Marily Nika: evaluation

Transcript context

…So say you want to start investing in some sort of model, some sort of AI within your team, you're saying maybe hire data scientists who can help you start to build something that you can start integrating. Is that your advice on the first step of once you start, you want to start getting serious about building some sort of AI component. There is something called the shiny object trap and I'm always telling people, "Hey, don't do AI for the sake of doing AI". Make sure there is a problem there. Make sure there is a pain point that needs to be solved in a smart way. Once you have identified what that problem is and what that very, very high level solution is, then reach out and try to figure out how to actually implement it. And there's a definition I like giving. I usually say that the generalist PM helps their team and their company build and ship the right product. But the AI PM helps their team or company solve the right problem. So if you want to get into AI PM figure out what the problem is that you will get a data scientist to create a model for solving, but there needs to be a problem, there needs to be audience, there needs to be a user and a pin going for it. What are signs that AI may not be a good approach to solving a problem? You said that, and this happened on a lot of my teams, oh we're going to build a really cool model, it's going to do something really smart in this case and it often ended up being a very low ROI investment and took six months to a year before you even knew what the hell what was happening. Do you have any thoughts on signs that maybe this isn't a place you should be putting a lot of time into AI versus this is definitely an opportunity. Yes, we should do this, invest a lot of time into this.…

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