AI is already generating metadata, assigning subject headings, and classifying materials automatically. Here's what that means for your career and what to do about it.
AI won't replace cataloging librarians entirely, but it's already replacing much of the routine descriptive work they do. Copy cataloging and basic metadata creation are increasingly automated by tools using MARC, BIBFRAME, and LLMs. Judgment, authority control expertise, and institutional knowledge remain irreplaceable.
TASK LEVEL RISK
Most of the work stays human. AI assists at the edges.
AI is handling specific tasks. The core role is intact but shifting.
AI is automating significant portions of the work. Adaptation is essential.
Higher risk
copy cataloging, basic MARC record creation, keyword tagging, ISBN lookup, routine subject assignment, format identification, spell checking, duplicate detection
Lower risk
authority control decisions, rare materials cataloging, controlled vocabulary maintenance, metadata standards development, taxonomy design, resolving cataloging disputes, training staff, special collections description
Cataloging librarians provide critical judgment for authority control, local context, and complex resources that AI classification systems consistently misinterpret or oversimplify.
WHAT YOU SHOULD DO
Skills to build for the AI era
New skills - Adapt to the AI landscape
New skills - Adapt to the AI landscape
Understand linked data, RDF, and BIBFRAME to prepare library metadata for semantic web and machine-readable discovery systems.
Review AI-generated metadata, identifying classification errors, biased subject headings, and inconsistencies across automated cataloging workflows and platforms.
Automate batch record processing, MARC transformations, and quality control using Python, MarcEdit, and OpenRefine for digital collections.
Apply inclusive cataloging practices, revising outdated or harmful subject headings and centering community voices in descriptive metadata decisions.
Timeless skills - What AI can't replicate
Timeless skills - What AI can't replicate
Resolve ambiguous authorship, complex relationships, and edge cases requiring deep knowledge of cataloging rules that AI consistently misinterprets.
Design taxonomies, controlled vocabularies, and classification schemes tailored to unique collections, user needs, and evolving disciplinary boundaries.
Work with subject librarians, archivists, and users to resolve cataloging challenges and shape institutional metadata policies together.
THE FULL PICTURE
What AI can do, what it can't, and where the career is headed
What AI can already do
- Generate MARC records from book covers and title pages
- Assign preliminary Library of Congress subject headings
- Extract metadata from digital files automatically
- Detect duplicate records across catalogs
- Suggest classification numbers based on similar items
- Translate descriptive metadata into multiple languages
What AI can't do
- AI cannot exercise judgment about ambiguous authorship, pseudonyms, or complex bibliographic relationships requiring cultural context.
- AI cannot design new metadata schemas or adapt standards to unique institutional collections.
- AI cannot make ethical decisions about culturally sensitive subject headings or reparative description practices.
- AI cannot mentor staff, negotiate with vendors, or lead cataloging policy discussions.
- These are the core contributions of Cataloging Librarians, and they remain entirely human.
Cataloging librarians who evolve into metadata strategists and linked data experts will remain essential for organizing knowledge in the AI era.
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Job outlook
The Bureau of Labor Statistics projects librarian employment to grow 3 percent from 2024 to 2034, slower than average. Demand is strongest in academic and special libraries with digital collections. Metadata specialists and those skilled in linked data have the best prospects.