Generative AI in Knowledge Auditing: A Systematic Approach to Mapping and Analyzing Critical Knowledge
Keywords:
Critical knowledge, Knowledge auditing, Generative artificial intelligence, Knowledge mapping, Knowledge engineeringAbstract
This study highlights knowledge auditing as a fundamental mechanism for guiding knowledge engineering and management initiatives, placing special emphasis on the application of Artificial Intelligence. Recognizing the challenges organizations face in coordinating knowledge management efforts, optimizing investments, and ensuring value creation, a systematic method is proposed to map and analyze critical knowledge, thereby strategically aligning institutional programs. The research, grounded in Design Science Research principles, draws on a literature review encompassing knowledge auditing and well-established knowledge engineering practices. Leveraging Generative Artificial Intelligence serves as a differentiator, expediting tasks related to knowledge planning, mapping, and analysis, while enabling more effective strategy formulation. An initial application at Petrobras University demonstrated the method’s robustness and breadth, showcasing its potential to enhance efficiency in identifying organizational needs and facilitating more precise strategic decisions. The findings suggest that integrating Generative Artificial Intelligence with the knowledge auditing process streamlines the identification and prioritization of critical knowledge, making it both faster and more reliable. Consequently, knowledge governance, structured through this auditing and analysis procedure, reinforces corporate management and consistently drives institutional value creation.
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