Yes, and there are two other points to follow up this one:

If one ML solution is better than the other one by only 0.01%, in this context it is worth a lot. It is a winner-take-all type of situation.

Edit: the second point was this: If one ML algorithm gives us a 1% saving and the other one gives us a 0.9% saving, you could say that is a 0.1% improvement or you could say that is a 10% (of 1% saving) solution. A percentage sign without context is not that meaningful.

On the other hand, if ML gives you a 0.5% disadvantage, that too is a non-trival amount of losses on a national scale. Because they compared against baseline rather than against another data science team (or both), it's difficult to know if ML actually brings an advantage here.

Can't we assume they already have a data science team? Optimizing the grid is their job already right?

From what I've seen in several places though is companies tend not to listen to their own people, but are willing to make changes based on the advice of hired "experts". So the experts will come in and ask everyone what the challenges are and what they might do to improve. Then the "experts" will pick several low hanging fruit and find a few things that cross internal boundaries (the harder things to change) and make recommendations. Upper management will go with it and improvements will be made. I suppose the way to sum it up is that they bring objectivity to the table.

What I'm wondering is why I can't find anything about ML applied to finding and curating data, which is the most tedious part of data science. That would be an interesting way of using ML without fuzzy stuff.

There are a few current projects, particularly in database research. I don't know how many of them use ML in the traditional understanding. Current projects I know of are Wrangler, Mimir, Katara, MayBMS (in no particular order).

Did you try any of them. Wrangler seems like a tool similar to OpenRefine to me.

Would you pay for it?

But is it non-trivial net of the expense of gaining it?

That is a much easier decision to make, but i have no idea what the costs and savings involved are so I do not know.