In 2026, librarians who understand how AI works will be better equipped to support students and researchers, organise collections, and help patrons find reliable information faster. Developing a few key AI skills can make everyday tasks easier and open up new ways to serve your community.
Why AI Skills Matter for Librarians
AI tools that recommend books, manage citations, or answer basic questions are becoming more common.
Learning how these tools work helps librarians:
- Offer smarter, faster search results.
- Improve cataloguing accuracy.
- Provide better guidance to researchers and students.
Remember, AI isn’t replacing professional judgment; it’s supporting it.
Core AI Literacy Foundations
Before diving into specific tools, it helps to understand some basic ideas behind AI.
Machine Learning Basics:
Machine learning means teaching a computer to recognise patterns in data. In a library setting, this could mean analysing borrowing habits to suggest new titles or resources.
Natural Language Processing (NLP):
NLP is what allows a chatbot or search tool to understand and respond to human language. It’s how virtual assistants can answer questions like “What are some journals about public health policy?”
Quick Terms to Know:
- Algorithm: A set of steps an AI follows to make a decision.
- Training Data: The information used to “teach” an AI system.
- Neural Network: A type of computer model inspired by how the brain processes information.
- Bias: When data or systems produce unfair or unbalanced results.
Metadata Enrichment With AI
Cataloguing is one of the areas where AI makes a noticeable difference.
- Automated Tagging: AI tools can read through titles and abstracts to suggest keywords or subject headings.
- Knowledge Graphs: These connect related materials, for example, linking a book on climate change with recent journal articles on the same topic.
- Bias Checking: Some systems can flag outdated or biased terminology in subject classifications.
Generative Prompt Skills
Knowing how to “talk” to AI tools is a skill in itself. The clearer your request, the better the result. Try experimenting with prompts like these:
- Research Prompt: “List three recent studies on community reading programs and summarise their findings.”
- Teaching Prompt: “Write a short activity plan for a workshop on evaluating online information sources.”
- Summary Prompt: “Give me a brief overview of this article’s key arguments and methods.”
Adjusting tone or adding detail can change the outcome. It’s about learning how to guide the tool rather than letting it guess.
Ethical Data Practices
AI tools can be useful, but they also raise questions about privacy and fairness. Librarians have always cared deeply about protecting patron information, and that remains true with AI.
- Keep personal data anonymous wherever possible.
- Review AI outputs for signs of bias or misinformation.
- Encourage clear policies around how data is stored and used.
Ethical AI is part of a librarian’s duty to maintain trust and fairness.
Automating Everyday Tasks
AI can take over some of the small, routine jobs that fill up a librarian’s day.
- Circulation: Systems can send overdue reminders automatically or manage renewals.
- Chatbots: Basic questions like “What are the library hours?” can be handled instantly.
- Collection Management: AI can spot patterns in borrowing data to suggest which books to keep, reorder, or retire.
Building Your Learning Path
Getting comfortable with AI doesn’t have to mean earning a new degree. Start small:
- Take short online courses or micro-certifications in AI literacy.
- Join librarian groups or online forums where people share practical tips.
- Block out one hour a week to try out a new tool or attend a webinar.
A little consistent learning goes a long way.
Making AI Affordable
Many smaller libraries worry about cost, but not every tool is expensive.
- Free Tools: Some open-access AI platforms, like Zendy, offer affordable access to research databases and AI-powered features.
- Shared Purchases: Partnering with other libraries to share licenses can cut costs.
- Cloud Services: Pay-as-you-go plans mean you only pay for what you actually use.
There’s usually a way to experiment with AI without stretching the budget.
Showing Impact
Once AI tools are in use, it’s important to show their value. Track things like:
- Time saved on cataloguing or circulation tasks.
- Patron feedback on new services.
- How often are AI tools used compared to manual systems?
Numbers matter, but so do stories. Sharing examples, like a student who found research faster thanks to a new search feature, can make your case even stronger.
And remember, the future of librarianship is about using AI tools in libraries thoughtfully to keep libraries relevant, reliable, and welcoming spaces for everyone.