The examples you give seem to fit into the "marginal process improvement" mentioned, I should think? I mean, forecasting demand, optimizing shopping paths, route dispatching, etc, don't require ML to do well. ML -might- make it a bit better still, but is it enough to offset the work that went into it? Couldn't say.
Certainly, the number of times I've seen business stakeholders treat ML as magic, where they say "It's like (standard business process) but we'll use ML!" to try and create a business case is appalling. And I think that's more what he's referring to; in many companies, ML is a solution in search of a problem, one that the business is quite happy to pay for to say they're doing (it pleases stockholders), and data scientists are happy to accept money for (it's a job, after all).
If a "marginal" process improvement increases your conversion rate by a double digit percent, then it's not all that marginal, and it's not a scam.
It's kind of boring to read "our revenue jumped 4% after adding multi-objective optimisation to our existing model", but if you stick a few 4% improvements together and apply them to a big revenue stream, you get a big number.
What you're missing is the scale at which ML allows you to do those things.
Classical algorithms for path-finding for example might work really well in narrow cases that have firm constraints. ML allows you to expand the scale of optimizations arbitrarily.
Certainly, the number of times I've seen business stakeholders treat ML as magic, where they say "It's like (standard business process) but we'll use ML!" to try and create a business case is appalling. And I think that's more what he's referring to; in many companies, ML is a solution in search of a problem, one that the business is quite happy to pay for to say they're doing (it pleases stockholders), and data scientists are happy to accept money for (it's a job, after all).