Paradise

1. Introduction

The quick evolution of artificial intelligence (AI) and cloud computing and enterprise information technology (IT) systems has brought about a complete transformation in how organizations create and operate their digital transformation programs (Feng et al., 2022). Organizations across the United States will implement AI-driven applications through cloud-based platforms and their current IT systems by 2025 to achieve better operational efficiency and improved decision-making and sustain market competitiveness (Sjödin et al., 2018). Recent industry reports indicate that 70% of large U.S. organizations have adopted cloud-integrated AI solutions while 55% of these companies use AI in their fundamental operational processes. The increasing number of organizations using complex integration projects does not stop them from facing ongoing difficulties when they work on these projects (Islam et al.., 2023). The combination of AI, cloud, and IT technologies creates various challenges which managers must handle during their integration process. The failure rate of major digital transformation initiatives reaches 40% because organizations fail to develop proper integration strategies and complete digital governance frameworks and obtain necessary competencies (Biswas, et al., 2024). The occurrence of cost overruns and schedule delays continues to be frequent as around 30-35% of IT integration projects surpass their planned budget allocations. The present obstacles show that organizations need management systems which perform more than basic technical implementation because they must assess organizational maturity and preparedness (Hossain et al., 2024).

The success of AI and cloud and IT integration projects depends on aligning technology initiatives with business strategy while having mature governance systems and skilled personnel and strong data analytics capabilities. Organizations find maturity models to be useful assessment tools which help them evaluate their current state for benchmarking capabilities and identifying weaknesses and ongoing improvement initiatives (Markopoulos et al., 2019). The current maturity models mainly examine separate aspects of cloud adoption maturity and AI readiness without considering how AI systems and cloud infrastructure and IT systems function together as interconnected systems (Allal-Chérif et al., 2020). The disconnected strategies prevent organizations from creating a total integration management system. The U.S. faces multiple barriers to integration because of regulatory compliance needs and cybersecurity threats and insufficient workforce capabilities (Akdil et al., 2017). Reports indicate that almost 45% of organizations in the United States face their main digital transformation obstacle because they do not have enough skilled AI and cloud professionals (Bibby & Dehe, 2018). The financial services sector together with manufacturing and government operations and telecommunications systems maintain their reliance on legacy systems which constitute more than half of their core systems that have operated for over ten years. The present limitations prevent new technology integration from its maximum potential while creating obstacles during system integration procedures (Wagire et al., 2020).

The study tackles these problems through the creation and testing of a maturity model which supports the management of projects that combine AI with cloud and IT integration. The model consists of five essential domains which include strategic alignment and technology infrastructure and data and analytics capability and project governance and human competency. The evaluation framework includes all technical and managerial components of integration maturity through its five domains which serve as evaluation criteria (Akbar et al., 2022). The research investigates organizational maturity levels across these domains to determine how different maturity stages affect project outcomes. This study provides an integrated maturity perspective which advances digital transformation research by enabling better AI, cloud, and IT integration results that will create sustainable strategic benefits.

2. Materials and Methods

2.1 Study Design and Study Situation

The study adopted a cross-sectional quantitative design to study organizational progress in managing AI and cloud and IT integration projects across U.S. organizations during 2025. The study environment demonstrates how public and private organizations implement advanced digital technologies because organizations need digital transformation and automation and data-driven operations (Lin et al., 2019). A survey-based approach functions as the best method to collect feedback from professionals who perform AI cloud and IT integration projects in their work. The design enables researchers to conduct maturity assessments by using standardized methods to evaluate various domains within a particular time frame (Czvetkó et al., 2021). The study examines five maturity domains which include strategic alignment and technology infrastructure and data and analytics capability and project governance and human competency according to modern integration maturity models (Lu, 2019). The study environment includes various business sectors to create a wide range of settings which enables the study results to show actual integration methods and difficulties that U.S. businesses encounter in today's complex digital world.

2.2 Population, Sampling, and Study Area

The research gathered data from IT managers and project managers and data analysts and system architects and senior technical experts who worked on AI and cloud and IT integration projects throughout the United States. Organizations were drawn from significant sectors, including IT & Software Services, Financial Institutions, Manufacturing, Public Sector, and Telecommunications (Chen et al., 2020). The purposive sampling method helped researchers select participants who had relevant work experience and direct responsibility for integration projects. The eligibility requirements needed participants to have at least one year of experience with AI systems and cloud platforms and large-scale IT integration projects (Islam & Biswas, 2023). Data collection took place throughout the United States by using online professional networks and industry forums and corporate contacts which span different regions of the country. A total of 225 valid responses were obtained, exceeding minimum sample size recommendations for multivariate statistical analysis. The sampling method provides an accurate representation of organizational practices and digital transformation maturity levels which exist in modern American organizations (Akkiraju et al., 2020).

2.3 Instrument Development and Measurement

The study team used a structured questionnaire to collect data which they designed by studying existing study about digital transformation and IT integration maturity. The instrument contained four sections which collected respondent demographics and organizational maturity domains and project success indicators and perceived integration barriers (Simetinger & Zhang, 2020). The five maturity domains strategic alignment, technology infrastructure, data, analytics capability, project governance, and human competency were measured using multiple items on a five-point Likert scale ranging from 1 (very low maturity) to 5 (very high maturity). The assessment of project success occurred through four main indicators which included cost performance and schedule adherence and system quality and user satisfaction to calculate a Project Success Index (Islam, 2023). The survey questions revealed four main types of obstacles which included organizational problems and technical issues and financial constraints and security risks. The assessment of the questionnaire occurred through based IT professionals and academic experts who verified clarity and relevance and contextual appropriateness for digital transformation practices in 2025.

2.4 Data Collection Procedure

The study team collected data through an online survey platform between January and March 2025 for their nationwide study across the United States. The invitations reached recipients through professional email lists and industry communities which focus on AI and cloud computing and enterprise IT integration (Zhang et al., 2018). The study participants received information about the research objectives and learned that their involvement would be completely optional. The questionnaire used logical sequencing and concise item wording and mandatory responses for key variables to improve response quality. The system identified duplicate entries which led to their automatic deletion while researchers removed all incomplete survey responses before starting their data analysis (Vásquez et al., 2021). The data collection method of digital technology fits present research standards because it reflects the technological environment which study participants interact with. The method allowed researchers to gather information efficiently while keeping the data accessible and consistent throughout different organizational environments (Ochoa & Peña-Reyes, 2021).

2.5 Data Analysis Techniques

The collected data analysis took place with IBM SPSS Statistics Version 26 and Python visualization tools during 2025. We used descriptive statistics to analyze demographic information and maturity domain scores through frequency counts and percentage values and average scores and deviation measures. The study used Pearson correlation analysis to analyze the connections between maturity domains and their predictive power for project success metrics (Jahan et al., 2024). We used multiple linear regression analysis to examine how each maturity domain affects the Project Success Index. Model diagnostics confirmed acceptable assumptions of normality, and homoscedasticity (Niño et al., 2018). We used heat maps together with bar charts to show correlation patterns and barriers in their data visualization process. The analytical methods enabled researchers to perform detailed structural relationship analysis which demonstrated through data that project success depends on maturity levels (Bian et al., 2022).

2.6 Reliability, Validity, and Ethical Considerations

we conducted a thorough assessment of reliability and validity to establish trustworthiness of the research outcomes. The internal consistency reliability assessment through Cronbach’s alpha showed that all maturity domains achieved scores above the 0.75 threshold which is currently recommended (Allal-Chérif et al., 2020). The content and construct validity of the study followed previous academic frameworks and received review from professionals who specialize in U.S. IT integration projects. A pilot test of 20 respondents helped researchers to enhance the item clarity and structure. The study maintained full ethical standards throughout its entire duration. The participants gave their informed consent before joining the study while avoided collecting any identifying personal data. The study followed ethical standards for social science and information systems research in the United States by keeping all responses anonymous during secure storage.

3. Results

3.1 Respondent Demographics

The demographic data from 225 participants who worked on AI and cloud and IT integration projects. The sample contained 140 males which made up 62.2% of the participants and 85 females who represented 37.8% of the total sample according to Table 1. Respondents showed up from different business sectors with IT; Software Services taking the lead at 31.1% followed by Financial Institutions at 23.6% and Manufacturing at 18.2% and Government/Public Sector at 15.1% and Telecom at 12.0%. The participants mainly belonged to the 25–34 age range which made up 45.3% of the group while 24.0% were 35–44 years old and 21.3% were under 25 and 9.3% were 45 or older which indicates most respondents belong to early career professionals who actively participate in digital transformation projects. The research sample reflects the exact mixture of organizations that implement AI and cloud and IT integration which enables the study to gather relevant insights from industry experts who work across different business sectors and age ranges (Froger et al., 2019).

3.2 Descriptive Statistics of Maturity Domains

The descriptive statistics of five maturity domains for organizations engaged in AI, cloud, and IT integration projects Table 2. The highest average score of 3.82 for Technology infrastructure shows it needs no improvement because the technical infrastructure operates effectively to support integration efforts. The mean score of 3.28 for Strategic alignment positions it as a medium-priority area which demonstrates a moderate level of alignment between IT projects and business targets. The three areas of data & analytics capability (2.93), project governance (2.71), and human competency (2.66) show lower average scores which identify them as high-priority areas that expose workforce skill gaps and deficiencies in governance processes and analytical abilities. Organizations display technical competence but they need to dedicate major work to build better governance systems and enhance human skills and data-based decision processes (Markopoulos et al., 2019). The organizations need to prioritize these critical areas because they will direct strategic planning and resource allocation and training development which will boost organizational maturity and lead to successful AI cloud IT integration.

3.3 Distribution of Organizational Maturity Levels

The evaluation process determines how organizations perform through their combined scores which assess their abilities in artificial intelligence and cloud services and information technology system integration. Most organizations fall within Level 2:  Emerging (36.9%), followed by Level 3 - Defined (27.1%), indicating that a large portion of organizations are in the early to mid-stages of maturity in Table 3. Only a small fraction has achieved Level 5: Optimized (7.1%), reflecting fully developed integration capabilities, while Level 1: Initial (8.9%) represents organizations with minimal maturity. The distribution pattern follows the standard progression of digital transformation maturity which researchers have identified in their studies. The research results show organizations have built formal procedures and technical systems yet they require further development in governance and data analytics and human skill sets to reach higher maturity levels. The knowledge of this distribution pattern enables organizations to assess their current position and develop strategic plans for improving AI and cloud and IT integration results (Zhang et al., 2022).

3.4 Pearson Correlation Matrix Between Maturity Domains and Project Success

The Pearson correlation matrix among five maturity domains in organizations engaged in AI, cloud, and IT integration projects. In Figure 1 shows that the diagonal values represent perfect self-correlation with a value of 1.00 while the off-diagonal values represent correlations between different domains. Project governance maintains the highest level of correlation with all other domains because its values range from 0.41 to 0.63 which demonstrates its crucial function in achieving successful integration. Data and analytics capability shows strong positive relationships with project governance (0.63) and strategic alignment (0.52) which proves that effective data management systems enable better organizational coordination and strategic alignment. Technology infrastructure and strategic alignment maintain moderate positive relationships with other domains which shows their supportive function yet they do not hold dominant positions. Human competency exhibits weaker correlations, suggesting that workforce skills, while important, contribute indirectly to integration outcomes (Davenport, 2018). The findings show that organizations need to build governance systems and data management systems to achieve maturity development in every area.

3.5 Contribution of Maturity Domains to Project Success

The findings from a multiple regression study which assesses how five maturity domains affect the total Project Success Index. The regression coefficients reveal how each domain affects the outcome after removing the effects of the other domains. The study revealed that project governance functions as the most influential factor which leads to project success (ß = 0.32, t = 5.15, p = 0.001) because structured governance systems prove essential for successful integration of AI with cloud services and IT systems in Table 4. The analysis revealed that strong data and analytics practices lead to better project results by showing a significant positive effect (ß = 0.28, t = 4.61). The results indicate that technology infrastructure (ß = 0.21, t = 3.67) and strategic alignment (ß = 0.17, t = 3.12, p = 0.002) create average effects which show that well-maintained technical infrastructure and proper organizational goal alignment produce successful projects. The analysis shows that human competency produces a positive impact though its strength remains weak (ß = 0.13, t = 2.41, p = 0.016). The model demonstrates that governance together with data capabilities functions as the primary elements which drive project success by explaining most of the variation in project outcomes.

3.6 Barriers to Artificial Intelligence, Cloud, and IT Integration

The barriers identified by respondents that affect AI, cloud, and IT integration maturity in organizations. The results in Figure 2 show that weak data governance (20%) and lack of skilled professionals (18.5%) are the main obstacles which demonstrates that poor data management together with insufficient human skills create major challenges for integration projects. The integration process between legacy systems and cloud solutions faces two main barriers which include 16.4% complexity and 15.2% inadequate project management that demonstrates both technical and organizational difficulties during the transition from traditional systems to cloud-based solutions. The system operates under two major constraints which include security and compliance limitations at 14.1% and budget constraints at 15.8% for advanced technology implementation. The results show that organizations need to build both technical infrastructure and organizational systems to achieve successful AI, cloud, and IT integration.

4. Discussion

The study conducts a detailed examination of how organizations mature when implementing AI and cloud and IT integration projects which produces essential knowledge about their operational strengths and weaknesses and strategic focus areas. The demographic analysis in Table 1 shows that 62.2% of professionals are male and 45.3% fall within the 25–34 age range which indicates that digital transformation efforts receive their main support from employees who work between entry-level and mid-career positions. The study includes participants from IT and Software Services and Financial Institutions and Manufacturing and Government and Telecom to study integration methods across various organizational types (Ng et al., 2017). The demographic data matches previous studies which show that younger staff members who understand digital technology lead successful technology implementation projects (Islam et al., 2023). The data in Table 2 shows that technology infrastructure scores highest with a mean value of 3.82 which shows a strong technical base for integration work. The organization shows partial alignment between its IT projects and overall business goals through its strategic alignment rating of 3.28. The organization needs to prioritize immediate development of three main areas which include data & analytics capability at 2.93 and project governance at 2.71 and human competency at 2.66. The research demonstrates organizations maintain strong technical systems but they need to develop better governance structures and staff capabilities and data analysis systems to reach advanced maturity levels. The organizations can achieve better resource allocation and decision-making and enhanced integration success by concentrating their efforts on these specific areas (Ulas, 2019).

The data from Table 3 shows that most organizations exist at Level 2 (Emerging, 36.9%) and Level 3 (Defined, 27.1%) which corresponds to organizations in their early to mid-stage development. Organizations exist at two extremes of the maturity spectrum because 7.1% of them reach Level 5 (Optimized) while 8.9% stay stuck at Level 1 (Initial). Organizations follow this distribution pattern because they need to enhance their governance systems and analytics capabilities and human competencies to reach complete integration success after establishing basic technical systems (Muller & Hart, 2016). The Pearson correlation matrix Figure 1; things to see the centrality of project governance and data; analytics capability, with correlations ranging from 0.41 to 0.63 across other domains. Organizations achieve coordination through governance systems which also establish accountability and standardized operational procedures yet data capabilities enable them to base their decisions on evidence which supports strategic alignment. The relationship between technology infrastructure and strategic alignment shows moderate strength because these elements establish supportive functions yet human competency demonstrates weak correlations which means workforce skills affect integration results through their impact on governance and analytical processes (Kuang et al., 2021). The analysis in shows that project governance and data; analytics capability serve as the main determinants for project success. The three factors of technology infrastructure (ß = 0.21), strategic alignment (ß = 0.17), and human competency (ß = 0.13) show positive effects which proves that successful outcomes need both technical systems and alignment and skilled personnel. The model explains 63% of the variance in the Project Success Index, highlighting the substantial impact of governance and data capabilities on integration performance (Czvetkó et al., 2021).

The data from Figure 2 reveals additional obstacles which stop organizations from reaching advanced maturity levels. The following areas need urgent attention because of weak data governance at 20% and skilled professional shortages at 18.5% and complex legacy-cloud integration at 16.4% and poor project governance at 15.2% and budget restrictions at 15.8% and security and compliance issues at 14.1%. The solution requires organizations to build their workforce and improve their management systems and update their old systems while providing suitable resources. Organizations maintain solid technical foundations yet they need to focus on governance and data analytics and human competency development to reach advanced maturity levels (García et al., 2016). The complete maturity landscape and all domain interrelationships and key elements that lead to project success and important barriers which exist. Organizations can achieve better results and resource efficiency and sustainable AI, cloud, and IT integration by concentrating on the most important areas (Islam, 2023).

5. Conclusion

The study evaluates organizational development in AI and cloud and IT integration project management through an analysis which shows technical infrastructure stands mature but organizations need to enhance their governance systems and data analytics capabilities. The study finds that structured project governance and strong data capabilities lead to better integration success rates which proves that organized processes and solid analytical systems matter. The advancement of maturity depends on solving three main obstacles which include insufficient data management systems. Organizations that focus on governance and data driven decision making, improved project performance and sustained digital transformation success.

Author Contributions

A.B. Conceptualization, methodology, data collection, formal analysis, investigation, writing – original draft, project administration, and correspondence. K.M.H.  Methodology, data validation, writing – review and editing, and supervision.

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