Towards scaling expertise with generative AI: design principles for governing AI-Mediated knowledge capture and transfer

Tacit expertise (e.g. decision heuristics, risk judgement) makes organisational decision-making distinctively human, yet resides in individuals, is hard to scale, and is lost on departure. Generative AI (GenAI) offers conversational elicitation, synthesis, and adaptive scaffolding, but risks hallucination, goal drift, and helpfulness-driven agreement bias, undermining independent judgement. Building on validated problem theory, we derive four design principles grounded in socio-cognitive knowledge-work theory. They specify governance mechanisms addressing persistent transfer challenges (knowledge hiding, articulation barriers, internalisation failure) and GenAI-specific risks (knowledge distortion, tool-dependent learning, disrupted social exchange). Logical reasoning validates for alignment, applicability, coherence, completeness, and operationality. A feasibility assessment across three GenAI configurations shows most mechanisms are implementable through prompting; those requiring persistent state or platform integration are not, with configurations approximating rather than enforcing governance. We contribute prescriptive design knowledge for preserving human-centric decision-making by motivating expert sharing, enabling articulation, establishing fidelity, and ensuring applicability to successors.