Introduction to AI in Libraries: Concepts, Opportunities & the New Knowledge Frontier

By Niranjan Mohapatra, World Skill Center
Series: AI Transformation in Libraries (Part 1 of 10)

Libraries are entering a new era of innovation—one shaped not by shelves or digital databases alone but by Artificial Intelligence (AI). From intelligent search to autonomous cataloguing, AI is rapidly transforming how libraries operate, support users, and envision their future role in society.

This first blog in the series explores what AI really means, why it matters for libraries, and how it is redefining the modern information ecosystem.

 

1. What Is Artificial Intelligence in the Library Context?

Artificial Intelligence refers to computer systems capable of performing tasks that typically require human intelligence. In libraries, AI is not limited to robots or chatbots—it spans a spectrum of technologies that process, analyze, recommend, and even generate information.

Core AI Technologies Relevant to Libraries

• Machine Learning (ML) – systems that learn from patterns (e.g., predicting book demand)
• Natural Language Processing (NLP) – understanding and generating human language (e.g., automated indexing)
• Computer Vision – identifying images or documents (e.g., smart scanning tools)
• Generative AI – creating text, summaries, translations, or metadata
• Recommendation Engines – like those used in e-commerce to suggest books or research resources
• Automation & Robotics – for circulation, sorting, or inventory management

Together, these technologies support a new generation of smart library services.

 

2. Why Libraries Need AI: The 5 Transformation Drivers

1. Growing Information Overload

Research publications, digital resources, and data repositories have grown exponentially. AI helps librarians navigate massive volumes of information.

2. Need for Faster, Personalized Services

Users expect instant answers, customized recommendations, and 24×7 virtual help—AI makes these possible.

3. Rising Demand for Research Support

AI-powered research discovery tools, impact analytics, and literature review assistants are becoming indispensable in academic settings.

4. Library Workforce Evolution

Routine tasks like classification, metadata entry, and repetitive user queries can be automated, allowing librarians to focus on higher-level roles.

5. Digital Transformation & Post-COVID Expectations

Automation, remote access, and hybrid models require smarter infrastructure—and AI fills this gap efficiently.

 

3. How AI Is Already Transforming Libraries (Real Use Cases)

a. AI Chatbots & Smart Reference Services

Libraries now deploy AI chatbots capable of handling thousands of queries—from “Where can I find this book?” to “How do I cite this article?”

b. AI-Driven Cataloguing & Metadata Enhancement

Tools like automated subject indexing, entity extraction, and MARC enhancement streamline technical processing.

c. Predictive Collection Development

Machine learning analyzes circulation patterns and user behavior to recommend what the library should acquire next.

d. Intelligent Search & Discovery

AI improves OPAC and discovery interfaces by showing relevant, personalized search results.

e. AI Tools for Researchers

• Semantic Scholar
• Elicit.org
• scite.ai
• OpenAlex
  These platforms assist with topic discovery, literature mapping, citation analysis, and research writing.

 

4. What AI Means for the Role of Librarians

AI is not replacing librarians—it is redefining their skills.

Shifting from Routine to Strategic Roles

AI automates:

• Data entry
• Basic cataloguing
• Repetitive reference queries
• Pattern analysis

This allows librarians to evolve as:

• Research consultants
• Data literacy trainers
• Digital curators
• Ethical AI mediators
• Knowledge strategists

The librarian becomes more valuable—not less.

 

5. Challenges & Ethical Considerations

AI adoption also comes with important concerns:

a. Data Privacy

Users’ behavior patterns, search histories, and biometrics may be collected if not controlled.

b. Algorithmic Bias

AI tools may unintentionally promote biased content or skewed recommendations.

c. Transparency

Libraries must clearly explain what AI tools are used and what data they collect.

d. Skill Gaps

Many LIS professionals require upskilling in AI literacy, data analytics, and machine learning basics.

e. Cost & Infrastructure

Not all libraries can invest in high-end AI systems.

A balanced and ethical approach is essential.

 

6. The Road Ahead: What This Series Will Cover Next

Over the next nine posts, this series will explore how AI reshapes every dimension of library services, including:

• Reference services
• Cataloguing & metadata
• Collection development
• Information literacy
• Generative AI applications
• Research support
• Ethical issues
• Librarian skills & training
• Future predictions

Together, these articles will build a practical, research-oriented guide for libraries transitioning into AI-powered knowledge hubs.

 

Conclusion

AI is not a distant future—it is already here, reshaping how libraries store, manage, discover, and curate knowledge. For libraries to remain dynamic, relevant, and user-centered, integrating AI is no longer optional—it is transformational.