IDEAS home Printed from https://ideas.repec.org/a/gam/jadmsc/v16y2026i2p95-d1862779.html

Autonomous Administrative Intelligence: Governing AI-Mediated Administration in Decentralized Organizations

Author

Listed:
  • Aravindh Sekar

    (College of Business & Information Systems, Dakota State University, Madison, SD 57042, USA)

Abstract

The increasing deployment of agentic artificial intelligence (AI) systems and decentralized digital infrastructures has challenged traditional assumptions about organizational administration, control, and governance. While AI has advanced task-level optimization and decision support, administrative functions such as coordination, compliance, and accountability remain largely centralized and dependent on humans. This paper introduces Autonomous Administrative Intelligence (AAI), a governance-aware AI capability that enables autonomous agents to execute and adapt administrative decisions within strategically defined constraints and decentralized governance mechanisms. Building on the Strategic Decentralized Resilience–AI (SDRT-AI) framework, the study develops a layered architecture and operational flow integrating agentic decision-making, governance-aware learning, and protocol-based validation. The proposed framework explains how strategic intent, organizational capabilities, and decentralized trust jointly enable scalable administrative autonomy while preserving accountability and control. By reframing administration as an AI-mediated governance process, this paper extends research on agentic AI and contributes to administrative science by providing a conceptual foundation for the design and governance of autonomous administrative systems in decentralized organizations.

Suggested Citation

  • Aravindh Sekar, 2026. "Autonomous Administrative Intelligence: Governing AI-Mediated Administration in Decentralized Organizations," Administrative Sciences, MDPI, vol. 16(2), pages 1-17, February.
    • Handle: RePEc:gam:jadmsc:v:16:y:2026:i:2:p:95-:d:1862779
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2076-3387/16/2/95/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2076-3387/16/2/95/
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jadmsc:v:16:y:2026:i:2:p:95-:d:1862779. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.