Plenty of industrial AI is using other methods as well, it's just not where most of the current hype is, so there's a kind of bait and switch where they lead with the DL and then if you look at the products and APIs they're actually deploying and selling, the workhorses are often some mixture of very classical stats methods (like logistic regression) and general-purpose non-NN ML algorithms (like gradient boosting). One common breakdown is that the general-purpose APIs use these general methods, and then there are separate NN-based APIs for a few specific kinds of problems like object recognition in images, where NNs give a big performance increase.

Especially true for companies in the data-science niche, since DL rarely gives you much of a win for tasks like data-mining SQL databases, or when you have only modest sized data sets, but nonetheless you still need to at least offer some kind of DL solution to be perceived as a state-of-the-art AI offering. (Like with the "big data" hype wave, many companies that think they have gigantic data sets don't.) These other methods are better understood though and not in a ton of flux, so there's no need for an acquihire frenzy to get that expertise.