The integration of artificial intelligence (AI) in the automotive industry represents a paradigm shift in customer experience management, fundamentally altering the dynamics between consumers and vehicle systems. This research investigates the complex interrelationships among AI-driven technologies, trust formation mechanisms, and emotional customer experiences within automotive contexts. Through a mixed-methods approach combining quantitative surveys (n=487) and qualitative interviews (n=35), this study develops and validates a comprehensive conceptual framework that elucidates how AI characteristics—including competence, benevolence, transparency, and reliability—influence both cognitive and emotional trust dimensions. The findings reveal that emotional trust mediates 68% of the relationship between AI system interactions and customer loyalty, while cognitive trust directly impacts technology adoption rates (β=0.74, p<0.001). Advanced Driver Assistance Systems (ADAS) demonstrate differential trust calibration patterns, with transparency-enhanced interfaces increasing trust scores by 42% compared to standard implementations. The research contributes a theoretically grounded, empirically validated framework integrating Technology Acceptance Model (TAM), Trust Transfer Theory, and Affective Computing principles, providing actionable insights for automotive manufacturers seeking to optimize AI-enhanced customer experiences. Practical implications emphasize the necessity of designing emotionally intelligent AI systems that balance functional competence with empathetic engagement to foster sustainable customer relationships in an increasingly automated automotive landscape