This chapter covers
- Understanding the difference between an AI vision and an AI project
- Scouting for opportunities for AI in your organization
- Prioritizing and testing your AI project ideas
- Breaking real-world complex AI products into independent ML-powered components
- Translating business requirements into ML tasks using the Framing Canvas
In this chapter, you’ll take your first steps into the messy world of AI innovation. Think about machine learning as a toolbox full of various items like hammers, screwdrivers, and saws. After reading part 1 of the book, you know how all these tools can be used to build products. Your next step is to figure out what to build with these tools and how.
This chapter starts by presenting the framework we developed to identify, select, and validate the most promising opportunities for AI in your organization. We’ll also show you how to break a complex product into more ML-friendly components that can be built independently. Finally, our Framing Canvas will teach you how to translate the output of all this preliminary work into a description of your project that can be consumed by a technical team.