As markets consolidate, most companies will use one, maybe three different AI products to rate people. So if an AI decides that you're not fit for work, you're done for - no matter where you apply, it'll be that one AI returning the exact same result. There's no variance of different people looking at applications.
Maybe it doesn't like your name, one of your past employers, or Oxford commas. It's effectively a black box, so who knows?
Filtering the initial batch of candidates is basically a classification problem. It is also very time-consuming to perform by hand.
If recruiters are able to used supervised/unsupervised algorithms to filter out 90% of the initial candidates with a low misscladsification error and without wasting valuable time the they'll be able to operate more efficiently.
The trick is how to tell how far the auto classification algorithms should go in helping pick the best candidates. Some company use stupid fizzbuzz tests to weed out candidates based on a quantifiable index by abusing a metric whose value is negligible but very effective in weeding out a considerable portion of initial candidates.