Not every AI project fails because of the model. Many fail because they never should have reached the proof-of-concept stage in the first place.
AI Go/No-Go is a book built around that idea. Instead of jumping straight into development, it offers a repeatable way to evaluate AI opportunities before you invest in building or buying anything.
The framework combines user-centered design, hypothesis-driven problem solving, and data and engineering considerations into a practical road test that teams can complete in days. Not months. Whether you're working with generative AI, traditional machine learning, or optimization, the goal is the same: make evidence-based investment decisions before committing resources.
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Early reviews from readers
"The overall concept is excellent. The book fills a real gap between AI strategy and AI implementation."
Sreenidhi Polineni
Product Manager, Nuanza
"This is a timely and practical book that provides a solid framework for planning and implementing AI projects."
Alaa Khamis
Professor at KFUPM and author "Optimization Algorithms: AI techniques for design, planning, and control problems"
"This book will help you navigate the Daedalian complexity of modern AI software construction with greater ease and confidence."
Ioannis Atsonios
Athens, Greece