I'll take a stab at actually guessing why aside from the issue that people making purchasing decisions don't see how bad it is until work has already gone into bringing in docs and pushing people to use it.

Aside from the organizational issues, I think there's a problem where basically no search system can be good for every org with any kind of internal info and different queries from perhaps several distinct types of users with different goals. To get good, a system needs to improve through at least rudimentary ML. At its simplest, if Alice searches for X today and clicks doc3, if Bob searches for X tomorrow, doc3 should rank higher. This requires collecting and aggregating click stream data, and using this count info (with cardinality #docs x #queries) at search time. But sometimes it requires a richer model relating search terms to terms in relevant (clicked) docs and optimizing for some measure of search quality (NDCG) etc. All of this requires detailed access to docs, search/click histories, and a fair amount of computation and storage. But customers have legit reasons for wanting these docs to only be accessible by their own employees. And they don't want to dedicate their own staff to improving such a system. No one wants to hear that their model retaining ran out of memory, etc. So shipping a simple system which doesn't improve but doesn't have moving parts becomes a local optima.