AI and machine learning products are on the roadmap of almost any company today, and the trends are rising.

Hands-on tips for companies to build Machine Learning Products that are being adopted by their users and customers. The biggest difficulty for products based on machine learning (ML) will be user or customer adoption. How did I come to this conclusion? A top executive of one of the biggest European insurance companies told me: "We have the money and technical talent to build sophisticated ML-products, but we do not know how to make users adopt those products. We spent millions of dollars on an ML-based app but only got around 300 users. We do not understand why people do not want to use our app."

I hear a lot of complaints about machine learning from business clients. It's too complicated is one of them. Machine learning is complex by nature. The more that complexity spills over to users and executive management, the more turned off to machine learning they become. I've seen it happen in real time.

So think about how to build the whole product with the following in mind: Is the data that's coming out of this product going to be good training data? That, I think, is something that should always be on the table. Some examples of how to do it right would be giving users opportunities to correct errors when they occur, and making sure that it's done in the flow of the product. It should be presented in a way that the user feels like it's going to provide value, because they are helping to fix the product and helping to improve it for their own benefit.