Productizing AI-Powered Outputs

How AI product management is different

In this lesson, we will be exploring the difference between product management for traditional products and product management for AI/ML products. At first glance, it may seem that AI/ML products aren’t that different from traditional products. We’re still creating a baseline of value, use, performance, and functionality and optimizing that baseline as best we can. This is true for every product as well as for the greater practice of product management, and this won’t change just because our product works with AI.

Customization challenges

The true differentiator when it comes to AI products is we’re essentially productizing a service. Think about it for a moment. In order for AI to work, it has to learn from us or our customers’ data. Different models might work better on different kinds of data. Different datasets will require different hyperparameters from our models. This is the nature of AI/ML. In a way, this means that we could find ourselves in a situation where the way we build and structure our product could even change fundamentally from one customer to another, especially at first when we have very few customers.