The exponential growth of information worldwide has rendered entirely manual cataloging, classification, and metadata creation economically and logistically unsustainable. Conversely, the rush toward fully autonomous, AI-driven knowledge organization systems has proven equally problematic, as these systems are beset by algorithmic hallucinations, contextual blindness, and the implicit encoding of historical biases. Consequently, the discipline of library and information science finds itself trapped in a false dichotomy between the unscalable bottleneck of human labor and the unreliable opacity of machine autonomy. This editorial argues that Human-in-the-Loop (HITL) in knowledge organization is not a concession to technological limitations, but rather a strategic cognitive partnership that leverages both machine capabilities and human nuance. This paradigm transcends a mere quality-control mechanism, emerging instead as a foundational philosophy for knowledge organization in the algorithmic age. Drawing upon empirical evidence from evaluation studies conducted at the Library of Congress, the German National Library of Economics (ZBW), and the Shanghai Library, this study demonstrates that while automated systems perform poorly in subject heading assignment and classification number allocation, with accuracy rates below 50%, HITL workflows can achieve near-perfect accuracy (approaching 100%). The application of this paradigm across five core domains—namely cataloging, classification, indexing, metadata, and ontology—is briefly addressed. The editorial concludes by analyzing the key challenges of implementing HITL in knowledge organization, including intervention timing, training data quality, and organizational adaptation, and argues that the future of the field lies not in human versus machine, but in human-directed AI systems—an approach that guides the enduring profession toward a renewed and empowered purpose in the age of artificial intelligence.