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<p>This article examines the growing intersection of Cyber-Physical Infrastructures (CPI) and Artificial Intelligence (AI) in the context of eGovernment, providing a comprehensive overview of the opportunities, challenges, and policy considerations shaping this convergence. It begins by exploring how AI-driven CPI optimizes data-driven policymaking, public service delivery, and operational efficiency, highlighting use cases such as predictive analytics, resource allocation, and cybersecurity. The discussion then addresses key implementation barriers, including data privacy, algorithmic bias, system integration, and regulatory uncertainties, offering targeted solutions for ethical AI adoption, privacy-preserving methods, and robust cybersecurity frameworks. Emphasizing the potential of emerging technologies such as Quantum AI, federated learning, and blockchain, the article outlines a structured roadmap toward an AI-enabled, resilient, and sustainable CPI. The conclusion underscores the importance of proactive policy measures, interdisciplinary collaboration, and transparent governance in ensuring that AI not only drives innovation but also upholds public trust and democratic values.</p>

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Towards AI-Enabled Cyber-Physical Infrastructures—Challenges, Opportunities, and Implications for a Data-Driven eGovernment Theory, Policy, and Practice

  • 期刊论文

  • 发表日期：

    2025年5月1日

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  • Maro Androutsopoulou,
  • Elias G. Carayannis,
  • Dimitris Askounis,
  • Nikos Zotas

摘要

This article examines the growing intersection of Cyber-Physical Infrastructures (CPI) and Artificial Intelligence (AI) in the context of eGovernment, providing a comprehensive overview of the opportunities, challenges, and policy considerations shaping this convergence. It begins by exploring how AI-driven CPI optimizes data-driven policymaking, public service delivery, and operational efficiency, highlighting use cases such as predictive analytics, resource allocation, and cybersecurity. The discussion then addresses key implementation barriers, including data privacy, algorithmic bias, system integration, and regulatory uncertainties, offering targeted solutions for ethical AI adoption, privacy-preserving methods, and robust cybersecurity frameworks. Emphasizing the potential of emerging technologies such as Quantum AI, federated learning, and blockchain, the article outlines a structured roadmap toward an AI-enabled, resilient, and sustainable CPI. The conclusion underscores the importance of proactive policy measures, interdisciplinary collaboration, and transparent governance in ensuring that AI not only drives innovation but also upholds public trust and democratic values.