiencies, including prolonged work-order processing, high maintenance error rates, and suboptimal resource utilization. This study examines how AI-enabled optimization reshapes after-sales service performance using a quasi-experimental pre–post design. Longitudinal system-generated KPI data collected before and after AI deployment are integrated with structured face-to-face technician interviews to capture both performance outcomes and underlying behavioral mechanisms. The results indicate statistically and practically significant improvements following AI implementation: average processing time decreased by 24.2%, maintenance error rates declined by 43.3%, spare-part shortage frequency fell by 45.6%, and first-time fix rates increased by 17.3%. These findings demonstrate that AI enhances service efficiency, quality, and resource allocation when embedded within organizational workflows. The study contributes theoretically by positioning AI-enabled after-sales systems as dynamic capabilities, integrative operant resources, and acceptance-dependent technologies, while managerially advocating closed-loop AI–analytics frameworks to institutionalize continuous improvement and strategic alignment.