Only if it is built to. Explanation cannot be bolted onto an AI system afterwards; the answer has to be produced in a way that leaves evidence. That is an architecture decision, and it is the difference between AI you can use in a boardroom and AI you can only use for brainstorming.
Two ways to produce an answer
A language model asked “what was our revenue last month” will produce a fluent number from nothing; that answer cannot be explained because nothing real produced it. The alternative is to make the model orchestrate rather than remember: translate the question into a query over real records, or a retrieval from real documents, and phrase what came back. That answer is explainable by construction, because there is a query and there are sources.
What explanation looks like in Butterflai
- Numeric answers are computed by SQL over your synced business data. The figure has a query behind it, not a vibe.
- Document answers cite the files they drew from, so verification is seconds, not archaeology.
- Structured reports run on deterministic pipelines: same inputs, same outputs, every time.
- AI-generated content is labeled as AI on every surface, per the EU AI Act’s transparency rules.
- And when the data cannot answer the question, the right output is “the data does not show this”, not an improvisation.
The full picture is on the Explainable AI page. Get a demo and ask it to show its work.