Enhancing Document Life-Cycle Management with Generative AI and Human-Centered Design

Abstract

Effective management of documents throughout their life cycle is a critical challenge for modern organizations, where volumes of information are growing exponentially. Traditional document management systems often struggle with tasks such as automated classification, summarization, version control, retrieval, and compliance monitoring. Recent advancements in Generative Artificial Intelligence (GenAI), particularly large language models and transformer-based architectures, provide transformative potential for automating and optimizing document life-cycle management (DLM). This paper explores the integration of Generative AI into DLM, emphasizing human-centered workflows, explainability, and operational efficiency. Key capabilities of GenAI—such as automated content generation, intelligent summarization, context-aware retrieval, and semantic indexing—are evaluated for their impact on document creation, approval, storage, distribution, and archival processes. By combining AI automation with human oversight, organizations can achieve enhanced productivity, reduce errors, and ensure compliance with regulatory standards. The paper also discusses challenges such as data privacy, model bias, and adoption barriers, offering a roadmap for future research and deployment of GenAI-driven DLM systems.