This paper explores the integration of artificial intelligence (AI) into knowledge management (KM) practices within energy enterprises, highlighting both the challenges and opportunities arising from sector-wide transformation. Amid growing pressure to decarbonize, modernize legacy infrastructure, and preserve critical expertise during workforce shifts, energy companies must adopt more adaptive and intelligent approaches to managing knowledge. We first outline the contemporary challenges facing the sector, including fragmented data systems, skill gaps, and cultural resistance to AI adoption. We then examine the growing need for KM solutions that can capture institutional knowledge, support faster decision-making, and enable cross-functional collaboration. The paper reviews current applications of AI in the energy industry such as predictive maintenance, digital twins, and semantic search tools demonstrating how AI is already enhancing operational efficiency and strategic alignment. Building on these insights, we propose four key directions for further AI integration into KM: codification of tacit knowledge, context-aware knowledge retrieval, personalized learning and upskilling, and automated content governance. Taken together, these elements show how AI-driven KM can become a foundation for resilience, innovation, and long-term competitiveness in the energy sector. The article concludes with practical insights and best practices for organizations seeking to transform scattered data and expertise into a shared, strategic knowledge infrastructure.