(Oct 2026) NISO has announced a 12-month pilot for a minimum viable metadata model to facilitate the tracking of provenance and attribution of content in AI systems, addressing a critical challenge AI presents for scholarly publishing.
With the development of AI-assisted search, machines have effectively become readers, with AI agents analyzing and retrieving vast amounts of content and data to answer queries. This transformation has led to a “zero-click” environment in which users can access information drawn from published content without ever visiting a publisher site. This scenario poses a problem for scholarly communications, as both publishers and librarians have traditionally relied on usage statistics to assess the value of content. At the same time, large language models are notoriously unreliable in disclosing where content came from as well as in citing sources, which can weaken credibility and trust in scholarly research. To address this challenge, the NISO pilot will develop and test a minimum metadata model that accurately tracks provenance and attribution when AI systems retrieve information or data from scholarly content.
The announcement in full is here.
