The one area I've seen knowledge graphs come up are: Product Knowledge Graphs (PKGs), which are a centralized, semantic, and highly interconnected data structure that brings together information about products, customers, and their interactions into a single, comprehensive "360-degree" view. Basically, it's the idea of combing through all the data (CRMs, codebases, Ticketing System, Churn Management System, sales calls, ...) that the company has digitally about their customers, and building one giant knowledge graph that they can use to determine a bunch of business intelligence use cases, or using it to power how to create new features. Then you slap an answer bar or semantic search on top of it, and you have a powerful way of getting insights or doing gap analysis on your product versus your customer needs.

Anyway, that's just one example of why you might want to use a knowledge graph. I'm sure there are literally hundreds, of more examples.

I can't speak for everyone, knowledge graphs are the "new hotness" of the ai space (RAG and MCP are seeing a lull in their hype cycles I guess). But I've used graphs professionally for a long time to connect relationships that SQL normal forms have trouble expressing non-recursively. E.g. I used graphs to define identity relationships between data sources hierarchically, and then had a another graph relationship on top of that to define connections between those identities, user at one level and organizations at the next. Graphs as indexes allow you to express arbitrary relationships between data to allow for more efficient lookups by a database. Some folks use it to express conceptual relationship between data for AI now, so if I have a bunch of images stored in google drive, I might want to abstract the concept of pets and pets have relationship with a human etc. then my database queries for looking up all pictures related to the dog-pets owned by some human becomes a tractable search instead of a scan of the corpus!