Notes: AI in Product Management
I came across an ad for the titled event via a post by a connection for a bay area event on Linkedin hosted by Women In Product (WIP).
How I have missed these events! Going back to my MBA days, I have attended events like these at the Computer Science (CS) school in Carnegie Mellon when I did my MBA at the Tepper School. Sharing some brief notes I took based on the moderator’s questions, I captured what I could. Which I thought will be quite interesting to share and even add my own opinion to the mix as well after collating some of the speaker’s responses.
The following are the speaker notes (rephrased)
How deep should PMs get in AI/ML technologies? How technical should an AI based PM be?
Product Managers should be able to effectively define success metrics, and determine the objective function of what the product is trying to achieve. Understand the problems the product can solve and what use cases it fulfils. Based on this, how tactical should we get?
- be involved in the data: Alyssa spoke about an algorithm labelling a wheelchair bound person as “loser”, which is a problem when the underlying training dataset proved to be problematic.
- Product managers should be inherently curious. We should be able to…
