AI-Ready Data Blueprints

Chapter 5. Knowledge Bases and Vector Databases

In today’s AI-driven enterprise landscape, the ability to ground generative AI applications in accurate, up-to-date organizational knowledge has become a competitive imperative. This chapter explores the data strategy foundations that enable organizations to build production-ready GenAI and agentic AI systems through three critical pillars: knowledge bases, retrieval-augmented generation (RAG), and vector databases.

The GenAI Data Challenge

Traditional large language models, while powerful, face fundamental limitations when deployed in enterprise environments. They operate with static training data, often outdated by months, and lack access to proprietary organizational knowledge. This creates a critical gap between AI capabilities and business needs—one that costs organizations both accuracy and competitive advantage.

Unstructured data is widely estimated to comprise 80–90% of enterprise information, with enterprise data volumes growing at around 55–65% per year.¹ Yet most organizations struggle to leverage this data effectively, lacking the technical infrastructure needed to access, integrate, and utilize unstructured data in trusted ways. As a result, the vector database market is rapidly expanding: projections suggest it will grow from $2.55 billion in 2025 to over $15 billion in 2035, reflecting the increasing demand for infrastructure capable of managing and retrieving this data at scale.

This chapter centers on three interconnected ...