* Conway's law causing multiple different data science toolchains, different philosophies on model training, data handling, schema and protocol, data retention policies, etc.
* Coming up with tech solutions to try to mitigate the impact of multiple silos insisting on doing things their own way while also insisting that other silos do it their way because they need to access other silos' data.
And the reason standardization won't happen: the feudal lords of each of those branches of the hierarchy strongly believe their way is the only way that can meet their business/tech needs. As someone who gets to see all of those approaches - most of their approaches are both valid and flawed and often not in the way their leaders think. A few are "it's not going to work" levels of flawed as a result of an architect or leadership lacking operating experience.
So yeah, it might look like technical problems on the surface, but it's really people problems.