AI-Enabled Service Quality Management in Automotive Outsourcing

Abstract The automotive industry increasingly relies on outsourcing engineering services, software development, and technical support to specialized providers. Extensive research exists on artificial intelligence applications for product quality in manufacturing and on service quality assessment using the SERVQUAL model. However, no studies have examined the integration of AI technologies with service quality dimensions for managing outsourced automotive services. This conceptual paper addresses this gap by developing an AI-enabled SERVQUAL framework for automotive service outsourcing. The framework integrates five SERVQUAL dimensions (reliability, responsiveness, assurance, empathy, and tangibles) with intelligent monitoring systems including automated quality assessment tools, natural language processing for communication analytics, predictive analytics for risk assessment, sentiment analysis, and infrastructure assessment technologies. The proposed framework enables real-time service quality monitoring, predictive quality alerts, automated supplier benchmarking, and continuous improvement recommendations. This research bridges service quality theory, technological innovation, and automotive outsourcing practice, providing actionable framework for improving supplier relationship management and service delivery in the automotive sector.