Tips for successful adoption of machine learning products

Great article. One obstacle to ML adoption I've noticed in my field (public accounting) is what I describe as the chicken and the egg data/value challenge. You can't deliver value with ML until you have the right data to train value-generating algorithms. But often you can't get the right data until you produce the value to attract users.

Everyone at my firm wants to jump in and create some shiny new AI tool to take to market, but rarely understand what that requires. So I frequently find myself pushing back against premature AI projects. I usually argue that we first need to build a simpler application that provides value out of the box in order to drive data into a single repository which we can then use to train algorithms on. I keep repeating the phrase "ML should be a version 2 feature."

I think this article identifies the root of the problem. No amount of marketing hype can replace a legitimate value proposition for users. And with rare exceptions, ML alone isn't going to provide that value.

Despite the baity title, I clicked and I'm glad that I did. Though the article is not presenting 10 actionable tips, the information is quite insightful and well-researched.

The real takeaway is that "shiny! AI!" does not sell products. Meeting a need and not requiring users to change their behaviour sells products, AI or not.

Nice article, I especially (and always do enjoy) a plotted out Gartner graphs although some placements always surprise me!

Autonomous vehicles are bounded for the trough of disillusionment? I hope not :)

Shiny is a product too, though.

Is that graph current? ML is on the inflated expectations peak! Geez...