Generative AI and the Architecture of Knowledge Work

Expertise Bypass, Exchange Governability, and Shifting Bottlenecks

Research on generative AI (GenAI) and work has largely focused on tasks. We ask when task-level effects alter knowledge-work industry architecture: the allocation of activities across firm boundaries. Using a three-stage abductive design – an executive roundtable, a panel of 626 firm websites (2017–2025), and 33 interviews plus two further roundtables with C-level executives and corporate directors – we document divergent repositioning: professional services firms foreground relational value, while interface-oriented technology firms emphasize scalable interfaces.

We find that activity reallocation is shaped by two independently varying dimensions: expertise bypass and exchange governability. Their intersection yields four configurations: contestable reallocation, persistent bundles with selective insourcing, federated expert provision, and integrated expert provision.

We extend industry architecture theory by explaining change when production and exchange conditions shift separately.

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