Transforming Knowledge Management with AI: Leveraging Retrieval-Augmented Generation (RAG) in Business Strategy

Abstract In today’s fast-evolving business landscape, organizations must harness their knowledge assets effectively to stay competitive. This chapter explores how Large Language Models (LLMs)-based Retrieval-Augmented Generation (RAG) systems are transforming knowledge management (KM) and business strategy. Traditional KM struggles with inefficient knowledge retrieval, fragmented information, and outdated content. RAG systems bridge these gaps by integrating real-time data retrieval with AI-driven synthesis, allowing organizations to enhance decision-making, operational efficiency, and innovation. The authors provide a detailed breakdown of LLMs, differentiating embedding models and generative models while also examining the limitations of transformer-based architectures. This chapter contrasts fine-tuning LLMs with RAG solutions, demonstrating how RAG offers a more flexible and scalable approach to keeping AI models updated without frequent retraining. Through real-world case studies in marketing, customer service, finance, and healthcare, the authors illustrate how enterprises are leveraging RAG to improve personalization, automation, and knowledge accessibility. Beyond the technical aspects, this chapter also highlights essential considerations for successful RAG adoption, including governance frameworks, ethical AI implementation, and strategies for mitigating risks such as data security, bias, and transparency. As AI-driven KM continues to evolve, advancements in RAG with agents for relational databases, multimodal RAG, and graph-based RAG will further redefine how organizations create, manage, and apply knowledge in dynamic business environments.