Sounds like in that case it's better to just control things manually.
ML is pattern recognition. Anything outside of that is still AI, but it isn't ML. I can think of very few feature sets we could supply to help predict someone will be deployed to East Asia for a few days other than scraping calendars and mail for religious and military organizations.
From a design perspective, Nest and others are either additively learning in situ to enhance a base model or they are working from a base model that doesn't directly learn, just classifies workflow to categorize observations on a base model. I doubt heavy training is occurring where the Nest and similar is treated as the central compute node.
If the task is clear enough, ML can take it on by itself, but this requires clear rules and an absolutely unambiguous definition of what winning means. For example, the best chess players in the world are machines, and are FAR better than the best human players. Same for Go (the game, not the programming language).
If you have a fairly regular life I would think a schedule would outdo ML pretty much all the time, because you know exactly what that schedule should be. ML might be useful for a secret agent whose life is so erratic that a schedule would be useless.
That is to say ML is maybe better than falling back to nothing.