Hamel Husain says the biggest mistake teams make building AI products is not focusing enough on data. Product managers need to write SQL, use spreadsheets, and monitor metrics. Data-driven decision-making is what separates successful AI products from the rest.
What are the fundamental concepts that you think folks building AI products really need to know about the process of getting to higher quality products?
The most important thing is looking at data, and I, I believe from working with many product managers in the past is looking at data has always been a thing even before AI. I'm pretty sure that product managers can like write a little bit of SQL or okay with spreadsheets, looking at numbers, looking at metrics. That feels like it's kind of table stakes for being a good product manager nowadays. And so there's just a little bit of a twist on it for AI, but really the same thing applies.
