Ai For Enhanced Observability In Complex Information Systems

The digital transformation of organizations has led to an increase in the complexity of information systems (IS). As a result, managing them has become extremely difficult. Traditional observability tools often prove inadequate due to this growing complexity of IS. This study explores the contribution of artificial intelligence (AI) to IS observability by improving knowledge management within organizations. Drawing on the SECI model theory, the research examines how AI can capture and organize knowledge within organizations. The study follows the Design Science Research (DSR) methodology, integrating qualitative studies, artifact implementation and validation. Two main artifacts are developed: the first exploits different AI techniques to analyze IS architecture using existing organizational documentation; the second focuses on knowledge management for the maintenance and evolution of IS. The preliminary results indicate that AI can significantly enhance the observability of IS and contribute to knowledge conversion, thereby enabling more effective governance of complex IS.