Effective item categorization is vital for businesses, enabling the transformation of unstructured datasets into organized categories that streamline inventory management. Despite its importance, item categorization remains highly subjective and lacks a uniform standard across industries and businesses. The United Nations Standard Products and Services Code (UNSPSC) provides a standardized system for cataloguing inventory, yet employing UNSPSC categorizations often demands significant manual effort. This paper investigates the deployment of Large Language Models (LLMs) to automate the classification of inventory data into UNSPSC codes based on Item Descriptions. We evaluate the accuracy and efficiency of LLMs in categorizing diverse datasets, exploring their language processing capabilities and their potential as a tool for standardizing inventory classification. Our findings reveal that LLMs can substantially diminish the manual labor involved in item categorization while maintaining high accuracy, offering a scalable solution for businesses striving to enhance their inventory management practices.