Generative AI and Organizational Structure in the Knowledge Economy
Generative AI (GenAI) is rapidly transforming knowledge work, yet its implications for organizational hierarchies remain poorly understood. Unlike earlier automation technologies, GenAI can both perform tasks autonomously and assist human workers, while its intrinsic fallibility, the tendency to produce confident but incorrect outputs, demands continuous human oversight. We develop a theoretical model to study how GenAI reshapes workforce composition and organizational structure in knowledge-based hierarchies. Our analysis highlights two deployment dimensions, namely mode (automation vs.\ augmentation) and location (worker vs.\ expert layer), which generate a 2X2 design space whose organizational implications are not predicted by traditional technology adoption theories. We obtain three main findings. First, GenAI's effect on entry-level skill requirements is critically mode-dependent. Worker-level automation leads firms to hire fewer but more skilled workers who validate AI outputs and limit costly escalation to experts. Worker-level augmentation, by contrast, expands workers' effective capability, allowing firms to relax entry-level knowledge requirements while sustaining performance. The decline in junior employment documented in recent studies therefore reflects deployment choices favoring automation over augmentation, not an inevitable consequence of GenAI itself. Second, expert-level deployment uniformly lowers entry-level skill requirements, regardless of whether GenAI automates or augments. By expanding experts' capacity to support downstream workers, it enables organizations to employ a broader base of less specialized workers, thereby broadening entry-level access to knowledge work. Third, organizational structure evolves non-monotonically as GenAI improves: across all four deployment architectures, the span of control initially contracts before eventually expanding.