How Program Management Accelerates the National AI Revolution

Main Article Content

Kaushik Ponnapally

Abstract

This paper looks at how practices in managing the program can enable faster adoption of AI nationally. The analysis is applied using the quantitative information gathered on 120 professionals working in the fields of governance, engineering, compliance, and MLOps determining the competency of structured processes in speed, stability, and cost-efficiency. Statistical tests demonstrate that there is a strong positive relationship between program governance and data quality controls and cloud readiness and MLOps maturity and the overall results of AI performance. The best predictors of acceleration of AI obtained as the result of regression are governance maturity, cross-functional coordination, and cloud orchestration. The research finds that disciplined program management is necessary in order to scale AI initiatives reliably, minimise the risks, and enhance operational effectiveness is improved

Article Details

Section

Articles

How to Cite

How Program Management Accelerates the National AI Revolution. (2025). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(1), 11817-11826. https://doi.org/10.15662/IJRPETM.2025.0801011

References

[1] Batool, A., Zowghi, D., & Bano, M. (2023). Responsible AI Governance: A Systematic Literature Review. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2401.10896

[2] Taboada, I., Daneshpajouh, A., Toledo, N., & De Vass, T. (2023). Artificial Intelligence Enabled Project Management: A Systematic Literature Review. Applied Sciences, 13(8), 5014. https://doi.org/10.3390/app13085014

[3] Lakshminarasimham, K. (2024, September 4). Integrating AI into Program and Project Management: Transforming Decision-Making and Risk Management. https://ijsdai.com/index.php/IJSDAI/article/view/59

[4] Gupta, N. G. (2025). The impact of artificial intelligence on modern program management. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 11(1), 592–600. https://doi.org/10.32628/cseit25111266

[5] Badmus, O. F. (2023). Application of AI technology in program management. Journal of Engineering Research and Reports, 25(8), 48–55. https://doi.org/10.9734/jerr/2023/v25i8958

[6] Esseme, A. C. B., Faniyan, A. A., Farayola, G. F., Kadri, T., Chimezie, C., & Olagbaju, I. E. (2025). AI in Project Management: Enhancing Efficiency, Decision Making, and Risk Management. Journal of Artificial Intelligence Machine Learning and Data Science, 3(1), 2107–2114. https://doi.org/10.51219/jaimld/alain-claude-bah-esseme/462

[7] Schwaeke, J., Peters, A., Kanbach, D. K., Kraus, S., & Jones, P. (2024). The new normal: The status quo of AI adoption in SMEs. Journal of Small Business Management, 63(3), 1297–1331. https://doi.org/10.1080/00472778.2024.2379999

[8] Mehmood, Y., Sabahat, N., & Ijaz, N. M. A. (2024). MLOps critical success factors - A systematic literature review. VFAST Transactions on Software Engineering, 12(1), 183–209. https://doi.org/10.21015/vtse.v12i1.1747

[9] Georgiev, S., Polychronakis, Y., Sapountzis, S., & Polychronakis, N. (2024). The role of artificial intelligence in project management: a supply chain perspective. Supply Chain Forum an International Journal, 1–14. https://doi.org/10.1080/16258312.2024.2384823

[10] Hashimzai, I. A., & Mohammadi, M. Q. (2024). The Integration of Artificial Intelligence in Project Management: A Systematic Literature Review of emerging trends and challenges. TIERS Information Technology Journal, 5(2), 153–164. https://doi.org/10.38043/tiers.v5i2.5963