Navigating complexity: A relational perspective on generative AI adoption in government

Abstract This research note presents preliminary findings from exploratory research examining how Australian public servants understand and use generative AI (GenAI) in government work. Drawing on 37 interviews across 22 agencies, we highlight the importance of a relational view of GenAI adoption, that is, the co‐creation of meanings that emerges from the relations between users, technologies, and organisational contexts. Based on our empirical work, we offer three propositions as an initial research agenda: GenAI's equivocal nature triggers fragmented sensemaking processes across agencies; GenAI's affordances become entangled with bureaucratic institutional norms that require negotiation; and GenAI adoption necessitates reconstituting public sector legitimacy. These findings challenge narratives of straightforward technological integration, demonstrating that GenAI adoption is a fundamental renegotiation of core public sector work aspects. Points for practitioners Generative Artificial Intelligence (GenAI) promises to deliver a number of positive impacts on productivity, and governments are under pressure to use these tools. However, there is a range of risks that these tools raise for how governments work. Public agencies are grappling with how to enable controlled experimentation with GenAI while maintaining accountability frameworks. GenAI adoption fundamentally involves relational processes that require moving beyond treating technology as a fixed entity to examine the evolving relations between users, technologies, and organisational contexts.

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Publication Details

Journal
Australian Journal of Public Administration
Published
2026-04-17
DOI
https://doi.org/10.1111/1467-8500.70044
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00
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article

Navigating complexity: A relational perspective on generative AI adoption in government

Kate Henne, James Connor, Helen Dickinson, Shibaab Rahman et al.
Australian Journal of Public Administration
Ethics and Social Impacts of AI
article

Navigating complexity: A relational perspective on generative AI adoption in government

Kate Henne, James Connor, Helen Dickinson, Shibaab Rahman, Vanessa McDermott
article en

Abstract

Abstract This research note presents preliminary findings from exploratory research examining how Australian public servants understand and use generative AI (GenAI) in government work. Drawing on 37 interviews across 22 agencies, we highlight the importance of a relational view of GenAI adoption, that is, the co‐creation of meanings that emerges from the relations between users, technologies, and organisational contexts. Based on our empirical work, we offer three propositions as an initial research agenda: GenAI's equivocal nature triggers fragmented sensemaking processes across agencies; GenAI's affordances become entangled with bureaucratic institutional norms that require negotiation; and GenAI adoption necessitates reconstituting public sector legitimacy. These findings challenge narratives of straightforward technological integration, demonstrating that GenAI adoption is a fundamental renegotiation of core public sector work aspects. Points for practitioners Generative Artificial Intelligence (GenAI) promises to deliver a number of positive impacts on productivity, and governments are under pressure to use these tools. However, there is a range of risks that these tools raise for how governments work. Public agencies are grappling with how to enable controlled experimentation with GenAI while maintaining accountability frameworks. GenAI adoption fundamentally involves relational processes that require moving beyond treating technology as a fixed entity to examine the evolving relations between users, technologies, and organisational contexts.

Australian Journal of Public Administration
Australian National University (AU), ACT Government (AU)
Decent work and economic growth
Openalex Percentile: Top 9%
Ethics and Social Impacts of AI
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