This study explores the utilization of Large Language Models (LLMs) in managing Design Science Research (DSR) knowledge. It addresses the absence of a consolidated knowledge base for DSR artifacts, proposing an AI-driven approach for extracting and structuring information from scientific articles. Three experiments were conducted to evaluate the effectiveness of LLMs — specifically GPT 3.5 and GPT 4 — when the requested information is 1) spread across multiple articles, 2) contained within a single article, and 3) well-structured inside a single file. Our results indicate the potential of AI tools in efficient information extraction and real-time data processing. However, they also underscore the limitations of current AI models, such as underperforming when extracting DSR-related information spread across many articles. Overall, our research contributes to the understanding of AI capabilities in enhancing DSR knowledge management while identifying promising areas for further exploration.