Predictive maintenance is not that controversial. I built that for a big mining company with pretty broad asset base about 10 years ago. It’s easy to plug some ML in the place of other prediction models. The challenge with these programs are important nuances such as work order definitions and standardisation, whether you use new vs refurbished components, impact on cost, downstream maintenance impacts, useful life etc. Quite exciting domain, I wish I worked on that a bit more as I hand to hand over and people who received got confused on even more basic staff such as dynamic updates on strategy, I.e. run to failure vs predictive etc. Therefore, yes, it can possibly go wrong, but no, it does not have to.