Exploring Knowledge Management Approaches to Enhance Documentation Management within the AI realms

Artificial intelligence (AI) technologies have an increasing impact on companies' digital transformation. On the one hand, it is expected that AI tools can support process automation and quality improvement, on the other hand, many professionals fear that AI systems will displace their roles. It is assumed that paperwork processes and workflow documentation are among the most tedious tasks for employees. However, proper workflow documentation is the backbone of effective company knowledge management (KM) and decision-making. In contrast with traditional documentation management systems (DMS) that struggle with retrieval, accessibility, and workflow automation, KM challenges hinder documenting and sharing insights gained during projects, with knowledge often hidden or lost post-project. Overcoming these barriers requires AI-driven systems to contextualize and structure unstructured knowledge for better inter-project collaboration and retention. By investigating KM approaches, the present research aims to identify and model the key features that will support job augmentation with intelligent document management processing. The study steps on the Automated Documentation Management System (ADMS) with an integrated ChatGPT-based assistant, which enables users to interact with documents through conversational queries, analysis, retrieval, and summarization requests. The first part of the paper explores KM approaches related to workflow documentation and project management. Next, a short overview presents the ADMS system functionalities for improving contextual understanding of the complex processes, data integration, and knowledge sharing. Based on this, a new application model is identified, supporting KM job augmentation. At the end, the discussion explores how advanced DMSs can support KM processes in companies to enhance quality, efficiency, and job augmentation, improving decision-making, collaboration in project management. By enabling seamless knowledge sharing, it preserves insights beyond project lifecycles, supporting future initiatives. As projects grow more complex and data-driven, AI integration in PM can transform how teams capture, share, and apply knowledge for sustained success.