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Organizations attempt to enact RAI through guiding principles that aim to minimize threats such as bias and privacy violations and enhance outcomes such as transparency and fairness. In the empirical study we report in this paper, we share insights about how AI experts view RAI principles, including relationships among them, these principles, and the enactment of RAI principles. Employing a sequential mixed-methods approach, we analyse survey data from 135 AI experts from Europe and America using fuzzy-set Qualitative Comparative Analysis (fsQCA), complemented by follow-up interviews. Our analysis identifies five equifinal combinations of RAI principles associated with high overall RAI enactment. The findings reveal the crucial role of accountability and safety-reliability principles and highlight the contextual variability of other principles. 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