Tips for successful adoption of machine learning products(efma.com)
efma.com
Tips for successful adoption of machine learning products
https://www.efma.com/article/detail/28915
12 comments
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.
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.
Thanks for your comments. I agree with the baity title and your suggestion 'don't make shiny AI' is a better one :)
Agreed on both counts hah. This does nail the main problem being adoption rather than technology, that's 100%
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 :)
Autonomous vehicles are bounded for the trough of disillusionment? I hope not :)
Shiny is a product too, though.
"To sell something surprising, make it familiar; and to sell something familiar, make it surprising." - Raymond Loewy.
Shiny AI only works when it's augmenting something passively.
Shiny AI only works when it's augmenting something passively.
Is that graph current? ML is on the inflated expectations peak! Geez...
Don't take Gartner graphs too seriously. It's an illustration, not a quantitative display.
It is from 2017.
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.