Reimagining technical services in university libraries: Leveraging AI for efficient resource management

Abstract

REIMAGINING TECHNICAL SERVICES IN UNIVERSITY LIBRARIES: LEVERAGING AI FOR EFFICIENT RESOURCE MANAGEMENT

Hussaini Musa

1. Introduction

The Technical Services Department is responsible for acquisitions, cataloguing, classification, metadata creation, and processing and has traditionally been the “backbone” of university library operations. However, in the AI era, automation, machine learning, and intelligent metadata systems are transforming workflows, reducing manual repetition, and introducing predictive and adaptive tools. This chapter will explore how technical services units can evolve from purely operational units to innovation hubs that power seamless access and discovery in academic libraries.

The Technical Services Department of a university library traditionally responsible for acquisitions, cataloguing, classification, metadata creation, and the processing of information resources forms the operational backbone of library functions. While public services departments engage directly with patrons through reference, circulation, and instructional services, technical services work largely behind the scenes to ensure that resources are accurately described, systematically organized, and efficiently processed for seamless discovery and access (Intner & Weihs, 2019; Taylor & Joudrey, 2018). Without effective technical services, even the most extensive library collection risks being invisible to its intended users.

In the artificial intelligence (AI) era, these long-standing responsibilities are undergoing unprecedented transformation. Advances in machine learning (ML), natural language processing (NLP), computer vision, and knowledge graph technologies have begun to reshape how libraries manage their back-end operations (Yu & Qin, 2023). Routine, repetitive tasks such as descriptive cataloguing, subject indexing, and metadata cross-walking that once required hours of meticulous human effort