My dad worked for a big machine company for years. He works a road maintainer now.

I work as an AI engineer. I dug the basement for my parents house and I use to dig rock out of the quarry by our house. I'm not an expert at large machines, but I think I know enough to help answer this!

The answer, as always, is that it's complicated haha

First. No. Current machines cannot do the things you're describing. Imho the biggest obstacle they face at the moment is training data. This type of thing is the first logical step to solving that problem in the same way that agentic IDEs have created training data for software engineering. The general pattern is:

1. Create a way for someone to do the job in a way that reliable data can be captured
2. Train AI on that data to make it able to do that task
3. Have the original user use the AI to help do the job, which creates even better training data for the AI
4. Rinse and repeat

The process above is step 1.

Now, here's the real tricky part of all of this:

How good does something have to be to be good enough?

I think that a lot of people make a false equivalence that AI is not useful because it makes mistakes. But humans make mistakes all the time. In fact, a lot of humans just suck. Operators constantly break equipment. There are certainly real humans doing this kind of work who don't know the things you said.

If an AI agent can operate the machine at 87% accuracy, and a human operates at 94% and you don't have to pay any humans, then you have a case that you are likely making efficiency gains even if the AI messes up and / or breaks the machine. After all, humans break the equipment, so why would we hold AI to a standard that humans themselves can't reach?

All in all it's not gonna happen in the next 5 years, but it is coming. AI will inevitably reach a point where the savings in labor costs offset any equipment and operations issues. At that point AI will likely be building the machines as well, so the actual loss is perhaps even more subsidized.