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Research shows more than 80% of AI projects fail, wasting billions of dollars in capital and resources: Report
(www.tomshardware.com)
this post was submitted on 29 Aug 2024
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Re 1, 3 and 5, maybe it is upon the AI projects to stop providing shiny solutions looking for a problem they could solve, and properly engaging with potential customers and stakeholders to get a clear understanding of the problems that need solving.
This was precisely the context of a conversation I had at work yesterday. Some of our product managers attended a conference that was rife with AI stuff, and a customer rep actually took to the stage and said 'I have no need for any of that because none of it helps me solve the problems I need to solve.'
I don’t disagree. Solutions finding problems is not the optimal path—but it is a path that pushes the envelope of tech forward, and a lot of these shiny techs do eventually find homes and good problems to solve and become part of a quiver.
But I will always advocate to start with the customer and work backwards from there to arrive at the simplest engineered solution. Sometimes that’s a ML model. Sometimes a ln expert system. Sometimes a simpler heuristics/rules based system. That all falls under the ‘AI’ umbrella, by the way. :D