AI in Libraries: Discovery, Cataloguing and Reader Services

Libraries hold collections that readers cannot search effectively, maintain cataloguing backlogs that hide material entirely, and answer reference questions ranging from the trivial to the genuinely difficult. They also hold a professional commitment to reader privacy that shapes what any system may do. This guide covers both sides, drawing on FISTA Solutions' AI agents work in knowledge institutions. It complements ai in museums and cultural institutions and the enterprise knowledge management whitepaper. This article is general guidance, not professional advice.

Why does catalogue search fail readers?

Because it requires knowing the vocabulary. Subject headings and controlled terminology are precise, standardised, and unfamiliar to most readers, who search in their own words.

The result is a reader finding nothing while the material they want sits in the collection under a term they did not think of. Semantic search over catalogue records and full text closes that gap without discarding the controlled vocabulary that makes precise searching possible for those who know it.

Function	Automatable	Librarian required
Semantic discovery over records	Yes	—
Metadata drafting	Yes	Cataloguer review
Subject term proposal	Yes	Professional judgement
Routine reference questions	Yes	Escalation
Complex reference enquiries	No	Yes
Collection development decisions	No	Yes

How can cataloguing be supported?

By drafting for review. Metadata proposed from the item, from publisher data, and from comparable records gives a cataloguer something to correct, and consistent subject term proposal addresses the variation that accumulates across decades of cataloguing by many people.

The professional judgement — what this item is, how it should be classified, what a reader looking for it would search — stays with the cataloguer. The composition and the consistency checking need not.

Why does reader privacy constrain the design?

Because libraries hold a professional commitment to reader confidentiality that is stronger than commercial privacy norms and is taken seriously as an ethical matter rather than a compliance one.

What someone reads is sensitive, and personalisation that depends on retained reading history sits uncomfortably against a commitment not to keep that history. Systems should be designed to work without retention where possible, and any retention should be a deliberate institutional decision rather than a default. See what is purpose limitation.

What is the digitisation problem?

Metadata. Material digitised without adequate description is invisible — a large image set that nobody can search is a storage cost rather than a collection.

Generating usable metadata at scale, including text extraction from digitised documents, is what converts digitisation into access. It is exactly the kind of high-volume document work automation handles well, with review proportionate to the material's significance.

What about reference services?

Questions range from the trivial — opening hours, loan periods, where to find something — to the genuinely difficult, where a reader needs help framing a research question.

The first category is high volume and answerable. The second is professional work, and the value of automating the first is that librarians spend more time on the second rather than on directions.

What about special collections?

Different again: unique material, restricted access, and researchers who need to know what exists before they can request it. Making finding aids searchable in natural language, rather than requiring a reader to navigate a hierarchical archival description, opens holdings substantially.

Who should own it?

Library services, with cataloguing owning metadata standards and a named owner for the privacy position. That last is important, because the privacy constraints are professional commitments that a technology-led implementation will erode without meaning to.

How is it evaluated?

Successful discovery — readers finding material they previously could not — reference questions resolved without escalation, cataloguing throughput with quality maintained, digitised material with usable metadata, and adherence to the stated privacy position. Searches run measures activity.

What goes wrong?

Semantic search deployed as a replacement for controlled vocabulary rather than alongside it. Metadata generated and published without review. Personalisation built on retained reading history without an institutional decision. And digitisation programmes that produce images nobody can find.

What does it cost to run?

Modest. Document and image processing for digitisation is the main variable cost; discovery and reference support are inexpensive. The investment is in metadata standards work and in the review capacity that generated metadata requires.

What should you do first?

Take twenty real reader queries that returned nothing and check whether the material existed in the collection. The proportion that did is the discovery gap, and it is usually large enough to make the case immediately.

What about public and academic differences?

Substantial. Public libraries serve a general population with wide-ranging needs and strong community roles; academic libraries serve researchers with deep subject requirements and licensing obligations around scholarly content.

Discovery matters in both and looks different: a public reader needs to find something readable on a topic, a researcher needs everything relevant including material they have not heard of. Building for one and deploying to the other produces a system that serves neither well, and the difference should shape the design rather than being handled by configuration.

How FISTA Solutions helps

FISTA Solutions builds library systems with semantic discovery alongside controlled vocabulary, metadata drafting for cataloguer review, digitisation pipelines that produce usable descriptions, and reference support bounded by an explicit reader privacy position, through AI agents, AI enablement, and web and mobile engineering. The record behind the approach is 150+ projects for 50+ companies across 12+ countries.

To help readers find what you already hold, message FISTA on WhatsApp, or read the enterprise knowledge management whitepaper.