The integration of Artificial Intelligence (AI) and Generative Artificial Intelligence (GenAI) is transforming Knowledge Management. Digital technologies are evolving from passive information repositories into active knowledge co-constructors capable of supporting knowledge generation, recombination, dissemination, interpretation, and application. This study examines the emerging relationship among Knowledge Management, AI, and GenAI, focusing on their implications for firms operating in increasingly data-intensive, uncertain, and knowledge-driven environments. A key difference exists between AI-enabled Knowledge Management and current systems. As technology and society evolve, businesses learn, make decisions, generate new ideas, compete, and govern in new ways. This research employs a bibliometric methodology centered on Scopus data, keyword co-occurrence networks, and ensemble MCA-based community detection techniques. A consensus community detection method combines multiple algorithms and merges their outputs using dimensionality reduction and hierarchical clustering to enhance methodological rigor. This approach allows the analysis to move beyond reliance on a single algorithm, identifying more stable thematic structures within the literature. The objective is to uncover the principal thematic cores, novel research trajectories, and uncharted gaps in AI-enabled knowledge creation, dissemination, storage, application, decision support, organizational performance, ethical governance, trust, explainability, and human–AI collaboration. This study contributes to the literature on AI-enabled Knowledge Management by providing a systematic conceptual mapping of a rapidly evolving field. It also offers practical insights for managers and organizations seeking to understand how AI and GenAI can reshape knowledge processes while generating new challenges related to data quality, algorithmic bias, privacy, accountability, organizational readiness, employee skills, and responsible implementation. In this context, AI and GenAI are interpreted as transformative infrastructures through which firms may redesign knowledge ecosystems, augment human expertise, and develop more adaptive, intelligent, and strategically oriented knowledge management capabilities. By examining AI’s potential for boosting creativity, automating tasks, and improving decision-making, we also take into consideration ethical issues like fairness and inclusion. By balancing innovation with responsible practices, businesses can achieve a healthy balance. In a world that is becoming increasingly AI-driven, companies are making sure AI allows them to drive sustainable growth, give employees more power, and fit with long-term strategic goals.