AI product teams don’t look like traditional product teams anymore.
Designers and software engineers are now joined by data scientists, ML engineers, and research-heavy roles — and for many Product Managers, this shift can feel intimidating.
In this episode of Product Bakar, Rishikesh Kankal breaks down how PMs can work effectively with data-heavy AI teams — without being data scientists themselves.
You’ll learn:
The real difference between data scientists, ML engineers, and software engineers
Where PMs add the most value in AI product teams
How to frame problems instead of prescribing technical solutions
Why data availability and feasibility matter more than flashy AI ideas
How to define success beyond model accuracy — focusing on adoption, trust, and business impact
Practical ways to build strong collaboration and protect your team from “AI magic” expectations
If you’re a PM working with AI teams — or about to — this episode will help you lead with clarity, credibility, and confidence.
Episode Tags / Keywords
Product Management, AI for PMs, Data Scientists, Machine Learning Engineers, AI Product Teams, Generative AI, Product Leadership, AI Strategy, PM Skills, AI Collaboration