AI adoption in bureaucracies

Luca Pieroni and Melcior Rossello Roig

Journal of Institutional Economics, 2026, vol. 22, -

Abstract: This paper examines relationships between AI occupational exposure and workforce patterns in U.S. federal agencies from 2019–2024. Using administrative employment data, we document systematic associations between agencies’ concentrations of AI-exposed occupations and employment dynamics. Agencies with higher AI exposure exhibit declining routine employment shares, expanding expert roles, and wage compression effects. We develop a theoretical framework incorporating institutional constraints distinguishing public organisations: employment protections, standardised compensation systems, and political oversight. The model features strategic interactions between budget-maximising directors and electoral-sensitive overseers, predicting workforce evolution under institutional constraints. Our identification exploits fixed occupational exposure scores, so observed changes in agency-level exposure reflect workforce composition shifts rather than measurement artefacts. Patterns suggest agencies with greater AI-susceptible occupations experience reallocation rather than displacement, providing insights for understanding technological change in institutionally constrained environments and informing governance frameworks balancing modernisation with democratic accountability.

Date: 2026
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