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Volume 1 Number 1 2025
RESEARCH ARTICLE (Open Access)
A Maturity Model for Managing Artificial Intelligence, Cloud, and IT Integration Projects
Anik Biswas1*, Kh Maksudul Hasan2
Paradise 1 (1) 1-8 https://doi.org/10.25163/paradise.1110530
Submitted: 01 May 2025 Revised: 19 July 2025 Accepted: 25 July 2025 Published: 27 July 2025
Abstract
Background: The growing integration of artificial intelligence (AI), cloud computing, and enterprise IT systems has reshaped digital transformation initiatives in U.S. based organizations. Integration projects encounter various difficulties with governance and data capability and human competency.
Methods: The study used a cross-sectional survey design to collect data from 225 professionals who worked on AI and cloud and IT integration projects throughout major U.S. industry sectors during 2025. Organizational maturity was evaluated across five domains: strategic alignment, technology infrastructure, data and analytics capability, project governance, and human competency. The study used descriptive statistics together with Pearson correlation analysis and multiple linear regression to study how maturity levels affect project success rates.
Results: The study data indicates that technology infrastructure development reached its highest level of maturity with an average score of 3.82 whereas data and analytics capability (2.93) and project governance (2.71) and human competency (2.66) lagged behind industry requirements. The majority of organizations operated at the emerging (36.9%) or defined (27.1%) maturity stages. The analysis showed that project governance maintains strong positive correlations with all other maturity domains through correlation coefficients ranging from 0.41 to 0.63. The statistical analysis reveals that project governance displays a beta coefficient of 0.32 and data and analytics capability shows a beta value of 0.28 which together explain 63% of the project success variance.
Conclusion: The study shows that successful AI and cloud and IT integration needs both technical preparedness and solid governance systems and strong data capabilities.
Keywords: Cloud computing, Digital transformation, Artificial intelligence integration, IT integration maturity, Project governance
References
Akbar, M. A., Leiva, V., Rafi, S., Qadri, S. F., Mahmood, S., & Alsanad, A. (2022). Towards roadmap to implement blockchain in healthcare systems based on a maturity model. Journal of Software Evolution and Process, 34(12). https://doi.org/10.1002/smr.2500
Akdil, K. Y., Ustundag, A., & Cevikcan, E. (2017). Maturity and Readiness model for Industry 4.0 strategy. In Springer series in advanced manufacturing (pp. 61–94). https://doi.org/10.1007/978-3-319-57870-5_4
Akkiraju, R., Sinha, V., Xu, A., Mahmud, J., Gundecha, P., Liu, Z., Liu, X., & Schumacher, J. (2020). Characterizing Machine Learning Processes: a maturity framework. In Lecture notes in computer science (pp. 17–31). https://doi.org/10.1007/978-3-030-58666-9_2
Allal-Chérif, O., Simón-Moya, V., & Ballester, A. C. C. (2020b). Intelligent purchasing: How artificial intelligence can redefine the purchasing function. Journal of Business Research, 124, 69–76. https://doi.org/10.1016/j.jbusres.2020.11.050
Bian, Y., Lu, Y., & Li, J. (2022). Research on an Artificial Intelligence-Based Professional Ability Evaluation System from the Perspective of Industry-Education Integration. Scientific Programming, 2022, 1–20. https://doi.org/10.1155/2022/4478115
Bibby, L., & Dehe, B. (2018). Defining and assessing industry 4.0 maturity levels – case of the defence sector. Production Planning & Control, 29(12), 1030–1043. https://doi.org/10.1080/09537287.2018.1503355
Biswas, A., Hossain, M. I., Jahan, I., Chowdhury, N., Mia, M. S. (2024). "Advanced Analytics in IT Systems Unlocking Insights Enhancing Decision Making and Maximizing Business Value", Applied IT & Engineering, 2(1),1-8,10379. https://doi.org/10.25163/engineering.2110379
Chen, H., Li, L., & Chen, Y. (2020). Explore success factors that impact artificial intelligence adoption on telecom industry in China. Journal of Management Analytics, 8(1), 36–68. https://doi.org/10.1080/23270012.2020.1852895
Czvetkó, T., Kummer, A., Ruppert, T., & Abonyi, J. (2021). Data-driven business process management-based development of Industry 4.0 solutions. CIRP Journal of Manufacturing Science and Technology, 36, 117–132. https://doi.org/10.1016/j.cirpj.2021.12.002
Davenport, T. H. (2018). From analytics to artificial intelligence. Journal of Business Analytics, 1(2), 73–80. https://doi.org/10.1080/2573234x.2018.1543535
Feng, X., Guo, C., Jiao, T., & Song, J. (2022). A maturity model for AI-empowered cloud-native databases: from the perspective of resource management. Journal of Cloud Computing Advances Systems and Applications, 11(1). https://doi.org/10.1186/s13677-022-00318-1
Froger, M., Bénaben, F., Truptil, S., & Boissel-Dallier, N. (2019). A non-linear business process management maturity framework to apprehend future challenges. International Journal of Information Management, 49, 290–300. https://doi.org/10.1016/j.ijinfomgt.2019.05.013
García, J. a. L., Peña, A. B., Pérez, P. Y. P., & Pérez, R. B. (2016). Project control and Computational intelligence: Trends and challenges. International Journal of Computational Intelligence Systems, 10(1), 320. https://doi.org/10.2991/ijcis.2017.10.1.22
Hossain, M. I., Jahan, I., Chowdhury, N., Mia, M. S., Biswas, A. (2024). "Integrating Cloud Computing, Big Data, and Business Analytics to Determination Digital Transformation and Competitive Advantage in Modern Enterprises", Journal of Primeasia, 5(1),1-8,10380. https://doi.org/10.25163/primeasia.5110380
Islam, S. N. (2022). "The Integration of AI in Advancing Electrical and Electronics Engineering", Journal of Primeasia, 3(1),1-11,10336. https://doi.org/10.25163/primeasia.3110336
Islam, S. N., Biswas, A. (2023). "Artificial Intelligence for Critical Power Infrastructure: Challenges and Opportunities", Applied IT & Engineering, 1(1),1-9,10397. https://doi.org/10.1080/03066150.2019.1680541
Islam, S. N., Biswas, A., Chowdhury, A. K. (2023). "AI-Enabled Cyber-Physical Power Systems: Review of Smart Grid Security, Optimization, and Decision Support", Applied IT & Engineering, 1(1),1-9,10396. https://doi.org/10.1080/03066150.2019.1680541
Jahan, I., Chowdhury, N., Mia, M. S., Hossain, M. I., Biswas, A. (2024). "Transforming Business Operations Through IT-Enabled Predictive and Prescriptive Analytics: Challenges, Opportunities, and Best Practices", Applied IT & Engineering, 2(1),1-7,10381. https://doi.org/10.25163/engineering.2110381
Kuang, L., Liu, H., Ren, Y., Luo, K., Shi, M., Su, J., & Li, X. (2021). Application and development trend of artificial intelligence in petroleum exploration and development. Petroleum Exploration and Development, 48(1), 1–14. https://doi.org/10.1016/s1876-3804(21)60001-0
Lin, T., Wang, K. J., & Sheng, M. L. (2019). To assess smart manufacturing readiness by maturity model: a case study on Taiwan enterprises. International Journal of Computer Integrated Manufacturing, 33(1), 102–115. https://doi.org/10.1080/0951192x.2019.1699255
Lu, Y. (2019). Artificial intelligence: a survey on evolution, models, applications and future trends. Journal of Management Analytics, 6(1), 1–29. https://doi.org/10.1080/23270012.2019.1570365
Markopoulos, E., Kirane, I. S., Balaj, D., & Vanharanta, H. (2019). Artificial intelligence and blockchain technology adaptation for human resources democratic ergonomization on team management. In Advances in intelligent systems and computing (pp. 445–455). https://doi.org/10.1007/978-3-030-27928-8_68
Muller, L., & Hart, M. (2016). Updating business intelligence and analytics maturity models for new developments. In Lecture notes in business information processing (pp. 137–151). https://doi.org/10.1007/978-3-319-32877-5_11
Ng, S. T., Xu, F. J., Yang, Y., & Lu, M. (2017). A master data management solution to unlock the value of big infrastructure data for smart, sustainable and resilient city planning. Procedia Engineering, 196, 939–947. https://doi.org/10.1016/j.proeng.2017.08.034
Niño, H. a. C., Niño, J. P. C., & Ortega, R. M. (2018). Business intelligence governance framework in a university: Universidad de la costa case study. International Journal of Information Management, 50, 405–412. https://doi.org/10.1016/j.ijinfomgt.2018.11.012
Ochoa-Urrego, R., & Peña-Reyes, J. (2021). Digital Maturity Models: A Systematic Literature Review. In Management for professionals (pp. 71–85). https://doi.org/10.1007/978-3-030-69380-0_5
Simetinger, F., & Zhang, Z. (2020). Deriving secondary traits of industry 4.0: A comparative analysis of significant maturity models. Systems Research and Behavioral Science, 37(4), 663–678. https://doi.org/10.1002/sres.2708
Sjödin, D. R., Parida, V., Leksell, M., & Petrovic, A. (2018b). Smart factory implementation and process innovation. Research-Technology Management, 61(5), 22–31. https://doi.org/10.1080/08956308.2018.1471277
Ulas, D. (2019). Digital Transformation process and SMEs. Procedia Computer Science, 158, 662–671. https://doi.org/10.1016/j.procs.2019.09.101
Vásquez, J., Aguirre, S., Puertas, E., Bruno, G., Priarone, P. C., & Settineri, L. (2021). A sustainability maturity model for micro, small and medium-sized enterprises (MSMEs) based on a data analytics evaluation approach. Journal of Cleaner Production, 311, 127692. https://doi.org/10.1016/j.jclepro.2021.127692
Wagire, A. A., Joshi, R., Rathore, A. P. S., & Jain, R. (2020). Development of maturity model for assessing the implementation of Industry 4.0: learning from theory and practice. Production Planning & Control, 32(8), 603–622. https://doi.org/10.1080/09537287.2020.1744763
Zhang, J., Budhdeo, S., William, W., Cerrato, P., Shuaib, H., Sood, H., Ashrafian, H., Halamka, J., & Teo, J. T. (2022). Moving towards vertically integrated artificial intelligence development. Npj Digital Medicine, 5(1), 143. https://doi.org/10.1038/s41746-022-00690-x
Zhang, X., Ming, X., Liu, Z., Yin, D., Chen, Z., & Chang, Y. (2018). A reference framework and overall planning of industrial artificial intelligence (I-AI) for new application scenarios. The International Journal of Advanced Manufacturing Technology, 101(9–12), 2367–2389. https://doi.org/10.1007/s00170-018-3106-3