The document provides guidance for product managers on building AI products. It discusses common questions received about using "magic pixie dust" or AI capabilities from companies like DeepMind. It then outlines the product manager's role in ML product development as creating clarity on problems, setting success criteria, advocating for culture, and communicating carefully. It provides a decision tree for determining when ML may be useful for a product based on available data, problem type, personalization needs, error tolerance, and user comfort with opacity. Finally, it notes things to watch out like preventing poor outcomes and that ML is not a replacement for good product management.

1. Building AI products
by Google Group Product Manager
productschool.com

2. Your Product Management
Certificate Path
Certificates
Product Manager
Certification™
Senior Product Manager
Certification™
Product Leader
Certification™

3. Corporate Training
Level Up Your Team Management Skills
productschool.com

4. Come join us on Launch!
Become part of our exclusive community
Where Product People go to connect, exchange
ideas and build better together!
Join exclusive product community

5. Free Product Management Resources
Resources
Events Courses Podcasts
Newsletters Communities eBooks and
Reports

6. Building AI products
Dr. James Smith
@jws7
linkedin.com/in/jamessmith7

7. Story time

8. #1 most common question I heard
at DeepMind.
*Disclaimer - this is what I heard. It
may not have been what was asked
“I hear you
have Magic
Pixie dust. Can
I have some?”*

9. My experience
building AI
products

10. DeepMind for Google

13. Adaptive Brightness + Adaptive Battery

14. The hardest
problem in AI, isn’t
AI…

15. Android
Telemetry
Count things. Find bad numbers.

16. Building AI
products 101

17. Artificial Intelligence
Machine Learning
Deep Learning

18. PM’s role in ML product development
As a PM, generally you won’t be training models or choosing specific
technologies to use. As a PM what you will be doing:
Creating clarity on
the problem (User
Research, PRD, etc)
Setting success
criteria (PRD,
experiment design)
Advocating for
culture (privacy,
user respect, bias,
etc)
Communicating
with care (to users,
to stakeholders, to
execs)

19. When to use ML in
Products?

21. Is ML useful for your product?

22. Simplified Decision Tree: “Should I use ML?"
Do I have data that is suitable to power an ML solution?
Problem that cannot be solved with pre-programmed rules?
Does my product require personalization?
Will users tolerate errors in my product?
Are users comfortable not knowing exactly how a product functions?
Donʼt use ML Maybe try ML
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes

23. Building ML
products

28. Things to watch
out for

29. Preventing poor outcomes

30. TL;DR

31. It’s an incredibly powerful tool, that
needs to be used wisely.
PMs beware!
ML is not
Magic Pixie
Dust!

32. Learn more
Machine Learning Crash Course:
https://bit.ly/2lZ4Sdr
The Step-By-Step PM Guide to Building Machine Learning Based Products:
https://bit.ly/3RkXu5N
The Google Way to Use Machine Learning for PMs:
https://bit.ly/3KO3S36

33. Part-time Product Management
Training Courses
and Corporate Training
productschool.com