Some AI systems can show their work: which documents they retrieved, which rule fired, which factor weighed most heavily in a decision. Others, particularly large models making judgment calls, are genuinely harder to fully explain even by the people who built them. Explainability is how much of that gap you can close, in terms a non-technical reviewer can actually follow.
It’s not a nice-to-have layered on afterward. Research on enterprise AI adoption consistently finds it’s one of the strongest drivers of whether people actually trust and use a system: an unexplainable answer generates hesitation even when it’s correct, and a wrong answer with no visible reasoning is far harder to catch and correct than one you can trace back to its source.
For regulated decisions, explainability is often the difference between a system that’s deployable and one that isn’t. Regulators increasingly test whether a decision can be explained and audited after the fact, not whether a human clicked “approve” on every individual case.