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In artificial intelligence (AI) we (dis)trust? Navigating institutional pressures for automation and augmentation in the implementation of AI in organizations
Citation. Giannitsas, D., Sun, R., & Baptista, J. (2026). In artificial intelligence (AI) we (dis)trust? Navigating institutional pressures for automation and augmentation in the implementation of AI in organizations. Information and Organization, 36(1), 100609. https://doi.org/10.1016/j.infoandorg.2026.100609
Summary. This qualitative nested case study follows three AI implementations in a European airline to examine how institutional pressures shape automation and augmentation choices. It argues that instrumental–analytic and contextual–normative logics configure organizational trust and distrust in AI, rather than those orientations being only individual attitudes or technical assessments. The authors identify mindful evaluation and proactive safeguarding as practices for reconciling efficiency with ethical and contextual demands.
Key claims
- Automation and augmentation are institutionally shaped implementation trajectories that organizations often combine across tasks and decision components rather than adopt as pure alternatives.
- Instrumental–analytic priorities for calculative rationality and replicable performance, and contextual–normative priorities for situational appropriateness and ethical accountability, generate distinct trust–distrust configurations around AI.
- In the case evidence, mindful evaluation and proactive safeguarding helped actors reconcile competing institutional pressures during implementation.
Evidence & limitations
- Evidence comes from a qualitative nested case study of three operational AI implementations at one European airline; it supports theory building rather than prevalence or causal-effect estimates.
- The published article is open access under CC BY; the raw Drive artifact was deterministically replaced with the verified 25-page repository PDF after the staged Markdown artifact proved to be a DOI 404 stub.