AI explainability is not a single feature. It is a design problem that changes depending on who is reading the explanation. Nielsen Norman Group's latest piece argues that developers, system administrators, and domain experts each require fundamentally different explanations of AI outputs, and that one-size-fits-all transparency is not transparency at all.
The article defines AI explainability as the degree to which an AI system's decisions are understandable to humans, covering traceability, source attribution, and reasoning steps. The core argument is that low adoption in enterprise AI is not just a change-management problem. It is an explainability design failure. When the people building and maintaining AI systems cannot interpret its behavior in terms relevant to their own expertise, trust collapses before the tool reaches end users.
What makes this worth reading in full is not the conclusion but the role-by-role breakdown. NN Group maps specific explainability needs to specific technical personas, which gives practitioners a concrete framework rather than a principle. If your organization is measuring AI adoption and finding it flat, this is the diagnostic you are missing.
[READ ORIGINAL →]