Knowledge Management Methodology for Complex context. The Defence case

This paper identifies and presents accelerated and automated AI solutions for major knowledge management problems in complex systems, especially in defense. From the interviews and surveys conducted on defense person-nel and using defending-against-knowledge-loss triangulation, the study points to the following enduring issues: paperwork, siloed knowledge, se-curity measures, and knowledge obsolescence due to employee turnover. The paper uses thematic analysis using the MAXQDA qualitative data analysis to elucidate the identified inefficiencies as barriers to decision-making and collaborative work that threaten organizational outcomes. In re-sponse to these issues, the study seeks to develop a model offering new so-lutions for the mechanised adoption of knowledge management processes, including knowledge capture, distribution, and enhancement of interopera-bility to support structured knowledge sharing. This model incorporates AI functionalities to automate processes and minimise paperwork to reduce decision-making latency and advance cross-departmental knowledge flow with the added advantage of accessing real-time information while conform-ing to the security requirements through operational access privileges. Keywords: Knowledge management, Complex systems, System engineering, Artificial intelligence, Defense.