Get the eBook free when you register your print book at Manning. How can you be sure your next AI project is worthwhile before you build it? Look Before You Leap offers a repeatable go/kill/pivot decision framework you can apply to any AI project, from classical machine learning to generative AI agents. It unifies design, strategy, and finance assessments with engineering feasibility into a fast, evidence-based way to determine which AI ideas deserve resources and which are best avoided!
This book teaches you how to make evidence-based investment decisions on AI projects. Author Christophe De Greift guides you through examples in real estate, retail, manufacturing, finance, and more. You'll learn to apply one-page canvases, problem-solving frameworks, readiness and maturity scorecards, stakeholder interview guides, prioritization models, editable AI prompts, and other practical tools, all with explicit criteria built in. The result is de-risked AI projects that serve real user needs, create measurable business value, and can be delivered with the data and tools easily available to your organization.
What's inside - Investigating user needs, business goals, and organizational readiness
- Align on an AI approach statement through creative, efficient problem-solving
- Build a compelling business case by testing desirability, feasibility, buy-in, and viability
- Organize for effective collaboration throughout the road test
- Make evidence-based go/kill/pivot decisions before development begins
About the reader For AI product owners, project managers, and data scientists who understand the principles of Agile and design thinking.
About the author Christophe De Greift is a senior expert in business analytics and artificial intelligence who helps organizations turn AI ambition into measurable results. With top-tier training from Boston Consulting Group and London Business School, hands-on analytics leadership at Amazon, and advanced studies in Statistics & Data Science through MITx, Christophe bridges strategy, technology, and execution. Since 2015, he has worked with large enterprises and growing organizations to convert boardroom vision into testable AI initiatives.
