International Journal of Science, Engineering and TechnologyAuthors: Santhosh Reddy BasiReddy
Abstract: Customer Relationship Management (CRM) platforms increasingly rely on intelligent recommendation systems to support personalized engagement, operational efficiency, and proactive decision-making across sales, marketing, service, and risk-management functions. However, traditional recommender systems largely designed for consumer-facing domains often struggle to capture the rich, multi-dimensional context inherent in enterprise environments, including user intent, organizational roles, regulatory constraints, temporal dependencies, and evolving business workflows. This article presents a unified architectural perspective on context-aware CRM recommendation systems powered by AI agents, arguing that effective enterprise personalization requires tight integration of learning, interaction, and semantic reasoning. By synthesizing advances in contextual bandits for online adaptation, conversational recommender systems for interactive preference elicitation, and knowledge graph-based representation learning for semantic context modeling, we outline a scalable framework capable of operating under real-world CRM constraints. The proposed approach bridges theoretical foundations with practical deployment considerations, demonstrating how AI agents can orchestrate recommendation logic across data ingestion pipelines, user interactions, and real-time decision loops while continuously learning from feedback. Drawing on empirical insights from large-scale recommender deployments and CRM-focused studies, we show that context-aware, agent-driven systems consistently outperform static personalization methods in terms of recommendation relevance, user engagement, system transparency, and measurable business impact.
