Authors : Adeola Aliu

Volume/Issue : Volume 11 - 2026, Issue 8 - August

Google Scholar : https://tinyurl.com/yc7zn86f

DOI : https://doi.org/10.38124/ijisrt/26aug1364

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The United States is investing heavily in grid modernization, yet the literature guiding these investments is dominated by technical and policy perspectives, with limited attention to how utility infrastructure programs are actually delivered. This narrative review synthesizes project management maturity and organizational competence research, construction change-order and scope management research, and industry and regulatory evidence on right-of-way and permitting friction. From that synthesis I develop a strategic project management framework organized across four program lifecycle phases: scope discovery, sequencing, adaptation, and delivery. The framework is grounded in my experience managing more than thirty concurrent utility infrastructure programs across four states and two wholesale markets. I then propose, as a conceptual and forward-looking extension for future adoption, an AI-enabled decision support layer mapped onto the four phases, drawing on published research in construction natural language processing, delay-risk prediction, automated compliance checking, and cost forecasting. This layer is presented as a direction for future work rather than as a system that has been built, deployed, or validated. The framework is illustrated with an anonymized case and a hypothetical applied example, and implications are drawn for utilities, engineering firms, policymakers, and future research.

Keywords : Grid Modernization; Project Management; Substation Automation; Fiber Optic Infrastructure; Artificial Intelligence; Decision Support; Right-of-Way; Grid Resilience.

References :

1. Infrastructure Investment and Jobs Act, Pub. L. No. 117-58, 135 Stat. 429, 2021.
2. U.S. Department of Energy, “Grid Resilience and Innovation Partnerships (GRIP) Program,” Office of Electricity. [Online]. Available: https://www.energy.gov/oe/grid-resilience-and-innovation-partnerships-grip
3. Federal Communications Commission, Build America: Eliminating Barriers to Wireline Deployments, Notice of Proposed Rulemaking, WC Docket No. 25-253, adopted Jun. 25, 2026. [Online]. Available: https://www.fcc.gov/document/accelerating-wireline-infrastructure-buildout-0
4. M. Panteli and P. Mancarella, “The grid: Stronger, bigger, smarter? Presenting a conceptual framework of power system resilience,” IEEE Power and Energy Magazine, vol. 13, no. 3, pp. 58–66, 2015, doi: 10.1109/MPE.2015.2397334.
5. V. C. Gungor et al., “Smart grid technologies: Communication technologies and standards,” IEEE Transactions on Industrial Informatics, vol. 7, no. 4, pp. 529–539, 2011, doi: 10.1109/TII.2011.2166794.
6. R. Mackiewicz, “Overview of IEC 61850 and benefits,” in 2006 IEEE PES Power Systems Conf. and Exposition, Atlanta, GA, 2006, pp. 623–630, doi: 10.1109/PSCE.2006.296392.
7. Fiber Broadband Association, Permitting for Fiber Network Projects: The Good, the Bad, and the Ugly, Deployment Specialists Committee, 2025. [Online]. Available: https://fiberbroadband.org/wp-content/uploads/2025/08/FBA-Whitepaper_Local-Permitting-for-Fiber-Network-Projects_FINAL-1.pdf
8. Fiber Broadband Association, Fiber Deployment Cost Annual Report, 2026. [Online]. Available: https://www.businesswire.com/news/home/20260120910286/en/Fiber-Broadband-Association-Reports-Rapid-Fiber-Expansion-Amid-Rising-Labor-Materials-Permitting-Costs
9. T. R. B. Taylor, M. Uddin, P. M. Goodrum, A. McCoy, and Y. Shan, “Change orders and lessons learned: Knowledge from statistical analyses of engineering change orders on Kentucky highway projects,” Journal of Construction Engineering and Management, vol. 138, no. 12, pp. 1360–1369, 2012, doi: 10.1061/(ASCE)CO.1943-7862.0000550.
10. R. Al-Marri, G. Abdalla, and E. Mahdi, “Project management maturity in project-based organizations: Frameworks, drivers, and the role of sustainability,” Future Business Journal, vol. 11, art. 245, 2025, doi: 10.1186/s43093-025-00670-z.
11. N. Gareeb and P. D. Rwelamila, “Rethinking project management maturity models for the South African power sector,” African Journal of Science, Technology, Innovation and Development, vol. 14, no. 3, pp. 759–773, 2022, doi: 10.1080/20421338.2021.1899558.
12. H. Kerzner, Using the Project Management Maturity Model: Strategic Planning for Project Management, 3rd ed. Hoboken, NJ: Wiley, 2019.
13. K. Jugdev and J. Thomas, “Project management maturity models: The silver bullets of competitive advantage?,” Project Management Journal, vol. 33, no. 4, pp. 4–14, 2002.
14. R. Al-Marri, G. Abdalla, and E. Mahdi, “A project-based organizational maturity assessment framework for efficient environmental quality management,” Systems, vol. 13, no. 4, art. 289, 2025, doi: 10.3390/systems13040289.
15. N. Gareeb and P. D. Rwelamila, “PESTLE analysis and power sector factors as building blocks in the PMMM concept development: The case of the South African power sector,” Journal of Public Administration, vol. 58, no. 3, pp. 655–670, 2023, doi: 10.53973/jopa.2023.58.3.a9.
16. L. Khalema, C. C. van Waveren, and K.-Y. Chan, “The relationship between project management office maturity and organisational project management maturity: An empirical study of the South African government infrastructure departments,” South African Journal of Industrial Engineering, vol. 26, no. 3, pp. 12–26, 2015, doi: 10.7166/26-3-1021.
17. G. Huang, S.-M. Lee, and D. L. Clinciu, “Competitive advantages of organizational project management maturity: A quantitative descriptive study in Australia,” PLOS ONE, vol. 18, no. 6, art. e0287225, 2023, doi: 10.1371/journal.pone.0287225.
18. M. Gunduz and K. O. Mohammad, “Assessment of change order impact factors on construction project performance using analytic hierarchy process (AHP),” Technological and Economic Development of Economy, vol. 26, no. 1, pp. 71–85, 2020, doi: 10.3846/tede.2019.11262.
19. K. K. Naji, M. Gunduz, and A. F. Naser, “Construction change order management project support system utilizing Delphi method,” Journal of Civil Engineering and Management, vol. 28, no. 7, pp. 564–589, 2022, doi: 10.3846/jcem.2022.17203.
20. K. K. Naji, M. Gunduz, and A. F. Naser, “The effect of change-order management factors on construction project success: A structural equation modeling approach,” Journal of Construction Engineering and Management, vol. 148, no. 9, art. 04022085, 2022, doi: 10.1061/(ASCE)CO.1943-7862.0002350.
21. D. Garner, Permitting Success: Closing the Digital Divide Through Local Broadband Permitting. Benton Institute for Broadband & Society, 2024. [Online]. Available: https://www.benton.org/sites/default/files/permitting-success.pdf
22. National Telecommunications and Information Administration, “Permitting,” BroadbandUSA. Accessed: Jul. 2026. [Online]. Available: https://broadbandusa.ntia.gov/assistance/permitting
23. F. U. Hassan and T. Le, “Automated requirements identification from construction contract documents using natural language processing,” Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, vol. 12, no. 2, art. 04520009, 2020, doi: 10.1061/(ASCE)LA.1943-4170.0000379.
24. R. Khalef and I. H. El-adaway, “Automated identification of substantial changes in construction projects of airport improvement program: Machine learning and natural language processing comparative analysis,” Journal of Management in Engineering, vol. 37, no. 6, art. 04021062, 2021, doi: 10.1061/(ASCE)ME.1943-5479.0000959.
25. H. Zhou, B. Gao, S. Tang, B. Li, and S. Wang, “Intelligent detection on construction project contract missing clauses based on deep learning and NLP,” Engineering, Construction and Architectural Management, vol. 32, no. 3, pp. 1546–1580, 2023, doi: 10.1108/ECAM-02-2023-0172.
26. A. Gondia, A. Siam, W. El-Dakhakhni, and A. H. Nassar, “Machine learning algorithms for construction projects delay risk prediction,” Journal of Construction Engineering and Management, vol. 146, no. 1, art. 04019085, 2020, doi: 10.1061/(ASCE)CO.1943-7862.0001736.
27. A. Moussa, M. Ezzeldin, and W. El-Dakhakhni, “Machine learning and optimization strategies for infrastructure projects risk management,” Construction Management and Economics, vol. 43, no. 8, pp. 557–582, 2025, doi: 10.1080/01446193.2025.2479764.
28. J. Wu, X. Xue, and J. Zhang, “Invariant signature, logic reasoning, and semantic natural language processing (NLP)-based automated building code compliance checking (I-SNACC) framework,” Journal of Information Technology in Construction, vol. 28, pp. 1–18, 2023. [Online]. Available: https://www.itcon.org/paper/2023/1
29. B. A. Manu, “Leveraging artificial intelligence for optimized project management and risk mitigation in construction industry,” World Journal of Advanced Research and Reviews, vol. 24, no. 3, pp. 2924–2940, 2024.
30. Project Management Institute, A Guide to the Project Management Body of Knowledge (PMBOK Guide), 7th ed. Newtown Square, PA: Project Management Institute, 2021.