Enterprise AI adoption fails when explanations are built for no one in particular. Nielsen Norman Group's latest piece makes the case that AI explainability must be role-specific, targeting the three distinct technical stakeholders who build and maintain enterprise systems: developers, system administrators, and domain experts.
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 transparency. The core argument is that these three roles bring different goals and expertise, so a single explanation format serves none of them well. That friction is a direct cause of stalled adoption.
The full article is worth reading for its breakdown of what each role actually needs from an explanation, not just the principle that differences exist. If you are designing or procuring enterprise AI tools right now, the role-specific frameworks here are operational, not theoretical.
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