Most organizations do not fail at AI because of algorithms. They fail because they mistake hype for execution.
AI is not magic. It is engineering, systems thinking, and discipline under real-world constraints. Data quality, ownership, pipelines, governance, and operational reality decide whether an AI initiative survives contact with production or quietly joins the growing graveyard of abandoned projects.
This book was written from inside real systems, not from slides, benchmarks, or conference promises. It focuses on the gap between ambition and delivery, and on what actually separates projects that ship from those that stall.
What You Will Learn
- Why “We need AI” is not a strategy
- Why most AI projects fail before reaching production
- The role of data readiness, ownership, and pipelines
- How to move from prototype to real-world system
- How to evaluate when AI is the right tool — and when it is not
- How to build AI systems that survive beyond the demo
Who This Book Is For
This book is written for practitioners and decision makers who want clarity over buzzwords.
Engineers will recognize the operational realities of data, systems, and constraints. Leaders will find a framework to evaluate initiatives, avoid waste, and connect AI efforts to measurable outcomes.
If you care about delivery more than headlines, this book is for you.
Read the First Chapter for free
Enter your email below to receive the opening chapter.
No noise. No spam. Only when there is something worth reading.
Continue Reading
If the first chapter resonates with you, the full book goes deeper into:
- Data and operational reality behind AI systems
- The decision framework for AI initiatives
- Governance, risk, and cost in production environments
- Building organizations that can actually deliver AI
- Turning ambition into measurable production impact
About the Author
Dominique Ronde works on real-world data systems and production AI architectures. His focus is not on hype, but on execution: how systems are built, how they fail, and how they succeed under real constraints. His work bridges engineering, operational reality, and decision frameworks for technology initiatives.
Optional FAQ
Is this book technical?
It is practical. Engineers will recognize the system and data realities, while decision makers gain a framework for evaluating AI initiatives.
Do I need prior AI knowledge?
Basic familiarity helps, but the book focuses on principles, execution, and real-world constraints rather than theory.
Is this about machine learning models?
Partly. The focus is broader: how AI systems actually work in production environments.
What makes this book different?
It is grounded in execution, not hype. It explains why projects fail and how to build systems that survive beyond prototypes.