Generative organizational learning: Affordances for new modes of knowledge search, creation, transfer, and forgetting with LLMs

Abstract

Scholars of organizational learning typically focus on how organizations operate in an environment of domain knowledge scarcity, assuming that knowledge abundance is a highly sought after, but fundamentally elusive, goal. In our case study of organizational learning at Northeast Health, we find that generative AI, in combination with actors’ goals and organizational institutions, may afford a new form of learning that we call generative organizational learning enabling innovation in the service of organizational goals. In particular, generative catalyzing, iterating, and personalizing may enable abundance rather than scarcity of domain knowledge—both useful and risky. At the same time, actors, institutions, and GenAI’s limitations and capabilities may afford processes of generative curating and guardrailing that facilitate knowledge forgetting to limit the abundance of risky domain knowledge. Our model extends the current understanding of organizational learning by exploring how these new affordances enable the development of scalable solutions with GenAI.

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