Tacit knowledge — personal, experience-based and resistant to codification — is a strategic resource for innovation, yet sharing it remains difficult because it is informal and context-dependent. The difficulty intensifies in fintech start-ups, which combine flat structures, short product cycles, strict regulatory obligations and high staff turnover. In parallel, generative AI chatbots have introduced a conversational interface between employees and organizational knowledge. This paper develops a conceptual framework specifying how such chatbots shape tacit knowledge sharing among Generation Z employees in fintech start-ups. Following a conceptual-analytical approach based on a purposive review of literature published between 1994 and 2026, the study uses the SECI model as an integrating framework. The analysis indicates that generative chatbots differ most sharply from traditional knowledge management tools in externalization, through staged reflective questioning, and in internalization, through Socratic dialogue and scenario simulation; their contribution to socialization is confined to an expert-finder role that raises the frequency of human encounters without performing the conversion itself. Contrary to a common assumption, recent survey evidence shows that adoption among Generation Z has been accompanied by growing doubt about the cognitive consequences of these tools. The framework therefore incorporates user trust and human validation as moderators, and partial substitution of human interaction as an inhibiting effect. Crucially, it specifies task knowledge intensity as the boundary condition determining whether facilitation or substitution prevails. Eight testable propositions and proposed operationalizations open a path to empirical validation.

generative artificial intelligence tacit knowledge sharing SECI model Generation Z fintech start ups