Are we talking about ML or DL in particular? Also, does ML have to be sophisticated in order for it to "count"?
Just off the top of my head, 3D/HD mapping companies like HERE rely on DL-based image recognition to recognize objects at reasonable levels of accuracy -- totally infeasible to do manually at scale.
Also, one of the triggers of the renaissance of NNs is that it was shown to outperform traditional computer vision techniques around the early 2010s. USPS does handwriting recognition every single day -- maybe not with DL -- but definitely with some ML algorithm (I was at a talk given by one of the originators of said algorithm).
ML is more than just DL. If take the definition of ML encompass to statistical learning -- which it traditionally does -- production ML deployments is extremely pervasive, from industries as varied as finance to chemical manufacturing.
The exact FICO algorithm is proprietary, but algorithms of its ilk are pervasive. As you know, at least two other companies (Transunion and Experian) also have their own algorithms. And credit scoring algorithms are among the least sophisticated ML deployments.
I'm not able to talk about the ML models I work on, but they're in production and the business relies on them.
I guess I'm not seeing the finer points of the argument -- as it stands without further qualification or refinement, it does not seem to be a valid conclusion.