I managing some teams right now that do a mix of high-end ML stuff with more prosaic solutions. The ML team is smart, and pretty fast with what they do, but they tend to (as many comments here have mentioned) focus on delivering only PhD level work. This translates into taking simple problems and trying to deorbit the ISS through a wormhole on it rather than just getting something in place that answers the problem.
In conjunction with this, it turns out 99% of the problems the customer is facing, despite their belief to the contrary, aren't solved best with ML, but with good old fashioned engineering.
In cases where the problem can be approached either way, the ML approach typically takes much longer, is much harder to accomplish, has more engineering challenges to get it into production, and the early ramp-up stages around data collecting, cleaning and labeling are often almost impossible to surmount.
All that being said, there are some things that are only really solvable with some ML techniques, and that's where the discipline shines.
One final challenge is that a lot of data scientists and ML people seem to think that if it's not being solved using a standard ML or DL algorithm then it isn't ML, even if it has all of the characteristics of being one. The gatekeeping in the field is horrendous and I suspect it comes from people who don't have strong CS backgrounds wrapping themselves too tightly against their hard-earned knowledge rather than having an expansive view of what can solve these problems.