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.
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.
Almost all companies are product driven, not customer driven. I think AI has the most benefit to customer centric driven companies, and we sort of have to wait for companies to catch on and change before we can really start seeing the expected results.
12 comments
[ 2.9 ms ] story [ 39.4 ms ] threadThe 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.
Shiny AI only works when it's augmenting something passively.
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.
Autonomous vehicles are bounded for the trough of disillusionment? I hope not :)