Like other popular terms (“Big Data”, “Blockchain” ...) companies fear being left behind so they scrape up whatever they were already doing and get the marketing team to just say “now with AI” and carry on doing what they always did/sold.
After three months of discussions explanations and meetings and all the jazz, I asked them for a summary of the proposed solutions on their side and the costs as we were already making no progress and the cost estimates they were throwing around were massive. At the end, the cost estimate was huge, and the solution was basically "we're gonna try doing X and at the end it might work, or it might not work because what we do is magic".
So when talking to the CEO of the company whether we should embark on this expensive journey and decided against it. But because they had no other solution, they went with the vizualization tool I added and suddenly they realized it fits all their needs. Since half a dozen years, they've been using just that tool and extracting insane value from it.
I guess I lost my train of thought there, but to conclude, I believe until execs and managers learn to use the tools already available at their disposal, and generate awesome reports with almost 0 SQL knowledge - that can really cover pretty much every traditional business scenario I've encountered so far -, AI and ML are very much outside of their grasp and is more useful for technology businesses.
Way back, I was getting to know about stock market analysis. I concluded that the recommended (technical) analysis was, by any individual recommender, just outside their area of mathematical competence. Someone with very little math would be impressed with (say) moving averages, but someone who understood some statistics would denigrate moving averages, but be impressed by Bollinger bands, etc.
Since no one understands neural networks :-) everyone was impressed by them.
Seems like a similar thing with AI/ML -- "Hey, this is beyond my level of understanding, it must be magic! Buy buy buy!"
Having successfully built that, those capabilities could be applied at scale and then we started experiments with more advanced analyses, this time more successful since both we had much more data and the customer became familiar with the data-intensive development.