Overview

Most generative AI pilots work in controlled conditions but fall apart when exposed to real data, legacy systems, and day-to-day operations. This book explains why. Drawing on years of work across automotive and industrial environments, it shows how weak data practices, unclear ownership, and misaligned expectations undermine even the most promising pilots. It then outlines how strong data foundations, sound governance, and practical leadership choices help organizations scale AI safely. Readers will learn how to assess data readiness, structure pilot charters, avoid pilot purgatory, and design a path from proof-of-concept to production. The guidance connects technical, organizational, and regulatory realities, making it useful for leaders responsible for stable, enterprise-ready results. This book is written for CIOs, CDOs, AI CoE leads, and senior executives who want AI systems that perform reliably in the real world. The approach works because it is grounded in lived experience, industry research, and examples shaped by high-stakes environments. Read it to avoid common pitfalls and build AI capabilities that stand up to operational complexity.

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