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10 Learning from successful and failed applications of AI

 

This chapter covers

  • Successful uses of AI
  • Problematic uses of AI
  • Failed AI applications
  • The importance of good data
  • Recommendations for using AI
  • How to plan an AI project and set an AI project up for success

Every AI project, whether it succeeds or faces challenges, offers valuable lessons. Learning from these experiences empowers us to make informed decisions, guiding our AI projects toward success while avoiding common pitfalls. In this chapter, we explore the lessons learned from both the mistakes and achievements of past projects because it’s crucial to understand the factors that determine AI project outcomes. I will also share valuable advice on building the right team, fostering the appropriate mindset, and developing a promising plan for your AI project.

10.1 AI successes

Artificial intelligence has already proven its worth across a multitude of specific, well-defined applications, demonstrating its potential to revolutionize various sectors. In this discussion, we will explore these AI applications and their significant effect on our lives, while also acknowledging the limitations of current technology and offering insights into the characteristics of next-generation systems.

10.2 AI misuse

10.3 AI failures

10.4 How to set your AI project up for success

10.4.1 Data: The lifeblood of AI

10.4.2 The realistic AI perspective

10.4.3 The importance of planning

10.4.4 Risk mitigation

10.4.5 Collaboration and expertise

10.5 AI model lifecycle management

10.5.1 Data preparation

10.5.2 Behavior analysis

10.5.3 Data transformation

10.5.4 Model creation

10.5.5 Live production

10.5.6 Data storage

10.5.7 Notifications

10.5.8 Back-office review