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The AI Product Manager's Handbook

You're reading from  The AI Product Manager's Handbook

Product type Book
Published in Feb 2023
Publisher Packt
ISBN-13 9781804612934
Pages 250 pages
Edition 1st Edition
Languages
Author (1):
Irene Bratsis Irene Bratsis
Profile icon Irene Bratsis

Table of Contents (19) Chapters

Preface 1. Part 1 – Lay of the Land – Terms, Infrastructure, Types of AI, and Products Done Well
2. Chapter 1: Understanding the Infrastructure and Tools for Building AI Products 3. Chapter 2: Model Development and Maintenance for AI Products 4. Chapter 3: Machine Learning and Deep Learning Deep Dive 5. Chapter 4: Commercializing AI Products 6. Chapter 5: AI Transformation and Its Impact on Product Management 7. Part 2 – Building an AI-Native Product
8. Chapter 6: Understanding the AI-Native Product 9. Chapter 7: Productizing the ML Service 10. Chapter 8: Customization for Verticals, Customers, and Peer Groups 11. Chapter 9: Macro and Micro AI for Your Product 12. Chapter 10: Benchmarking Performance, Growth Hacking, and Cost 13. Part 3 – Integrating AI into Existing Non-AI Products
14. Chapter 11: The Rising Tide of AI 15. Chapter 12: Trends and Insights across Industry 16. Chapter 13: Evolving Products into AI Products 17. Index 18. Other Books You May Enjoy

Summary

This chapter was all about trends and insights for AI adoption collected from some of the most reputable companies that speak about it. We looked at some of their insights and projections for the coming years and decades regarding AI adoption. Building an AI-native product is, in many ways, more straightforward than transitioning a product from traditional software development to an AI product.

In this chapter, we wanted to set the stage and discuss some of the high-growth areas for AI adoption because, for many companies, knowing where to begin is often the hardest part. Once you’re in the flow of things, you can better anticipate what comes next, but when you’re at the precipice of a major paradigm shift, there’s a lot of friction. Going over the growth areas, data, and common use cases and setting the stage for AI enablement was an intuitive choice to make sure product managers out there are aware of what adopting AI can mean for their product. It...

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