Some of my work is related to traditional BI tools and not one day goes with me hearing someone, specially the management, talk about applying machine learning

Some of my work is related to traditional BI tools and not one day goes with me hearing someone, specially the management, talk about applying machine learning in some way. Now while I can see some aspects which can be improved by usage of ML many don't tend to understand the implication of such systems. A good example came last year. On the BI system there existed 200+ reports with an average user having access to maybe 20 reports based on their department - finance, sales etc. There was a big push from the management to "improve" user experience with ML. Build a recommendation system they said. There was no cost benefit analysis done for using a complex ML based recommendation system on 20 reports - It sounded cool and in line with the hype. Then there is the question of user involvement. While I believe there is surely a case to made for stuff which involves software engineers, like the ones mentioned in the post - search ranking, data center energy usage etc. But things which require non-software engineers is a bit doubtful. This is because as the post puts it - "This doesn’t require the same technical skills as traditional programming, but it does need a deep knowledge of the problem domain. " And engineers cannot be expected to have deep knowledge of every function. If they get too drunk on the ML kool aid, and some of them do, the end result will be mess.