How Are Academic-Library Metadata Departments Using AI?

Academic-library metadata departments are using AI, but adoption is advancing faster than confidence, policy and institutional support. Primary Research Group's survey of 47 metadata librarians found that more than 38% were actively using AI tools in cataloging or metadata work. Respondents cited ChatGPT, MARCedit with AI plugins and machine-learning classification systems, among other tools.

The clearest benefit is not replacing professional judgment. It is reducing repetitive work. Forty-four percent said AI had helped with tasks such as authority control and metadata enrichment, freeing time for more complex cataloging decisions.

The same survey shows why implementation remains cautious. Although 51% had received some AI-related training or professional development, only 19% felt very confident applying the tools effectively. Nearly two-thirds—63%—expressed concern about reliability and ethics, especially bias and transparency in automated subject classification.

Institutional direction is uneven. Twenty-nine percent said their organizations actively encouraged AI experimentation, while 36% reported little or no guidance or support. That gap matters: a department can acquire tools faster than it develops review standards, acceptable-use rules and a method for documenting machine-assisted decisions.

For library leaders, the practical question is no longer whether metadata staff will encounter AI. It is whether experimentation will be governed well. The strongest programs are likely to pair limited, reviewable use cases with training, quality checks and clear accountability. In metadata work, efficiency is valuable—but trust in the record remains the product.