From Hierarchies to Loops: Rethinking Public Sector Structures for Responsible AI Integration

Sebastian Obeta*, Adoration Chigere, Dr Ikpe Ibanga, Ruth Itua, Linda Oraegbunam, Andrew Chinonso Nwanakwaugwu, Godson Nmesoma Ozioma, Chidinma Anumaka

Received : November 21, 2025 | Published : January 09, 2026

Citation: Obeta, S., Chigere, A., Ibanga, I., Itua, R., Oraegbunam, L., Nwanakwaugwu, A. C., Ozioma, G. N. & Anumaka, C. (2026), ‘From hierarchies to loops: Rethinking public sector structures for responsible AI integration’, Journal of Artificial Intelligence and AI Ethics, 1(1), pp. 1–10. DOI: 10.64978/jaiae.2026.0109001

License: This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0) , which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, provided appropriate credit is given to the original author(s) and the source, a link to the Creative Commons licence is provided, and any changes made are indicated.

Abstract

As artificial intelligence (AI) systems shift from automating tasks to supporting cognitive decision-making, public-sector organisations face both structural and philosophical disruptions. Traditional departmental hierarchies, designed to manage human limitations in complexity, are increasingly misaligned with AI-native workflows, which prioritise speed, feedback, and dynamic abstraction. This paper introduces the concept of “Neural Government Design,” a framework where public organisations function as adaptive cognitive systems with looped information flows, recursive decision structures, and hybrid human-AI reasoning. Drawing on organisational theory, systems thinking, and real-world examples from the NHS and local councils, we examine how AI integration challenges traditional bureaucracy. We then present both hypothetical and existing use cases where looped, feedback-driven models yield more responsive, ethical, and efficient public services. Finally, we propose design principles and metrics for assessing intelligence throughput, collaborative yield, and cognitive compression in AI-enabled government structures. This paper aims to bridge academic theory and public sector transformation, offering actionable insights for researchers, technologists, and civil servants shaping the future of AI governance.

Keywords: artificial intelligence, neural government design, human-AI, AI integration, AI ethics, AI governance