Paper Number
ICIS2025-1627
Paper Type
Complete
Abstract
Developing AI within a silo-based organization presents numerous challenges, including redundancy, resource waste, resistance to change, and missed opportunities for innovation. Utilizing an interventionist research strategy that emphasizes active researcher participation and an interactive research process, we provide insights into deconstructing silo cultures through four sociotechnical interventions: Collective Envisioning, Reframing Positions, Embodied Reflection, and Radical Reorientation. Our case study also shows that these interventions enable possibilities for collective judgment in developing socially purposeful AI without requiring reorganization of existing organizational structures or functions. We argue that collective understanding and judgment of socially purposeful AI evolve through pluralistic dialogue and rely on how the organization enables collective inquiry, reflection and metacontingencies with diverse stakeholders and perspectives.
Recommended Citation
Eriksson Lundström, Jenny; Hylving, Lena; and Koutsikouri, Dina, "Breaking the Silos for Sustainable AI: Imaginary Workshops and Provocative Joint Inquiry in the Swedish Transport Administration" (2025). ICIS 2025 Proceedings. 9.
https://aisel.aisnet.org/icis2025/general_topic/general_topic/9
Breaking the Silos for Sustainable AI: Imaginary Workshops and Provocative Joint Inquiry in the Swedish Transport Administration
Developing AI within a silo-based organization presents numerous challenges, including redundancy, resource waste, resistance to change, and missed opportunities for innovation. Utilizing an interventionist research strategy that emphasizes active researcher participation and an interactive research process, we provide insights into deconstructing silo cultures through four sociotechnical interventions: Collective Envisioning, Reframing Positions, Embodied Reflection, and Radical Reorientation. Our case study also shows that these interventions enable possibilities for collective judgment in developing socially purposeful AI without requiring reorganization of existing organizational structures or functions. We argue that collective understanding and judgment of socially purposeful AI evolve through pluralistic dialogue and rely on how the organization enables collective inquiry, reflection and metacontingencies with diverse stakeholders and perspectives.
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02-GeneralTopics