chapter two

2 What should be the focus of AI opportunity testing?

 

This chapter covers

  • Understanding the three lenses of AI Road Testing
  • Assessing and dealing with analytics maturity, stakeholders and AI literacy
  • Focusing on the core challenges of your opportunity
  • Avoiding cognitive traps in AI project management
  • Making the AI Road Test work for you

Chapter 2 deepens the journey introduced in the previous chapter by moving from the importance of AI Road Testing to a clear, structured understanding of what it is and how to make the best use of it. While Chapter 1 highlighted the many pitfalls that make AI initiatives fragile, this chapter lays the practical foundation for avoiding them.

It introduces Design Thinking, hypothesis-driven problem solving, and the CRISP-DM method for AI project management—the three proven methods associated with the three lenses used to uncover potential flaws in an AI idea: the user, enterprise, and data and analytics lenses, respectively. It also presents practical frameworks for clarifying otherwise vague concepts such as analytics maturity and AI literacy, along with pragmatic approaches for avoiding the cognitive biases that can derail an AI journey.

2.1 An (almost) perfect AI success story

2.1.1 Briolle Patisserie’s journey to success

2.1.2 Lessons from the Briolle Patisserie Case

2.2 The three lenses of AI Road Testing

2.2.1 Empathy and creativity with Design Thinking

2.2.2 Efficient problem solving with the hypothesis-driven approach

2.2.3 The voice of data with the rigor of data science

2.3 The intersections of user, enterprise and data analytics

2.3.1 The analytics maturity factor

2.3.2 Stakeholder buy-in

2.3.3 Data and AI literacy

2.4 Focus on the most relevant lenses

2.4.1 When the main challenge is user adoption

2.4.2 When the main challenge is data analytics or tech

2.4.3 When the main challenge is the cost-benefit ratio for the enterprise

2.5 Cognitive biases in AI project management

2.5.1 Cognitive biases that can derail your AI Project

2.5.2 Proven ways to mitigate frequent cognitive biases

2.6 About shortcuts

2.7 Summary