There are a lot of problems that arise from lack of domain expertise, but they can be overcome with a multidisciplinary team.
The biggest defeating problem for pure AI teams is that they don't understand the domain well enough to know if their data sets are representative. Humans are great at salience assessments, and can ignore tons of the examples and features they witness when using their experience. This affects dataset curation. When a naive ML system trains on this data, it won't appreciate the often implicit curation decisions that were made, and will thus be miscalibrated for the real world.
A domain expert can offer a lot of benefits. They could know how to feature engineer in a way that is resilient to these saliency issues. They can immediately recognize when a system is making stupid decisions on out of sample data. And if the ML model allows for introspection, then the domain expert can assess whether the model's representations look sensible.
I'm scenarios where datasets actually do accurately resemble the "real world", it is possible for ML to transcend human experts. Linguistics is a pretty good example of this.