AI introduces competing demands in organizations, creating pressures to balance efficiency and standardization with contextual responsiveness and ethical judgment. Tensions between these competing demands become particularly salient when some areas of organizations push for automation while others for augmentation , as two distinct paradigms of AI implementation. Drawing on a nested case study of a European airline, we follow three AI implementations to explore how higher order properties of the institutional environment shape how actors configure trust and distrust in AI systems in response to two coexisting institutional logics: instrumental–analytic and contextual–normative . We show how these two logics stimulate different trust–distrust configurations, which in turn guide how AI is implemented and adopted within organizations. We identify two reconciliation practices that help organizational actors manage inherent tensions between these competing institutional pressures: mindful evaluation and proactive safeguarding . The research reveals how AI implementation and adoption reflect conflicts between dominant institutional logics and contributes with a novel perspective on the role of institutional logics and trust in projects of AI implementation. • AI brings competing demands to organizations for either efficiency and standardization or contextual responsiveness and ethical judgment. • Some areas of organizations push for automation while others for augmentation. • Actors configure trust and distrust in AI systems in response to two coexisting institutional logics: instrumental–analytic and contextual–normative. • These two logics stimulate different trust–distrust configurations, which then guide how AI is implemented and adopted within organizations. • Two reconciliation practices help organizational actors manage inherent tensions between these competing institutional pressures: mindful evaluation and proactive safeguarding .
Giannitsas et al. (2026) studied this question.