"Explainable AI" often gets treated as a research topic — attention visualization, feature attribution, model interpretability techniques. For most enterprise use cases, the practical version is simpler and more achievable: can the system show exactly which source informed its answer, and can a human verify that source directly?
That's a retrieval and citation problem more than a model-interpretability problem. A RAG system that grounds every claim in a retrieved passage, and shows that passage alongside the answer, gives a reviewer something concrete to check — without needing to explain the internals of the model itself.
This matters most in exactly the industries where AI adoption is slowest for good reason: legal, healthcare, finance. A hallucinated clause in a contract summary or a wrong figure in a financial report isn't a minor bug in those contexts — it's a real-world harm with a real cost.
The systems that succeed in these environments share a common design goal: not maximum helpfulness, but maximum verifiability. An answer that's slightly less fluent but comes with a checkable source beats a more polished answer that can't be traced back to anything.