Open Access

AI-Driven Automation in Cloud-Based Business Systems: A Practical Implementation Using Microservices Architecture

https://doi.org/10.55640/ijmcsit-v03i05-03

PDF

Serhii Svynarov ¹ ,

⁴ Senior Python Engineer & Cloud Security Researcher, Restart AI LLC Miami, FL

Abstract

Objective: - The study examines how artificial intelligence components, when embedded directly into cloud-based microservices architectures, alter operational performance in enterprise environments. Rather than treating AI as analytics overlay deployed after the fact, the research focuses on configurations in which machine-learning models function as first-class participants in service orchestration, inter-service routing, and domain-specific processing.

Methods. A structured review of peer-reviewed publications and industry reports from 2023 to 2026 was combined with a comparative analysis of three enterprise deployment scenarios covering retail e-commerce, financial services compliance automation, and hybrid-cloud healthcare data processing.

Results. Predictive autoscaling reduced idle resource expenditure by 30-42% relative to static threshold-based policies. Intelligent service-mesh routing lowered peak-period request latency by 18-27%. AI-assisted incident classification cut mean time to resolution by 47%, and the share of incidents requiring manual re-routing fell from 31% to 8%. Anomaly detection embedded at the service level reduced mean time to recovery by more than 60% compared with conventional alerting systems.

Conclusions. The evidence supports a layered integration model in which AI components operate at three distinct levels - infrastructure orchestration, inter-service communication, and business-logic processing - rather than being consolidated into a single intelligent layer. Each level carries distinct requirements regarding model interpretability, update frequency, and fault-tolerance tolerance. Practical guidance is offered for architects and engineering teams, and the study identifies model explainability and cross-vendor data portability as the primary constraints on broader adoption.

Keywords

AI-driven automation, microservices architecture, cloud computing

References

Adnan, B., et al. (2026). Can AI agents generate microservices? How far are we? arXiv. https://arxiv.org/abs/2603.09004

Barua, B., & Kaiser, M. S. (2024). AI-driven resource allocation framework for microservices in hybrid cloud platforms. arXiv. https://arxiv.org/abs/2412.02610

Ghai, V. (2026). How AI is transforming cloud computing efficiency for businesses in 2026. Katalyst Technologies. https://katalysttech.com/blog/how-is-ai-transforming-cloud-computing-efficiency-for-businesses-in-2026/

Moreschini, S., et al. (2023). AI techniques in the microservices life-cycle: A systematic mapping study. arXiv. https://arxiv.org/abs/2305.16092

OpenTrends. (2026). Cloud in 2026: The operating system for AI-driven business. https://www.opentrends.us/en/article/cloud-2026-operating-system-ai-driven-business

Rajabhushanam, C. (2026). Design of intelligent cloud systems integrating AI for enterprise applications. International Journal of High-Performance Information Technology. https://ijhit.info/index.php/ijhit/article/view/188

Ramareddy, S. K. (2023). Cloud-native microservices for scalable AI-driven business process automation. International Journal on Advanced Computer Theory and Engineering. https://www.researchgate.net/publication/398281417

ResearchGate. (2026). AI-driven cloud automation for IT service management platforms. https://www.researchgate.net/publication/401727861

Springer. (2026). AI-driven network automation for multi-vendor environments using microservices. Arabian Journal for Science and Engineering. https://link.springer.com/article/10.1007/s13369-026-11319-6

Willard, J. (2025). The evolution and future of microservices architecture with AI integration. International Journal of Research in Engineering and Science, 12(1). https://ijresonline.com/archives/ijres-v12i1p103

Most read articles by the same author(s)

1 2 3 4 5 6 7 8 9 10 > >>

International Journal of Modern Computer Science and IT Innovations

AI-Driven Automation in Cloud-Based Business Systems: A Practical Implementation Using Microservices Architecture

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles