Research on human–AI collaboration in professional settings has focused primarily on isolated interactions between knowledge professionals and AI, usually at the point of decision making. Yet generative AI broadens these interactions into an ongoing conversational process that unfolds across the full workflow. In a field study of 244 global management consultants from the Boston Consulting Group, we investigated the human–GenAI decision-making workflow and found that knowledge professionals engaged in three distinct modes of human–AI knowledge co-creation: Fused Knowledge Co-Creation, Directed Knowledge Co-Creation, and Abdicated Knowledge Co-Creation. These modes correspond to three types that emerged in practice: “Cyborgs,” “Centaurs,” and “Self-Automators.” Across these modes, we identify two fundamental questions that structure any collaborative problem-solving dynamic between human and machine: Who selects what needs to be done? and Who identifies how it gets done? These questions reveal the underlying architecture of human–AI collaboration, showing not only what GenAI is used for but also who steers the workflow and how the division of labor unfolds in practice. As a result of these emergent modes of working with GenAI, Centaurs were upskilling themselves and increasing their current domain expertise. Cyborgs were newskilling themselves in GenAI-related capabilities, acquiring a new area of AI expertise. Self-Automators were not increasing either their domain expertise or AI expertise. We show how what seems to be the same “human-in-the-loop” workflow for decision making is actually enacted in three ways with significantly different implications for skilling and the future of work.*