Building Scalable Enterprise Systems: The Intersection of Web Technology, Cloud Computing, and AI Marketing

Authors

  • Awaz Ahmed Shaban Information Technology Department, Technical College of Informatics-Akre, Akre University for Applied Sciences, Duhok, Iraq
  • Subhi R. M. Zeebaree Energy Eng. Dept., Technical College of Engineering, Duhok Polytechnic University, Duhok, Iraq

DOI:

https://doi.org/10.58429/pgjsrt.v4n1a214

Keywords:

Enterprise scalability, cloud computing, artificial intelligence, web technology

Abstract

The rapid evolution of digital technologies has transformed enterprise scalability, necessitating the integration of cloud computing, artificial intelligence (AI), and web technologies. Traditional enterprise architectures often struggle with dynamic workloads, operational efficiency, and adaptability to market demands. This study explores the intersection of web technology, cloud computing, and AI-driven automation in building scalable enterprise systems. Through an extensive literature review, comparative analysis, and extracted industry statistics, this research identifies key strategies such as hybrid and multi-cloud adoption, microservices-based architectures, AI-driven decision-making, and Zero Trust security frameworks. The findings highlight the importance of API-first architectures, predictive analytics, and automated cloud resource management in achieving business agility and cost optimization. Additionally, the study discusses the challenges of data security, integration complexity, regulatory compliance, and cost management in implementing scalable enterprise solutions. The research concludes that organizations that effectively implement AI, cloud technologies, and web-based solutions gain a competitive advantage through increased agility, operational efficiency, and digital resilience. Future research should focus on quantum computing, blockchain integration, ethical AI governance, and industry-specific scalability challenges to further enhance enterprise digital transformation efforts.

Downloads

Download data is not yet available.

References

D. A. Hasan, S. R. M. Zeebaree, M. A. M. Sadeeq, H. M. Shukur, R. R. Zebari, and A. H. Alkhayyat, “Machine Learning-based Diabetic Retinopathy Early Detection and Classification Systems - A Survey,” in 1st Babylon International Conference on Information Technology and Science 2021, BICITS 2021, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 16–21. doi: 10.1109/BICITS51482.2021.9509920.

Y. S. Jghef et al., “Bio-Inspired Dynamic Trust and Congestion-Aware Zone-Based Secured Internet of Drone Things (SIoDT),” Drones, vol. 6, no. 11, Nov. 2022, doi: 10.3390/drones6110337.

D. A. Hasan, K. Hussan, S. R. M. Zeebaree, D. M. Ahmed, O. S. Kareem, and M. A. M. Sadeeq, “The Impact of Test Case Generation Methods on the Software Performance: A Review,” International Journal of Science and Business, vol. 5, no. 6, pp. 33-44, 2021, doi: 10.5281/zenodo.4623940.

S. R. M. Zebari, S. and O. Yaseen, N. (2011) ‘Effects of parallel processing implementation on balanced load-division depending on distributed memory systems’, Journal of University of Anbar for Pure Science, 5(3), pp. 50–56. doi:10.37652/juaps.2011.44313.

D. Q. Zeebaree, H. Haron, A. Mohsin Abdulazeez, and S. R. M. Zeebaree, “Combination of K-means clustering with Genetic Algorithm: A review,” 2017. [Online]. Available: http://www.ripublication.com

H. Dino et al., “Facial Expression Recognition Based on Hybrid Feature Extraction Techniques with Different Classifiers,” Test Engineering And Management, vol. 83, no. May-June 2020, pp. 22319-22329, 2020.

A. Al-Zebari, S. R. M. Zeebaree, K. Jacksi, and A. Selamat, “ELMS-DPU Ontology Visualization with Protégé VOWL and Web VOWL,” Journal of Advanced Research in Dynamic and Control Systems, vol. 11, pp. 478-85, 2019.

Z. M. Khalid and S. R. M. Zeebaree, “Big Data Analysis for Data Visualization: A Review,” International Journal of Science and Business, vol. 5, no. 2, pp. 64-75, 2021, doi: 10.5281/zenodo.4481357.

K. Jacksi, S. R. M. Zeebaree, and N. Dimililer, “LOD Explorer: Presenting the Web of Data,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 1, 2018, pp. 45-49.

R. Boya Marqas, S. M. Almufti, and R. Rajab Asaad, “FIREBASE EFFICIENCY IN CSV DATA EXCHANGE THROUGH PHP-BASED WEBSITES,” Academic Journal of Nawroz University, vol. 11, no. 3, pp. 410–414, Aug. 2022, doi: 10.25007/ajnu.v11n3a1480.

R. R. Zebari, S. R. M. Zeebaree, K. Jacksi, and H. M. Shukur, “E-Business Requirements For Flexibility And Implementation Enterprise System: A Review,” INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH, vol. 8, no. 11, 2019.

M. Shamal Salih et al., "Diabetic Prediction based on Machine Learning Using PIMA Indian Dataset," Communications on Applied Nonlinear Analysis, Vol 31, No. 5s, 2024, pp. 138–156, doi: 10.52783/cana.v31.1008.

R. E. A. Armya, L. M. Abdulrahman, N. M. Abdulkareem, and A. A. Salih, “Web-based Efficiency of Distributed Systems and IoT on Functionality of Smart City Applications,” Journal of Smart Internet of Things, vol. 2023, no. 2, pp. 142–161, Dec. 2023, doi: 10.2478/jsiot-2023-0017.

Zainab Salih Ageed et al., “Leveraging high resolution remote sensing images for vehicle classification using sea lion optimization with deep learning model,” Journal of Smart Internet of Things, vol. 2022, no. 1, pp. 97-113, 2023, doi: 10.2478/jsiot-2022-0007.

S. M. Almufti and S. R. M. Zeebaree, “Leveraging Distributed Systems for Fault-Tolerant Cloud Computing: A Review of Strategies and Frameworks,” Academic Journal of Nawroz University (AJNU), vol. 13, no. 2, 2024, doi: 10.25007/ajnu.v13n1a2012.

S. M. Almufti, R. B. Marqas, R. R. Asaad, and A. A. Shaban, “Cuckoo search algorithm: overview, modifications, and applications,” International Journal of Scientific World 2025. [Online]. Available: www.sciencepubco.com/index.php/IJSW

R. M. Abdullah, L. M. Abdulrahman, N. M. Abdulkareem, and A. A. Salih, “Modular Platforms based on Clouded Web Technology and Distributed Deep Learning Systems,” Journal of Smart Internet of Things, vol. 2023, no. 2, pp. 154–173, Dec. 2023, doi: 10.2478/jsiot-2023-0018.

S. H. Haji, A. Al-zebari, A. Sengur, S. Fattah, and N. Mahdi, “Document Clustering in the Age of Big Data: Incorporating Semantic Information for Improved Results,” Journal of Applied Science and Technology Trends, vol. 4, no. 01, pp. 34–53, Feb. 2023, doi: 10.38094/jastt401143.

Z. A. Younis, A. M. Abdulazeez, S. R. M. Zeebaree, R. R. Zebari, and D. Q. Zeebaree, “Mobile Ad Hoc Network in Disaster Area Network Scenario; A Review on Routing Protocols,” International journal of online and biomedical engineering, vol. 17, no. 3, pp. 49–75, 2021, doi: 10.3991/ijoe.v17i03.16039.

A. Kumar, “AI-Driven Innovations in Modern Cloud Computing,” Computer Science and Engineering, vol. 2024, no. 6, pp. 129–134, doi: 10.5923/j.computer.20241406.02.

Z. Asimiyu, “Modernizing Enterprise IT Systems The Intersection of Cloud Computing, AI, and Knowledge Management,” 2023.

F. O. Ugbebor, “Intelligent Cloud Solutions Bridging Technology Gaps for Small and Medium-Sized Enterprises”, doi: 10.60087.

Nasiba M. Abdulkareem et al., “COVID-19 World Vaccination Progress Using Machine Learning Classification Algorithms,” Qubahan Academic Journal, vol. 1, no. 2, pp. 100–105, May 2021, doi: 10.48161/qaj.v1n2a53.

M. Hakimi, G. A. Amiri, S. Jalalzai, F. A. Darmel, and Z. Ezam, “Exploring the Integration of AI and Cloud Computing: Navigating Opportunities and Overcoming Challenges,” TIERS Information Technology Journal, vol. 5, no. 1, pp. 57–69, Jun. 2024, doi: 10.38043/tiers.v5i1.5496.

S. M. Almufti, R. B. Marqas, Z. A. Nayef, and T. S. Mohamed, “Real Time Face-mask Detection with Arduino to Prevent COVID-19 Spreading,” Qubahan Academic Journal, vol. 1, no. 2, pp. 39–46, Apr. 2021, doi: 10.48161/qaj.v1n2a47.

R. Avdal Saleh and S. R. M. Zeebaree, “Transforming Enterprise Systems with Cloud, AI, and Digital Marketing,” International Journal of Mathematics, Statistics, and Computer Science, vol. 3, pp. 324–337, Mar. 2025, doi: 10.59543/ijmscs.v3i.13883.

Amira Bibo Sallow et al., “Machine learning skills to K–12”, Journal of Soft Computing and Data Mining, vol. 5, no. 1, 2024.

M. Piastou, “Enhancing Data Analysis by Integrating AI Tools with Cloud Computing,” vol. 9001, p. 13924, 2008, doi: 10.15680/IJIRSET.2024.1307182.

MAHMOOD, Mayyadah R. et al., “Classification techniques’ performance evaluation for facial expression recognition”, Indonesian Journal of Electrical Engineering and Computer Science, vol. 21, no. 2, pp. 1176-1184, feb. 2021, doi: 10.11591/ijeecs.v21.i2.

S. M. Almufti and A. A. Shaban, “U-Turning Ant Colony Algorithm for Solving Symmetric Traveling Salesman Problem,” Academic Journal of Nawroz University, vol. 7, no. 4, p. 45, Dec. 2018, doi: 10.25007/ajnu.v7n4a270.

S. Almufti, “The novel Social Spider Optimization Algorithm: Overview, Modifications, and Applications,” ICONTECH INTERNATIONAL JOURNAL, vol. 5, no. 2, pp. 32–51, Jun. 2021, doi: 10.46291/icontechvol5iss2pp32-51.

S. D. Jankovic and D. M. Curovic, "Strategic integration of artificial intelligence for sustainable businesses: implications for data management and human user engagement in the digital era," Sustainability, vol. 15, no. 21, p. 15208, 2023.

J. Zhao and B. Gómez Fariñas, "Artificial intelligence and sustainable decisions," Eur. Bus. Organ. Law Rev., vol. 24, no. 1, pp. 1–39, 2023.

H. Behrooz, C. Lipizzi, G. Korfiatis, M. Ilbeigi, M. Powell, and M. Nouri, "Towards automating the identification of sustainable projects seeking financial support: An AI-powered approach," Sustainability, vol. 15, no. 12, 2023.

A. Stecyk and I. Miciuła, "Harnessing the power of artificial intelligence for collaborative energy optimization platforms," Energies, vol. 16, no. 13, pp. 1–20, 2023.

S. Pal, "Integrating AI in sustainable supply chain management: A new paradigm for enhanced transparency and sustainability," Int. J. Res. Appl. Sci. Eng. Technol., vol. 11, no. 6, pp. 2979–2984, 2023.

S. A. Syed, D. Sierra-Sosa, A. Kumar, and A. Elmaghraby, "IoT in smart cities: A survey of technologies, practices, and challenges," Smart Cities, vol. 4, no. 2, pp. 429–475, 2021.

E. Aydın and M. Turan, "An AI-based shortlisting model for sustainability of human resource management," Sustainability, vol. 15, no. 3, p. 2737, 2023.

M. E. E. Alahi et al., "Integration of IoT-enabled technologies and artificial intelligence (AI) for smart city scenario: Recent advancements and future trends," Sensors, vol. 23, no. 11, 2023.

S. E. Bibri et al., "A comprehensive systematic review of the emerging paradigm of smarter eco-cities and their leading-edge AI and AIoT solutions for environmental sustainability," Sustainability, vol. 15, no. 3, 2023.

A. N. Martínez-García, "Artificial intelligence for sustainable complex socio-technical-economic ecosystems," Computation, vol. 10, no. 6, 2022.

D. Vrontis, R. Chaudhuri, and S. Chatterjee, "Adoption of digital technologies by SMEs for sustainability and value creation: Moderating role of entrepreneurial orientation," Sustainability, vol. 14, no. 13, 2022.

S. Mishra and A. R. Tripathi, "AI business model: An integrative business approach," J. Innov. Entrep., vol. 10, no. 1, 2021.

B. S. Rego, S. Jayantilal, J. J. Ferreira, and E. G. Carayannis, "Digital transformation and strategic management: A zystematic review of the literature," J. Knowl. Econ., vol. 13, no. 4, pp. 3195–3222, 2022.

M. J. Ziółkowska, "Digital transformation and marketing activities in small and medium-sized enterprises," Sustainability, vol. 13, no. 5, pp. 1–16, 2021.

L. Sanbella, I. Van Versie, and S. Audiah, "Online marketing strategy optimization to increase sales and e-commerce development: An integrated approach in the digital age," Startupreneur Bus. Digit., vol. 3, no. 1, 2024.

S. Saeed, S. A. Altamimi, N. A. Alkayyal, E. Alshehri, and D. A. Alabbad, "Digital transformation and cybersecurity challenges for businesses resilience: Issues and recommendations," Sensors, vol. 23, no. 15, pp. 1–20, 2023.

T. Amir and J. Henry, "The intersection of cybersecurity and environmental responsibility: Securing sustainable data stores with AI," Sustainability, vol. 16, no. 9, 2023.

L. P. Rondon, L. Babun, A. Aris, K. Akkaya, and A. S. Uluagac, "Survey on enterprise Internet-of-Things systems (E-IoT): A security perspective," Ad Hoc Netw., vol. 125, 2022.

J. Pal Singh, "Integrating artificial intelligence in circular economy: Optimization of resource management and waste minimization," J. Clean. Prod., vol. 13, 2023.

A. Maiurova et al., "Promoting digital transformation in waste collection service and waste recycling in Moscow (Russia): Applying a circular economy paradigm," J. Clean. Prod., vol. 354, 2022.

R. Martínez-Peláez et al., "Role of digital transformation for achieving sustainability: Mediated role of stakeholders, key capabilities, and technology," Sustainability, vol. 15, no. 14, 2023.

S. G. Gavrila, "Digitalization in retail: The future of small business in an AI-driven economy," Bus. Horiz., vol. 15, 2023.

X. Zhu, S. Ge, and N. Wang, "Digital transformation: A systematic literature review," Comput. Ind. Eng., vol. 162, 2021.

D. Soto Setzke, T. Riasanow, M. Böhm, and H. Krcmar, "Pathways to digital service innovation: The role of digital transformation strategies in established organizations," Inf. Syst. Front., vol. 25, no. 3, 2023.

L. Guo and L. Xu, "The effects of digital transformation on firm performance: Evidence from China’s manufacturing sector," Sustainability, vol. 13, no. 22, 2021.

S. M. Mohammed, K. Jacksi, and S. R. M. Zeebaree, “Glove Word Embedding and DBSCAN algorithms for Semantic Document Clustering,” in Proc. 3rd Int. Conf. Advanced Science and Engineering (ICOASE), IEEE, Dec. 2020, pp. 211–216, doi: 10.1109/ICOASE51841.2020.9436540.

M. B. Abdulrazaq et al., “An Analytical Appraisal for Supervised Classifiers’ Performance on Facial Expression Recognition Based on Relief-F Feature Selection,” J. Phys. Conf. Ser., vol. 1804, no. 1, Mar. 2021, doi: 10.1088/1742-6596/1804/1/012055.

S. R. M. Zeebaree and K. Jacksi, “Effects of Processes Forcing on CPU and Total Execution-Time Using Multiprocessor Shared Memory System,” Int. J. Comput. Eng. Res. Trends, vol. 2, pp. 275–279, 2015.

P. C. Saibabu, H. Sai, S. Yadav, and C. R. Srinivasan, “Synthesis of model predictive controller for an identified model of MIMO process,” Indones. J. Electr. Eng. Comput. Sci., vol. 17, no. 2, pp. 941–949, 2019, doi: 10.11591/ijeecs.

R. K. Ibrahim, S. R. M. Zeebaree, and K. F. S. Jacksi, “Survey on semantic similarity based on document clustering,” Adv. Sci. Technol. Eng. Syst. J., vol. 4, no. 5, pp. 115–122, 2019, doi: 10.25046/aj040515.

R. Ihsan, S. Almufti, and R. Marqas, “A Median Filter With Evaluating of Temporal Ultrasound Image for Impulse Noise Removal for Kidney Diagnosis,” J. Appl. Sci. Technol. Trends, vol. 1, no. 1, pp. 71–77, May 2020, doi: 10.38094/jastt1217.

S. Muawanah et al., “Stress and Coping Strategies of Madrasah’s Teachers on Applying Distance Learning During COVID-19 Pandemic in Indonesia,” Qubahan Acad. J., vol. 3, no. 4, pp. 206–218, Nov. 2023, doi: 10.48161/Issn.2709-8206.

R. B. Marqas, S. M. Almufti, and R. R. Asaad, “Firebase Efficiency in CSV Data Exchange Through PHP-Based Websites,” Acad. J. Nawroz Univ., vol. 11, no. 3, pp. 410–414, Aug. 2022, doi: 10.25007/ajnu.v11n3a1480.

A. B. Sallow et al., “Machine Learning Skills To K–12,” J. Soft Comput. Data Min., vol. 5, no. 1, pp. 132–141, Jun. 2024, doi: 10.30880/jscdm.2024.05.01.011.

G. N. Vivekananda et al., “Retracing-efficient IoT model for identifying the skin-related tags using automatic lumen detection,” Intell. Data Anal., vol. 27, pp. 161–180, Nov. 2023, doi: 10.3233/IDA-237442.

P. S. Othman et al., “Image Processing Techniques for Identifying Impostor Documents Through Digital Forensic Examination – Region, Iraq,” 2020.

S. M. Almufti, “Lion algorithm: Overview, modifications and applications,” Int. Res. J. Sci., vol. 2, no. 2, pp. 176–186, 2022, doi: 10.5281/zenodo.6973555.

S. M. Almufti, “Hybridizing Ant Colony Optimization Algorithm for Optimizing Edge-Detector Techniques,” Academic Journal of Nawroz University, vol. 11, no. 2, pp. 135–145, May 2022, doi: 10.25007/ajnu.v11n2a1320.

S. M. Almufti, R. B. Marqas, Z. A. Nayef, and T. S. Mohamed, “Real Time Face-mask Detection with Arduino to Prevent COVID-19 Spreading,” Qubahan Academic Journal, vol. 1, no. 2, pp. 39–46, Apr. 2021, doi: 10.48161/qaj.v1n2a47.

Published

2025-04-11

How to Cite

shaban, awaz, & R. M. Zeebaree, S. (2025). Building Scalable Enterprise Systems: The Intersection of Web Technology, Cloud Computing, and AI Marketing. Polaris Global Journal of Scholarly Research and Trends, 4(1). https://doi.org/10.58429/pgjsrt.v4n1a214

Issue

Section

Review Articles