AI Agent Operational Lift for The Ohio State University Libraries in Columbus, Ohio

Why AI matters at this scale

Ohio State University Libraries is one of the largest academic library systems in the United States, serving over 60,000 students and thousands of faculty. With a staff of 201–500 and a budget in the tens of millions, it manages vast collections—millions of volumes, extensive digital repositories, and special archives. This size band represents a sweet spot for AI adoption: large enough to have dedicated IT and digital initiatives teams, yet agile enough to pilot and iterate without the bureaucratic inertia of mega-enterprises. For academic libraries, AI is not about replacing librarians but augmenting their ability to connect users with knowledge efficiently. At this scale, even modest efficiency gains translate into significant service improvements and cost savings.

Three High-Impact AI Opportunities

1. Intelligent Discovery and Search The library’s discovery layer (likely Primo or similar) can be enhanced with semantic search and natural language processing. Instead of keyword matching, users could ask, “Find me recent articles on climate change impacts on Midwest agriculture,” and receive highly relevant results. ROI comes from reduced time-to-discovery for researchers and decreased bounce rates from failed searches. Implementation can leverage open-source models like BERT fine-tuned on scholarly metadata, with minimal licensing costs.

2. Automated Metadata and Cataloging Special collections and digital archives often lack rich metadata. AI can auto-generate descriptions, tags, and transcripts for images, audio, and video. This reduces the backlog of uncataloged materials and makes hidden collections discoverable. For a library with extensive archives, the labor savings alone can justify the investment, freeing staff for higher-value curation and outreach.

3. AI-Powered Research Support Services A chatbot trained on library FAQs, LibGuides, and policy documents can handle 30–40% of routine reference inquiries, especially after hours. Additionally, offering text and data mining services to researchers—using AI to extract entities, summarize texts, or analyze sentiment—can position the library as a critical partner in digital scholarship. Revenue or grant funding can offset costs, and the service differentiates the library in a competitive academic landscape.

Deployment Risks Specific to This Size Band

Mid-sized academic libraries face unique risks: limited in-house AI expertise, tight budgets that require clear ROI before scaling, and a strong ethical mandate around privacy and bias. Staff may fear job displacement, so change management is crucial. Start with low-risk, high-visibility pilots (like a chatbot) to build confidence. Ensure all AI tools comply with library privacy standards—avoid logging user queries or using patron data for training without consent. Finally, budget for ongoing model maintenance and staff upskilling to prevent shelfware. With careful planning, Ohio State University Libraries can become a model for AI-enabled academic libraries.