Paper Type
ERF
Abstract
Due to rapid technological advancement and pressures, public sector organizations often prioritize AI deployment over foundational enterprise data governance and civic data literacy. This rush generates operational legacy friction and biased service outcomes, threatening public trust. Existing literature frequently isolates internal IT constraints from these external societal consequences. To address this fragmentation, this research suggests a causal loop diagram, modeling public sector AI readiness as an endogenous sociotechnical ecosystem. Our conceptual model illustrates how short-term political urgency can crowd out foundational investments, causing cascading degradation across internal capabilities and external legitimacy. This architecture provides a theoretical foundation and methodological roadmap for future stock-and-flow simulations to evaluate policies for sustainable AI readiness.
Paper Number
1657
Recommended Citation
M. Najafabadi, Mahdi; Luna-Reyes, Luis Felipe; and DePaula, Nic, "The Capability-Friction Dynamics: A Macro-Architecture for Public Sector AI" (2026). AMCIS 2026 Proceedings. 5.
https://aisel.aisnet.org/amcis2026/agile/agile/5
The Capability-Friction Dynamics: A Macro-Architecture for Public Sector AI
Due to rapid technological advancement and pressures, public sector organizations often prioritize AI deployment over foundational enterprise data governance and civic data literacy. This rush generates operational legacy friction and biased service outcomes, threatening public trust. Existing literature frequently isolates internal IT constraints from these external societal consequences. To address this fragmentation, this research suggests a causal loop diagram, modeling public sector AI readiness as an endogenous sociotechnical ecosystem. Our conceptual model illustrates how short-term political urgency can crowd out foundational investments, causing cascading degradation across internal capabilities and external legitimacy. This architecture provides a theoretical foundation and methodological roadmap for future stock-and-flow simulations to evaluate policies for sustainable AI readiness.
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