If you haven't yet worked on a problem requiring ML, you will likely encounter one in the next decade as "Software Is Eating the World, but AI Is Going to Eat Software."
Making the transition isn't as hard as it may sound. Here's how it differs:
Online Data is Not Only the Output — It's a Key Part of the Input
Savvy product managers proactively utilize existing sources of data and find new ones. Think about how TikTok is designed: because there's exactly one video on the screen, the program knows exactly how long you watched and what actions you did. This enables it to recommend the next video almost perfectly.
You Don't Control the Recipe, Just the Outcome
In traditional software, the product team determines the exact steps to generate outputs. In ML, you define the desired outcome and performance criteria, and the program determines the steps. The beauty is that the program can discover unobvious ways to solve problems, but...
Be Careful How You Set the Desired Outcome
Say you optimize a content feed for clicks. Clicks go up, high fives all around. Then you look at top content and see clickbait, low-value content, soft porn. You didn't create the experience you wanted, and advertisers may boycott you.
Junk food is immensely popular & addicting, but it might not be the restaurant you want to create.
Your Cross-Functional Team is Larger
Common additions: specialized ML Engineers, project managers & ops for offline rating, privacy experts (you'll want to retain data longer since data fuels model performance).
You Don't Need to Know the Ins & Outs, But Understanding ML Concepts is Important
There are less than 100 car manufacturers in the US and more than 200M drivers. Drivers don't know how to create a car, but they know how to drive & read the dashboard.
Key things to know: supervised/unsupervised learning, optimization & loss functions, precision/recall/false positives.
While there are differences, the most important PM skills are the same: critical thinking, communication, and user empathy.