AI-Driven Digital Infrastructure and Infrastructure Innovation Systems: A Conceptual Analysis

Authors

  • Nguyen Minh Anh Vietnam National University, Hanoi, Vietnam Author
  • Tran Quang Huy Vietnam National University, Hanoi, Vietnam Author

DOI:

https://doi.org/10.51903/cpapgr89

Keywords:

Artificial Intelligence, Digital Infrastructure, Infrastructure Innovation Systems, Innovation Coordination, Multi-Actor Collaboration

Abstract

The evolution of infrastructure development increasingly relies on integrating digital technologies to support complex, multi-actor innovation systems. Modern infrastructure projects involve governments, construction industries, research institutions, and technology providers, requiring efficient coordination, data integration, and collaborative decision-making mechanisms. This study examines the role of AI-Driven Digital Infrastructure in enhancing coordination and collaboration within Infrastructure Innovation Systems. A conceptual literature analysis was employed to synthesize prior research on AI applications, digital infrastructure, and innovation systems and map relationships between AI capabilities and systemic innovation dynamics. This study adopts a purely conceptual research design based on structured literature synthesis rather than empirical testing. Additionally, illustrative conceptual scenarios demonstrate potential coordination mechanisms that may facilitate information flows, inter-organizational alignment, and collective decision-making. The analysis suggests that AI-Driven Digital Infrastructure may function as a structural enabler contributing to institutional alignment, knowledge integration, and multi-stakeholder interaction within infrastructure ecosystems. The study provides a conceptual framework that links digital intelligence to systemic innovation processes, highlighting the role of AI as a backbone for coordination within complex infrastructure networks. This research contributes to theory by integrating perspectives from digital infrastructure, AI, and Infrastructure Innovation Systems into a unified analytical model. It offers conceptual insights to inform future research on digital infrastructure development.

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References

Alahi, M., Sukkuea, A., Tina, F., Nag, A., Kurdthongmee, W., Suwannarat, K., & Mukhopadhyay, S. (2023). Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends. Sensors (Basel, Switzerland), 23. https://doi.org/10.3390/s23115206

Aldoseri, A., Al-Khalifa, K., & Hamouda, A. M. (2024). AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact. Sustainability. https://doi.org/10.3390/su16051790

Allam, Z., Sharifi, A., Bibri, S., Jones, D., & Krogstie, J. (2022). The Metaverse as a Virtual Form of Smart Cities: Opportunities and Challenges for Environmental, Economic, and Social Sustainability in Urban Futures. Smart Cities. https://doi.org/10.3390/smartcities5030040

Baduge, S., Thilakarathna, S., Perera, J., Arashpour, M., Sharafi, P., Teodosio, B., Shringi, A., & Mendis, P. (2022). Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Automation in Construction. https://doi.org/10.1016/j.autcon.2022.104440

Bibri, E. S., Krogstie, J., Kaboli, A., & Alahi, A. (2023). Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review. Environmental Science and Ecotechnology, 19. https://doi.org/10.1016/j.ese.2023.100330

Bibri, S., Huang, J., Jagatheesaperumal, S., & Krogstie, J. (2024). The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review. Environmental Science and Ecotechnology, 20. https://doi.org/10.1016/j.ese.2024.100433

Bourechak, A., Zedadra, O., Kouahla, M., Guerrieri, A., Seridi, H., & Fortino, G. (2023). At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives. Sensors (Basel, Switzerland), 23. https://doi.org/10.3390/s23031639

Christopher, L., & Grace, A. (2025). Integrating Predictive AI Models to Bridge Energy Efficiency Gaps in Smart Building Design. Civil Engineering Science and Technology (CEST), 1(2). https://doi.org/10.51903/2fwp7m63

Das, D. (2024). Exploring the Symbiotic Relationship between Digital Transformation, Infrastructure, Service Delivery, and Governance for Smart Sustainable Cities. Smart Cities. https://doi.org/10.3390/smartcities7020034

Gill, S., Golec, M., Hu, J., Xu, M., Du, J., Wu, H., Walia, G., Murugesan, S. S., Ali, B., Kumar, M., Ye, K., Verma, P., Kumar, S., Cuadrado, F., & Uhlig, S. (2024). Edge AI: A Taxonomy, Systematic Review and Future Directions. Cluster Computing, 28. https://doi.org/10.1007/s10586-024-04686-y

Handoko, M., Mubarok, H., Shaura, R. K., Widyastuti, R., Swastika, R., Haryanto, W., & Hartini, D. (2025). The Architecture of Intellegent Transportation System based on Sensor Monitoring (Implementation in Jakarta Area). Journal of Technology Informatics and Engineering, 4(2), 190–201. https://doi.org/10.51903/jtie.v4i2.357

Hong, Z., & Xiao, K. (2024). Digital economy structuring for sustainable development: the role of blockchain and artificial intelligence in improving supply chain and reducing negative environmental impacts. Scientific Reports, 14. https://doi.org/10.1038/s41598-024-53760-3

Kaluarachchi, Y. (2022). Implementing Data-Driven Smart City Applications for Future Cities. Smart Cities. https://doi.org/10.3390/smartcities5020025

Khan, A., Laghari, A. A., Li, P., Dootio, M. A., & Karim, S. (2023). The collaborative role of blockchain, artificial intelligence, and industrial internet of things in digitalization of small and medium-size enterprises. Scientific Reports, 13. https://doi.org/10.1038/s41598-023-28707-9

Kulkov, I., Kulkova, J., Rohrbeck, R., Menvielle, L., Kaartemo, V., & Makkonen, H. (2023). Artificial intelligence ‐ driven sustainable development: Examining organizational, technical, and processing approaches to achieving global goals. Sustainable Development. https://doi.org/10.1002/sd.2773

Leite, M., & Silva, B. (2025). AI-Driven Optimization of Project Cost and Duration in Infrastructure Development Projects. Civil Engineering Science and Technology (CEST), 1(2). https://doi.org/10.51903/yemg8d35

Lin, C.-C., Huang, A., & Lu, O. (2023). Artificial intelligence in intelligent tutoring systems toward sustainable education: a systematic review. Smart Learning Environments, 10, 1–22. https://doi.org/10.1186/s40561-023-00260-y

Liu, L., Song, W., & Liu, Y. (2023). Leveraging digital capabilities toward a circular economy: Reinforcing sustainable supply chain management with Industry 4.0 technologies. Comput. Ind. Eng., 178, 109113. https://doi.org/10.1016/j.cie.2023.109113

Martínez-Peláez, R., Ochoa-Brust, A., Rivera, S., Félix, V., Ostos, R., Brito, H., Félix, R., & Mena, L. (2023). Role of Digital Transformation for Achieving Sustainability: Mediated Role of Stakeholders, Key Capabilities, and Technology. Sustainability. https://doi.org/10.3390/su151411221

Nugroho, S. A. A., & Wibowo, A. (2025). Evaluating Digital Transformation within Integration Limitations using Desk-Based Analytical Case Study. Journal of Technology Informatics and Engineering, 4(2), 289–299. https://doi.org/10.51903/jtie.v4i2.365

Omrany, H., Al-Obaidi, K., Husain, A., & Ghaffarianhoseini, A. (2023). Digital Twins in the Construction Industry: A Comprehensive Review of Current Implementations, Enabling Technologies, and Future Directions. Sustainability. https://doi.org/10.3390/su151410908

Petrova, S., & Watanabe, K. (2025). User-Centered Mobile Navigation: Evaluating Local Usability for Improved UX. Journal of Technology Informatics and Engineering, 4(3), 478–492. https://doi.org/10.51903/jtie.v4i3.457

Qin, T., Wang, L., Zhou, Y.-N., Guo, L., Jiang, G., & Zhang, L. (2022). Digital Technology-and-Services-Driven Sustainable Transformation of Agriculture: Cases of China and the EU. Agriculture. https://doi.org/10.3390/agriculture12020297

Saeed, S., Altamimi, S. A., Alkayyal, N. A., Alshehri, E., & Alabbad, D. A. (2023). Digital Transformation and Cybersecurity Challenges for Businesses Resilience: Issues and Recommendations. In Sensors (Vol. 23, Number 15). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/s23156666

Schmitt, M. (2023). Securing the digital world: Protecting smart infrastructures and digital industries with artificial intelligence (AI)-enabled malware and intrusion detection. J. Ind. Inf. Integr., 36, 100520. https://doi.org/10.1016/j.jii.2023.100520

Supriadi, C., Wahyudi, W., Priyadi, A., & Jin, K. S. (2025). Decentralized AI on The Edge: Implementing Federated Learning for Predictive Maintenance in Industrial IoT Systems. Journal of Technology Informatics and Engineering, 4(2), 317–336. https://doi.org/10.51903/jtie.v4i2.281

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Published

2026-03-25

Issue

Vol. 2 No. 1 (2026): March | CEST (Civil Engineering Science and Technology)

Section

Articles

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