The rise of artificial intelligence (AI) is reshaping organizational decision-making at its foundations: data, information, and knowledge. Traditional frameworks, such as the data-information–knowledge–wisdom (DIKW) hierarchy and Choo’s Knowing Cycle, describe how humans engage in sensemaking, process information, and create knowledge to drive decisions. AI now intervenes at each of these stages, not just as a support tool but as an active participant. Delving into this new paradigm, this chapter introduces the artificial knowing cycle, a conceptual framework that integrates AI into knowledge processes, extending traditional and recent models. AI does not merely optimize knowledge structuring; it generates a parallel cycle of artificial data, information, and knowledge, either complementing or replacing human cognition in hybrid, or even fully artificial, knowing cycles. This perspective advances our understanding of AI’s epistemic role, highlighting scenarios where AI autonomously generates, validates, and applies knowledge. The discussion examines the implications of AI-driven knowledge processes for organizations, decision-making, and knowledge management, raising critical questions about the reliability and limitations of artificial knowledge. By positioning AI as an active participant in knowledge formation rather than a passive tool, this chapter provides a foundation for future research on the evolving interplay between artificial and human intelligence in knowledge management and decision-making.