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You’re holding a book that was born from a simple observation: most AI projects don’t fail because of bad models; they fail because of bad data. For the last two years, we have been working alongside organizations of every size across industries as they’ve raced to adopt generative AI. We’ve watched brilliant teams build impressive prototypes, only to see them stall on the road to production. The pattern was always the same. The models worked fine. The prompts were clever. But the data underneath? It wasn’t ready. That pattern was the motivation for writing this book. When ChatGPT launched in November 2022 and reached 100 million users in just 60 days, it set off a wave of excitement and panic across the enterprise world. Suddenly, every boardroom was buzzing with terms like “RAG,” “vector databases,” and “agentic AI.” Companies spun up proofs of concept by the dozen. But as a recent AWS study found, while data leaders overwhelmingly acknowledge the importance of preparing data for generative AI use cases, nearly 60% report that they have not yet made the necessary changes to their company’s data strategies. That gap between knowing data matters and actually making it work is what this book is about. We wrote AI-Ready Data Blueprints for the people in the trenches: the data architects redesigning pipelines for AI workloads, the engineers wrestling with chunking strategies, the leaders trying to figure out why their AI chatbot keeps hallucinating, and the governance teams wondering how to keep deployments safe and compliant. Whether you’re an executive trying to understand why your GenAI initiative stalled or a hands-on practitioner building your first production retrieval-augmented generation (RAG) system, we wanted to give you something practical—not theoretical, not hype-driven, but grounded in what actually works.