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The Future of Hybrid Intelligence in Administration and Digital Governance

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  • Haris Alibašić

    (University of West Florida, Department of Business Administration)

Abstract

This chapter synthesizes theoretical frameworks, empirical evidence, and international experiences to chart hybrid intelligence trajectories over the coming decade, demonstrating how emerging technologies—generative AI, autonomous agents, quantum computing, surveillance systems, and psychographic targeting—reshape human-AI collaboration and confront unprecedented threats to administrative institutions. The DOGE catastrophe exemplifies the maximization of efficiency without ethical frameworks: mass terminations resulted in billions in costs, caused thousands of preventable deaths, and a collapse of democratic accountability. Project 2025’s blueprint for administrative-state deconstruction through Schedule F reclassification threatens the civil service’s expertise, institutional memory, and independence that effective hybrid intelligence requires. Analysis reveals that generative AI hallucination risks require comprehensive mitigation strategies, while autonomous agents pose governance challenges in the “agentic state”. The EU AI Act’s phased implementation contrasts sharply with U.S. regulatory reversals, creating profound divergence. Risk scenarios spanning democratic backsliding, informational epistemic crisis, operational failures, and institutional capacity collapse require comprehensive anticipatory frameworks. The chapter concludes that genuine hybrid intelligence consistently outperforms pure automation when institutional conditions support responsible implementation. Yet current threats systematically target the professional expertise that distinguishes hybrid intelligence from algorithmic dominance.

Suggested Citation

  • Haris Alibašić, 2026. "The Future of Hybrid Intelligence in Administration and Digital Governance," Public Administration and Information Technology,, Springer.
  • Handle: RePEc:spr:paitcp:978-3-032-28086-2_7
    DOI: 10.1007/978-3-032-28086-2_7
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