If the business wanted to track the rate of failures and create predictive models about when things fail, or detect anomalous behaviour, that's what they would have set out with as the goal, and, perhaps, some ML model might have helped, but probably, it would've been too unreliable and any number of standard predictive models with well known characteristics would have been used instead.
That's not what they wanted.
What people are being sold is AI/ML as a magic bullet that will do something useful regardless of the situation,
and it lets business people avoid making decisions about what they actually want, because AI/ML can be anything,
so they just signup for it and expect to get 20 things they didn't know they wanted handed to them on a plate.
Turn out, it's not enough to just collect a bunch of data and wave your magic wand at it. It wasn't with web analytics 10 years ago, it's still not.
What you actually need is someone who has a bunch of tricks up their sleeve, and has done this before, and can suggest a bunch of Business Insights the business might need before they start building anything, people that actually decide what to do, and actions taken to investigate, and solve those problems.
I mean, to some degree you're right; perhaps ML models could be useful for tracking hardware failures, but that's not what the parent post is talking about. The previous post was talking about just collecting the data and expecting the predictive failure models to just jump out magically.
That doesn't happen; it needs a person to have the insight that the data could be used for such a thing, and that needs to happen before you go and randomly collect all the wrong frigging metrics.
...but hiring experts is expensive, and making decisions is hard. So ML/AI is sold like snake-oil to managers who want to avoid both of those things. :)