2 What should be the focus of AI opportunity testing?
This chapter covers
- The role of AI Road Testing in the project lifecycle
- Focusing on the core challenges of your opportunity
- Assessing and dealing with analytics maturity and AI literacy
- 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 actually 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 out the practical foundation for avoiding them: the three lenses that reveal potential flaws in your AI idea—the user, enterprise, and data-analytics lenses; practical frameworks for otherwise vague concepts like analytics maturity or AI literacy; and pragmatic ways to avoid the cognitive biases that can derail any AI journey. Grounded in the realities of real-world projects—such as the Briolle Patisserie case—this chapter addresses, in a practical way, concepts that are often ambiguous and therefore handled superficially despite their importance.
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 An essential cog in the AI project lifecycle
2.2.1 The AI project lifecycle and where the AI Road Test sits
2.2.2 Stage-gate does not mean not agile
2.3 The three lenses of AI Road Testing
2.3.1 Empathy and creativity with Design Thinking
2.3.2 Efficient problem solving with the hypothesis-driven approach
2.3.3 The voice of data with the rigor of data science
2.4 The intersections of user, enterprise and data analytics
2.4.1 The analytics maturity factor
2.4.2 Stakeholder buy-in
2.4.3 Data and AI literacy
2.5 Focus on the most relevant lenses
2.5.1 When the main challenge is user adoption
2.5.2 When the main challenge is data analytics or tech
2.5.3 When the main challenge is the cost-benefit ratio for the enterprise
2.6 Cognitive biases in AI project management
2.6.1 Cognitive biases that can derail your AI Project
2.6.2 Proven ways to mitigate frequent cognitive biases
2.7 A detailed map of the AI Road Test
2.7.1 Map of analyses to identify and mitigate causes of failure
2.7.2 Map of debiasing techniques
2.7.3 About shortcuts
2.8 Summary