Application of Generative Artificial Intelligence in the Mapping and Analysis of Critical Knowledge: A Case Study in the Oil and Gas Sector

Denilson Sell, Luciana Poli Silva, Arturo Cavalcanti Catunda and Tayane Cristina Mattera Souza

Chapter 3 in Knowledge Management and Leadership in the AI Era:Harnessing Technology for Innovative Organizations, 2026, pp 51-74 from World Scientific Publishing Co. Pte. Ltd.

Abstract: Organizations face increasing challenges in managing their most valuable asset: knowledge. The complexity of identifying and analyzing critical knowledge, ensuring assertiveness in investments, and maximizing value creation motivates this study. In response, a method was created to guide the mapping and analysis of an organization’s critical knowledge, aiming to better direct knowledge management programs and ensure an effective contribution to strategy and institutional deliverables. The method was developed based on design science research, through a literature review of knowledge audit approaches, an analysis of proven practices, and experimentation with generative artificial intelligence (GenAI) in various tasks related to planning, mapping, and knowledge analysis, as well as the development of knowledge management strategies. This chapter aims to demonstrate, through a case study conducted at Universidade Petrobras – the corporate university of Petrobras, one of the world’s largest energy companies and an international reference in deepwater oil exploration – how GenAI can integrally support the different stages of a knowledge audit based on the established method. The initial results show significant efficiency gains in identifying organizational needs, greater accuracy in categorizing knowledge assets, and agility in generating strategic recommendations, confirming the potential of GenAI to expand the reach and effectiveness of knowledge audits in large organizations.

Keywords: Business; Artificial Intelligence; Knowledge Management; Information Management; Leadership; Decision Making; Data Analysis (search for similar items in EconPapers)
JEL-codes: L23 M15 O32 O33 (search for similar items in EconPapers)
Date: 2026
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