AI is creating new choices for library leaders about where human expertise adds the most value
Artificial intelligence in academic libraries is moving from experimentation into everyday practice. Students use AI tools for research and writing, while publishers, discovery services and educational technology providers are adding AI to the platforms researchers and learners rely on. Libraries must now decide where AI can improve services and operations, and where human expertise remains essential.
These decisions come as libraries face growing expectations, limited resources and pressure on staff time. AI may help accelerate routine workflows and create capacity for teaching, research support and other work that depends on professional judgment. But governance, transparency, staff readiness and academic integrity require equal attention.
Research suggests that progress depends less on adopting more tools than on choosing focused uses, setting clear guardrails and building AI literacy. This article examines how academic libraries are using AI, where it is delivering value and what leaders should consider before adoption.
How academic libraries are using AI today
While adoption varies across institutions and regions, libraries around the world are beginning to apply AI to a range of operational and service-oriented activities. Recent research into AI adoption in academic libraries shows that organizations are finding value in targeted, practical applications rather than broad attempts at automation.
Increasing staff productivity
The Academic AI Impact Study shows how libraries are using AI to increase staff productivity in practical, targeted ways. Across metadata and course readiness workflows, participating institutions reported a 30–60% reduction in manual, repetitive work and a two- to four-fold increase in capacity without additional staff. Reading [DW1.1]list creation dropped from 15–45 minutes to 2–5 minutes, while metadata transcription fell from hours to minutes for draft records. Libraries kept professional judgment at the center: 70–90% of AI-generated output was accepted with minor edits, with staff reviewing and refining records and reading lists before approval.
These examples also reflect the distinct role of librarians working behind the scenes in library systems and technical services. Here, AI supports operational tasks such as transcription, parsing and first-draft compilation rather than decisions about students or academic work. It does not raise the thorny questions of academic rigor or integrity. Rather, it automates manual work and creates more time for the work that requires librarian expertise.
AI is also helping library staff spend less time on routine administrative work such as summarization, drafting and information organization. The aim is not simply to complete existing work faster, but to create more capacity for teaching, learning, research support and community engagement.
Supporting research, discovery and AI literacy
Natural-language search, conversational interfaces and AI-assisted research tools are changing how students and researchers find and use information. Libraries can help users apply these tools effectively, evaluate sources and outputs critically and understand where AI may obscure authority, context or provenance.
This work extends the library’s educational mission. By connecting AI literacy with information literacy, research support and academic integrity, libraries can help students and faculty understand both the capabilities and limits of emerging technologies while promoting responsible use.
While the specific applications vary, a common theme is emerging across institutions: AI delivers the greatest value when it addresses clearly defined challenges, operates within established workflows and remains subject to human oversight. The experiences of early adopters suggest that successful implementation relies on identifying the areas where technology can best complement library expertise.
What those areas are, and how libraries can evaluate them responsibly, is the next question facing library leaders.
What library leaders should consider before adopting AI
As interest in AI grows, libraries face a variety of potential applications. New tools promise greater efficiency, improved discovery and enhanced user experiences. Yet the experiences of early adopters suggest that successful implementation depends less on the technology and more on how institutions evaluate opportunities, establish governance and prepare staff for change. Research from both the Academic AI Impact Study and Pulse of the Library highlights an important lesson: libraries making the most progress with AI tend to approach adoption strategically rather than opportunistically.
1. Start with a problem, not a technology
One of the most common mistakes organizations make when evaluating AI is beginning with the tool rather than the challenge it is intended to address. Libraries seeing meaningful results typically start with a specific operational or service problem: cataloging backlogs, course readiness bottlenecks, limited staff capacity or growing user expectations. AI becomes valuable when it helps solve a clearly defined issue, not simply because it is available. This approach also makes success easier to measure. Instead of asking whether an AI initiative was successful in general terms, leaders can evaluate whether it improved productivity, increased service capacity or enhanced user outcomes in a specific area.
2. Keep librarian expertise at the center
AI can automate certain tasks, but libraries remain fundamentally human-centered institutions. Decisions involving collection development, information literacy, scholarly evaluation and user support continue to require professional judgment and expertise.
Research participants consistently described successful AI implementations as partnerships between technology and people. In many cases, AI generated an initial output while librarians reviewed, refined and approved the final result. This model allows institutions to benefit from productivity gains while maintaining quality, accountability and trust.
3. Establish governance before scaling adoption
As AI use expands, governance becomes increasingly important. Without clear policies and decision-making structures, adoption can become fragmented and inconsistent.
The 2026 Pulse of the Library findings point to a clear relationship between governance and implementation maturity. Libraries actively implementing AI are more likely to have formal governance structures and collaboration between library and institutional IT leadership.
Governance does not need to be complicated. However, library leaders should consider who evaluates AI tools, how decisions are made and what principles guide adoption across the organization.
4. Invest in AI literacy
Staff confidence and understanding play a critical role in whether AI initiatives gain traction.
Pulse of the Library findings suggest that libraries providing structured support for AI literacy tend to report greater confidence and deeper implementation.
Developing AI literacy includes understanding how AI systems work, recognizing their limitations, evaluating outputs critically and applying institutional policies consistently. ACRL Choice offers an AI literacy course that covers these topics from the lens of librarianship.
5. Define what success looks like
AI initiatives are often evaluated primarily through the lens of efficiency. While productivity gains can be important, library leaders may wish to take a broader view.
Success might include reducing processing times, increasing access to collections, improving service responsiveness or creating more time for teaching, outreach and research support. In some cases, the most meaningful outcome may be enabling work that was previously difficult to sustain because of resource constraints.
The most successful libraries are making intentional choices about where AI aligns with their mission, supports their users and strengthens their capacity to deliver value.
Alongside these operational and strategic considerations, library leaders must also navigate a broader set of questions surrounding ethics, AI literacy, discovery and the evolving relationship between AI and academic research. These topics are becoming increasingly important as AI adoption matures across the sector.
Related trends shaping the future of AI in libraries
Beyond immediate operational uses, AI is changing how students learn, researchers find information and institutions define responsible scholarship. These shifts raise questions that libraries are well placed to address.
AI literacy now sits alongside information literacy. Students, faculty and staff need to understand how AI systems produce answers, where those answers may be unreliable and how to evaluate them critically. Libraries can connect this work to wider conversations about academic integrity, authentic learning and responsible scholarship, helping institutions move beyond policing tool use toward teaching sound judgment.
AI-mediated discovery brings a related set of concerns. Conversational search and research assistants can open new routes to information, but they can also obscure sources, reproduce bias and make it harder for users to judge authority. Libraries can help institutions evaluate these systems against clear standards for privacy, transparency and accountability while ensuring that trusted resources remain visible.
Frequently asked questions about AI in academic libraries
How are academic libraries using AI?
Academic libraries are using AI for focused tasks such as metadata creation, course list preparation, summarization, drafting, information organization, research assistance and natural-language discovery.
What are the benefits of AI in academic libraries?
When applied to a clear need, AI can reduce repetitive work, shorten processing times, expand service capacity and give staff more time for teaching, research support and community engagement.
What risks should library leaders consider?
Key concerns include privacy, bias, unreliable output, unclear sourcing, fragmented governance and overreliance on automation. Human review and transparent policies help libraries manage these risks.
How can libraries build AI literacy?
Libraries can provide practical training that explains how AI systems work, where they may fail, how to assess their outputs and how institutional policies apply. This instruction can sit alongside information literacy and academic integrity programs.
What does responsible AI adoption look like in a library?
Responsible adoption starts with a defined problem, clear measures of success and governance for evaluating tools and their use. It also keeps librarians involved in reviewing outputs and making final decisions.
Choosing where AI belongs in the academic library
AI is already part of academic library work. The task now is to choose where it belongs, set clear expectations for its use and keep professional judgment at the center. Libraries that do this well can gain capacity without compromising the trust, expertise and service that define their value.
