Authors: Santhosh Reddy BasiReddy
Abstract: Customer Relationship Management (CRM) systems have evolved from static data repositories into dynamic enterprise platforms that orchestrate complex workflows across sales, service, and marketing functions. Despite these advances, many CRM implementations remain constrained by deterministic, rule-based automation, limited personalization, and inflexible interaction models. Recent progress in generative artificial intelligence, particularly transformer-based language models, introduces new opportunities to augment CRM systems with adaptive, context-aware intelligence capable of understanding intent, generating natural language responses, and supporting real-time decision-making. This paper investigates how generative AI can be systematically integrated into CRM workflows to enhance customer engagement, automate operational processes, and improve organizational efficiency. Building on prior research in natural language processing, conversational agents, recommender systems, and knowledge representation, we propose a conceptual architecture for AI-augmented CRM workflows that combines generative models with structured enterprise data and workflow orchestration. We analyze key enabling technologies, review empirical studies on AI-driven customer interactions, and examine ethical, privacy, and governance considerations essential for responsible enterprise adoption. Rather than replacing existing CRM platforms, we position generative AI as a complementary intelligence layer that transforms customer engagement from reactive, rule-driven processes into proactive, context-aware experiences.
DOI: https://doi.org/10.5281/zenodo.18324413
