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The Self-Driving State
AI-Driven Governance and the Transformation of Public Administration
von Florian Schnitzhofer, Patrick Pils, Philipp Seper-Ambros
Reihe: Professional Practice in Governance and Public Organizations
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Beschreibung
The Self-Driving State
AI-Driven Governance and the Transformation of Public Administration
von Florian Schnitzhofer
This book introduces the concept of a „self-driving state“, a vision of governance in which traditional bureaucratic structures are replaced by highly automated, service-oriented administrative systems. This model emphasizes seamless efficiency and responsiveness, redefining the relationship between the state and its citizens through advanced digital technologies.
Drawing on decades of advisory experience in the public sectors of Germany and Austria, the authors present a forward-looking framework for AI-driven administration. They trace the evolution from classical bureaucratic principles to autonomous decision-making and offer a systematic roadmap for implementing this transformation.
This book addresses policymakers, professionals, and scholars of public governance and explores how innovations such as digital legislative twins, optimized administrative workflows, and enhanced citizen services can improve quality of life and strengthen economic competitiveness. Through numerous applied examples, the authors demonstrate how the self-driving state can transition from concept to reality, providing both theoretical foundations and practical guidance for progressive public administration.
This book introduces the concept of a „self-driving state“, a vision of governance in which traditional bureaucratic structures are replaced by highly automated, service-oriented administrative systems. This model emphasizes seamless efficiency and responsiveness, redefining the relationship between the state and its citizens through advanced digital technologies.
Drawing on decades of advisory experience in the public sectors of Germany and Austria, the authors present a forward-looking framework for AI-driven administration. They trace the evolution from classical bureaucratic principles to autonomous decision-making and offer a systematic roadmap for implementing this transformation.
This book addresses policymakers, professionals, and scholars of public governance and explores how innovations such as digital legislative twins, optimized administrative workflows, and enhanced citizen services can improve quality of life and strengthen economic competitiveness. Through numerous applied examples, the authors demonstrate how the self-driving state can transition from concept to reality, providing both theoretical foundations and practical guidance for progressive public administration.
Highlights
- Shows how AI-driven governance can replace traditional bureaucracy with seamless, citizen-focused services Presents the transition from classical administration to autonomous decision-making through practical frameworks Demonstrates how digital legislative twins and automated workflows enhance efficiency and public trust
Über den Autor
Florian Schnitzhofer is a visionary and management consultant for AI and software, founder of Sysparency, owner of ReqPOOL group and investor in highly scalable software and AI companies.
Patrick Pils is an Austrian AI and technology expert who, as founder and CEO of LETO SPACE GmbH, drives digital transformation through satellite-based technologies.
Philipp Seper-Ambros is an experienced Austrian management consultant for software and managing director and managing partner for the public sector at ReqPOOL.
Zusätzliche Informationen
| Maße | 23,5 × 15,5 cm |
|---|---|
| ISBN | 978-3-662-74176-4 |
| Verlag | Springer Berlin |
| Reihe | Professional Practice in Governance and Public Organizations |
| Erscheinungsdatum | 29.10.2026 |
| Anzahl Seiten | 229 Seiten |
| Abbildungen | X, 229 p. 23 illus. |
| Autor | Florian Schnitzhofer, Philipp Seper-Ambros, Patrick Pils |
| Übersetzt von | Neil Solomon |
| Sprache | Englisch |
| Zielgruppe | Fach- und Sachbuch |
| Lieferbarkeit | Noch nicht erschienen. Erscheint laut Verlag/Lieferant |
| Datenbasis | 20260807_Onix30_Upd_03 |
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