AI is changing libraries through a collection of tools, not one all-purpose technology. It can help describe collections, improve discovery, translate or summarize information, and handle some routine work. Library chatbots may answer basic questions, but they are not substitutes for librarians: whether any of these tools helps depends on accuracy, privacy, accessibility, staff oversight, and a clear way for people to get human assistance.
What “AI in libraries” includes
AI may be built into a library’s catalog, discovery system, research database, campus platform, or vendor service, as well as used in a dedicated application. The American Library Association (ALA) uses a broad definition that covers systems that generate, classify, rank, recommend, summarize, predict, automate, or assist decisions. A feature may therefore use AI even when patrons encounter it simply as a search box, recommendation, or help service.
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The International Federation of Library Associations and Institutions (IFLA) identifies applications including collection description, metadata work, discovery and literature-review support, institutional chatbots, and robotic process automation. These are possible uses, not evidence that every library has adopted them or that a given system performs well.
Where AI and bots can change library work
Finding and recommending information
AI-enabled search and discovery tools can rank results, recommend material, summarize information, or help retrieve relevant sources. Their value depends on whether the results are useful and fair across subjects and communities. A fluent summary or high-ranking result is not, by itself, proof that information is accurate, complete, or appropriate for a research question.
#1 Best Overall
Describing collections and improving metadata
Tools can assist with creating or enriching catalog metadata and making collections machine-readable. This may help users discover materials that are difficult to describe or process at scale. IFLA discusses these as potential applications; the guidance does not establish that generated descriptions are consistently accurate or that human review can be removed. Libraries need a way to check and correct records, particularly where description affects access or the representation of people and communities.
Supporting digitization, language access, and accessibility
Transcription, translation, description, and summarization can make some materials easier to use. ALA also identifies draft plain-language summaries, alt text, language aids, and translation support as possible uses. Those outputs should be reviewed when errors could change a reader’s understanding, affect official communication, or interfere with access to services. Accessibility is a design and review requirement—not an automatic result of adding AI.
Answering routine questions
A library chatbot or virtual assistant might help with basic queries or direct someone to the right service. ALA recommends that public-facing systems meet recognized accessibility standards, account for different languages and literacy levels, and make it clear how to reach a person. It cautions libraries against replacing reference, readers’ advisory, instruction, or community support with AI systems. Questions that require judgment, subject knowledge, empathy, or an understanding of a patron’s circumstances need a human route.
Rank #2
Automating internal processes
Robotic process automation and other tools may assist with routine backend tasks, including initial metadata creation. Any claim that automation saves time should be tested against the time required for monitoring, fixing errors, integrating the system, and changing workflows. ALA recommends assessing employment and workflow effects and consulting staff who are affected before making changes.
Helping patrons understand AI
IFLA’s survey included promoting AI and data literacy as an area of planned or active work. Libraries can draw on their educational role to help patrons evaluate generated answers, verify sources, and recognize limitations. That is an emerging direction, not evidence that all libraries currently provide AI-literacy programs.
What a limited 2023 survey can—and cannot—show
IFLA’s 2023 working document, Developing a library strategic response to Artificial Intelligence, reports on a survey of 111 higher education, further education, and health librarians. The number of respondents varied slightly across questions. The figures below describe those respondents’ reported plans and activity; they are not adoption rates for all libraries, countries, or library types, and they do not establish current global uptake.
| Activity reported | Planned | In pilot | Mature |
|---|---|---|---|
| Library-specific chatbot | 22 respondents (20%) | 12 (11%) | 7 (6%) |
| Institutional chatbot | 15 respondents (14%) | 6 (5%) | 8 (7%) |
| Promoting AI and data literacy | 52 respondents (47%) | 18 (16%) | 3 (3%) |
The same survey asked about barriers. Respondents could assess each barrier separately, and the item totals varied; the figures should be read as survey responses, not as a ranked measurement of costs across the library sector.
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IFLA’s 2024 Trend Report, updated on 30 January 2025, and its later thematic guidance address broader questions about libraries and technology, but the reviewed ALA and IFLA materials do not provide a newer representative global census of library AI use. The 2023 survey is a useful snapshot of the respondents’ concerns and plans, not a trend line for all libraries.
What libraries need to weigh before using AI
Privacy and data governance
Before adopting a tool, a library should establish what it collects, retains, shares, or uses to train or improve systems. That can include prompts, searches, circulation details, and other patron information. ALA recommends rigorous privacy and security review, disclosure when a third-party service processes patron data, and ensuring patron data is not used to train models without consent.
Accuracy, bias, and accountability
AI-generated information can be wrong, and ranking or recommendation systems can affect what users see. ALA recommends evaluating bias across the system lifecycle, including data, algorithms, and outcomes in cataloging, reference, recommendations, and patron interactions. Libraries also need a process for reviewing outputs, correcting defects, and routing problems to responsible staff. IFLA notes that weaknesses in generated information can increase demand for trusted information—a reason to preserve, not obscure, the distinction between an automated answer and verified help.
Equitable access and human expertise
A service should be assessed for how it works for people with disabilities, different language and literacy needs, and varying access to technology. ALA’s guidance says public-facing tools should be accessible and offer a clear route to human assistance. Libraries should also consider whether a system could disadvantage marginalized groups or narrow the range of viewpoints surfaced to users. Work requiring empathy, professional judgment, or subject expertise should not be treated as interchangeable with automation.
Staff capacity, cost, and environmental impact
A purchase price is only one part of the commitment. IFLA identifies limited in-house development capacity, commercial product expense, data ownership and quality constraints, and a lack of turnkey library products as obstacles. Procurement, integration, staff training, output review, and continuing technical oversight also require capacity. ALA recommends consulting affected staff and examining labor impacts; verified efficiencies should support better working conditions, staffing capacity, training, or community services rather than be used as a reason for reductions or surveillance.
ALA also recommends considering the environmental lifecycle of AI procurement, including energy, water, and electronic waste, and applying a sufficiency mindset. The reviewed guidance does not give a library-specific emissions figure, so environmental impact is a question to assess for a particular system rather than a number to assume.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical way to assess a library AI service
Whether considering a chatbot, a discovery feature, or an internal workflow tool, a library can use the following questions before procurement and throughout operation:
- Purpose: What concrete patron or staff need does it address, and is AI necessary to meet it?
- Accuracy and correction: What errors are likely, who reviews outputs, and how can a patron or staff member report and correct a problem?
- Privacy and security: What data is collected, retained, shared, or used for training, and what consent and disclosure controls apply?
- Accessibility and inclusion: Does it work for users with disabilities, different languages and literacy levels, and varying technology access?
- Human assistance: Can users readily reach knowledgeable staff, and which services or decisions remain human-led?
- Bias and intellectual freedom: Could ranking or recommendations suppress viewpoints or disadvantage particular communities?
- Operating capacity: What training, technical support, review work, procurement cost, and staff consultation will be needed?
- Transparency and accountability: Does the vendor explain system limits, data sources, and consequential automated decisions—and can the library get defects addressed?
AI is most defensible in library services when it serves a defined purpose, is evaluated with staff and community needs in view, and leaves patrons able to question results and reach a person. The tools may change; privacy, equitable access, openness, accountability, and human expertise remain the standards by which libraries should judge them.
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