Redefining Automotive After-Sales: A Data-Centric Approach with BI & Agentic AI

Abstract

The automotive after-sales sector is experiencing a revolutionary transformation driven by integrating Business Intelligence (BI) and Agent-based Artificial Intelligence (AI). As vehicles become increasingly connected, traditional reactive maintenance models and fragmented customer experiences give way to data-driven approaches that enhance service delivery across the automotive value chain. This article examines how the convergence of advanced analytics and autonomous AI systems reshapes fundamental aspects of automotive service operations, from predictive maintenance to customer experience personalization. Through an analysis of implementation case studies at leading manufacturers, the article demonstrates how these technologies enable proactive service scheduling, optimize resource allocation, and deliver tailored customer interactions. It further explores emerging trends, including federated learning approaches that balance data utility with privacy protection, quantum computing applications for complex optimization challenges, sustainability-focused maintenance planning, and cross-brand service ecosystems. The article underscores how this technological revolution represents not merely an enhancement of existing processes but a fundamental reconceptualization of automotive after-sales services in an increasingly connected environment.