Abstract

This study proposes a novel AI-augmented Customer Relationship Management (CRM) framework that bridges traditional marketing approaches with emerging AI-powered relationship intelligence. While conventional CRM systems primarily focus on transactional data and customer satisfaction, recent advancements in artificial intelligence enable predictive analytics, hyper-personalization, and real-time decision-making capabilities. The research develops an integrated conceptual and empirical model combining machine learning, natural language processing, and customer behavioral analytics to enhance customer engagement, value co-creation, and long-term loyalty. Using a mixed-method approach, the study analyzes large-scale customer interaction data and evaluates the effectiveness of AI-driven personalization strategies in comparison to traditional CRM practices. Findings demonstrate that AI-powered CRM significantly improves decision quality, customer experience, and firm performance through dynamic adaptation and data-driven insights. The study contributes to the literature by introducing a relationship intelligence paradigm, emphasizing explainability, scalability, and strategic integration of AI into marketing decision processes.Managerial implications highlight how organizations can transition toward intelligent CRM ecosystems to achieve sustainable competitive advantage in increasingly digital and customer-centric markets.