The Impact of Generative Ai on Consumer Interaction Metrics

The rapid integration of Generative Artificial Intelligence (GenAI) has fundamentally transformed consumer-brand engagement, marking a transition from traditional, rule-based systems to the era of Agentic AI. Unlike predecessors characterized by rigid logic, Agentic AI consists of autonomous systems capable of performing complex multi-step tasks, maintaining contextual persistence, and generating high-fidelity content in real-time. This study analyzes how GenAI affects core performance indicators—specifically Average Handle Time (AHT), First Contact Resolution (FCR), and Customer Satisfaction (CSAT)—to evaluate whether operational efficiency translates into long-term brand equity and sustainable consumer relationships.Utilizing a mixed-methods research design, the study investigates the \\\"Trust Paradox\\\"—the tension between hyper-personalized convenience and rising anxieties regarding data privacy and the loss of human empathy. Findings indicate that while GenAI facilitates instantaneous query resolution, its success depends on the \\\"Authenticity Metric,\\\" as consumers are increasingly sensitive to the \\\"Uncanny Valley\\\" of AI interaction. The research concludes that the future of consumer interaction lies in Human-AI Synergy (HITL) and Retrieval-Augmented Generation (RAG), advocating for a shift in corporate KPIs toward \\\"Value-Per-Interaction\\\" (VPI) to maintain the most valuable metric in an AI-mediated economy: human trust.