Leveraging Artificial Intelligence for Enhanced Scalability, Accuracy, and Retrieval in Records Management Systems: A Case Study Approach

Abstract

This chapter investigates the application of artificial intelligence (AI) in enhancing records management systems (RMS) by addressing critical challenges such as scalability, accuracy, and retrieval speed. Using a multiple-case study approach, data were collected from three organizations across the healthcare, finance, and government sectors. The findings reveal that AI-driven techniques, including machine learning and natural language processing, significantly improve RMS performance by automating indexing, enabling context-aware retrieval, and reducing processing times. Additionally, AI integration enhanced user satisfaction by streamlining workflows and reducing manual efforts. However, challenges such as data quality, system integration, and ethical concerns were identified, underscoring the need for tailored implementation strategies. This chapter advances academic understanding of AI in data management and offers practical insights for organizations adopting AI-driven RMS to achieve operational efficiency and compliance.