Catalogue/Tier 3 · Job Skills/Research & Media

AI+ for Cataloguing & Metadata cover

T3-99 · Tier 3 · Job Skills

AI+ for Cataloguing & Metadata

Describe, Classify and Optimise Discovery with AI

The backlog is real. The expertise is yours. The system is missing.

Your backlog keeps growing and the time to clear it keeps shrinking. AI doesn't replace your cataloguing expertise — it handles the routine extraction and drafting so your judgement goes where it matters: classification decisions, authority control, quality assurance. The ENRICH Protocol gives you a six-step AI workflow for MARC21, Dublin Core, RDA, and schema.org, with 40+ copy-ready prompts and a 90-day adoption plan. Built by an AI engineering firm for cataloguers who know the standards — and need a faster, more sustainable way to apply them.

Tier

Tier 3 · Job Skills

Category

Research & Media

Format

Guide

Updated

Q2 2026

Read full bookSee in 3 bundles

Inside

40+ copy-ready prompts for record creation, subject analysis, authority control, batch processing, and discovery optimisation
The ENRICH Protocol — a six-step cataloguing workflow that maps exactly where AI helps and where professional judgement leads
The Five-Point Metadata QA Framework — a structured checklist for AI-generated records that catches systematic errors before they reach the catalogue
Four batch workflow archetypes for clearing backlogs of minimal-level, pre-RDA, and subject-sparse records at scale
A 90-day adoption plan with a workflow redesign template and a business case template for management

Who this is for

Library cataloguers and technical services staff (paraprofessional and professional), metadata librarians and specialists, and digital projects librarians working in academic, public, national, or special library settings.

Also for:Archivists and records managers managing finding aids and archival description; digital asset managers in publishing, media, and enterprise; LIS students and early-career information professionals; repository managers working with DSpace, ArchivesSpace, or Omeka.

You’ll be able to

Apply AI tools to create and enhance bibliographic records in MARC21, Dublin Core, RDA, and schema.org formats
Use AI to assign classification numbers (Dewey, LC) and subject headings (LCSH, MeSH) with professional-level accuracy and compliance
Implement authority control and linked-data enrichment using AI to connect records to VIAF, LCNAF, and Wikidata
Design batch-processing workflows to enhance legacy metadata records at scale without sacrificing quality standards
Evaluate AI-generated metadata for completeness, accuracy, and standards compliance using structured quality-assurance criteria

What’s inside

Diagnostic: How AI-ready is your cataloguing practice?
Chapter 1: AI in Cataloguing & Metadata Right Now
Chapter 2: Metadata Fundamentals in the AI Era
Chapter 3: AI-Assisted Descriptive Cataloguing
Chapter 4: Classification and Subject Analysis with AI
Chapter 5: Authority Control and Linked Data with AI
Chapter 6: Metadata Quality Assurance with AI
Chapter 7: Legacy Record Enhancement and Retrospective Conversion
Chapter 8: Digital Collections, Repositories, and Archives
Chapter 9: Metadata for Discovery: Interoperability and the Web
Chapter 10: Building an AI-Enhanced Cataloguing Practice
Back matter: Skill Summary · Recommended Next Reads · Glossary · Tool Reference

Built by an AI engineering firm for cataloguers who know the standards — and need a faster, more sustainable way to apply them at scale.

Appears in 3 bundles

IND-07The Government & Public Sector Pack ROL-10The Librarian's AI Pack SKL-02The AI Research Pack

Reads well with

Often packaged with this title.