Legacy systems are still critical business operations in many industries – but they are becoming roadblocks to innovation, agility, and scalability. As enterprises increasingly pressure themselves to modernize their aging infrastructures, strategic implementation of a transition from monolithic to microservices is gaining ground. Transforming this type of complex monolith into microservices is not a trivial task. It presents technical and organizational challenges, including bureaucratic service boundaries embedded in legacy codebases that tightly couple the service's functionality. The topic of this article is how artificial intelligence (AI) can help automate the decomposition of monolithic systems into decomposed, scalable microservices. By using machine learning, natural language processing, and clustering algorithms, AI tools can analyze source code, runtime data, and interactions between system components to determine intelligent service boundaries. A detailed methodology for AI-assisted decomposition is presented, along with real-world tools such as IBM Mono2Micro and AWS Microservice Extractor. A practical case study involving a global e-commerce company is included to illustrate applied outcomes. Additionally, the article addresses key challenges such as data inconsistency, domain misalignment, and organizational resistance. How it works outlines best practices to support successful implementation, including incremental migration patterns, domain-driven design, and DevOps integration. The article concludes with strategic recommendations and a forward-looking perspective on how AI will further change the modernization process. When done right, AI improves organizations’ ability to create agile, future-prepared software ecosystems.

Legacy Modernization, Microservices Architecture, Artificial Intelligence, Monolith Decomposition, Software Engineering Automation

Ahmadvand, M., Pretschner, A., Ball, K., & Eyring, D. (2018). Integrity protection against insiders in microservice-based infrastructures: From threats to a security framework. In Software Technologies: Applications and Foundations: STAF 2018 Collocated Workshops, Toulouse, France, June 25-29, 2018, Revised Selected Papers (pp. 573-588). Springer International Publishing.

Akerkar, R. (2019). Artificial intelligence for business. Springer.

Antal, G., Havas, D., Siket, I., Beszédes, Á., Ferenc, R., & Mihalicza, J. (2016, October). Transforming c++ 11 code to c++ 03 to support legacy compilation environments. In 2016 IEEE 16th International Working Conference on Source Code Analysis and Manipulation (SCAM) (pp. 177-186). IEEE.

Baldwin, C. Y. (2015). Bottlenecks, modules and dynamic architectural capabilities. Harvard Business School Finance Working Paper, (15-028).

Chavan, A. (2022). Importance of identifying and establishing context boundaries while migrating from monolith to microservices. Journal of Engineering and Applied Sciences Technology, 4, E168. http://doi.org/10.47363/JEAST/2022(4)E168

Chavan, A. (2024). Fault-tolerant event-driven systems: Techniques and best practices. Journal of Engineering and Applied Sciences Technology, 6, E167. http://doi.org/10.47363/JEAST/2024(6)E167

Crookshanks, E. (2015). Practical enterprise software development techniques: Tools and techniques for large scale solutions. Apress.

De Santis, S., Florez, L., Nguyen, D. V., & Rosa, E. (2016). Evolve the Monolith to Microservices with Java and Node. IBM Redbooks.

Delsing, J. (2017). Local cloud internet of things automation: Technology and business model features of distributed internet of things automation solutions. IEEE Industrial Electronics Magazine, 11(4), 8-21.

Dhanagari, M. R. (2024). MongoDB and data consistency: Bridging the gap between performance and reliability. Journal of Computer Science and Technology Studies, 6(2), 183-198. https://doi.org/10.32996/jcsts.2024.6.2.21

Dhanagari, M. R. (2024). Scaling with MongoDB: Solutions for handling big data in real-time. Journal of Computer Science and Technology Studies, 6(5), 246-264. https://doi.org/10.32996/jcsts.2024.6.5.20

Dickerson, P., & Worthen, J. (2024, May). Optimizing Pipeline Systems for Greater Precision, Efficiency & Safety Using Emerging Technologies. In PSIG Annual Meeting (pp. PSIG-2426). PSIG.

Fritzsch, J. (2024). Architectural refactoring to microservices: a quality-driven methodology for modernizing monolithic applications.

Geetha, R. S., Gowdhamkumar, S., & Jambulingam, S. (2019). Energy challenge, power electronics & systems (PEAS) technology and grid modernization. International Research Journal of Multidisciplinary Technovation, 1(2), 116-129.

Goel, G., & Bhramhabhatt, R. (2024). Dual sourcing strategies. International Journal of Science and Research Archive, 13(2), 2155. https://doi.org/10.30574/ijsra.2024.13.2.2155

Gohil, R., & Patel, H. (2024, June). Comparative Analysis of Cloud Platform: Amazon Web Service, Microsoft Azure, And Google Cloud Provider: A Review. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.

Gu, Q. (2020). A meta-approach to guide architectural refactoring from monolithic applications to microservices (Bachelor's thesis).

Harris, S. D., & Krueger, A. B. (2015). A proposal for modernizing labor laws for twenty-first-century work: the" independent worker" (p. 2015). Washington, DC: Brookings.

Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons, 61(4), 577-586.

Karwa, K. (2023). AI-powered career coaching: Evaluating feedback tools for design students. Indian Journal of Economics & Business. https://www.ashwinanokha.com/ijeb-v22-4-2023.php

Karwa, K. (2024). Navigating the job market: Tailored career advice for design students. International Journal of Emerging Business, 23(2). https://www.ashwinanokha.com/ijeb-v23-2-2024.php

Kibria, M. G., Nguyen, K., Villardi, G. P., Zhao, O., Ishizu, K., & Kojima, F. (2018). Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE access, 6, 32328-32338.

Kim, S. H., & Lim, Y. J. (2021). Artificial intelligence in capsule endoscopy: A practical guide to its past and future challenges. Diagnostics, 11(9), 1722.

Konneru, N. M. K. (2021). Integrating security into CI/CD pipelines: A DevSecOps approach with SAST, DAST, and SCA tools. International Journal of Science and Research Archive. Retrieved from https://ijsra.net/content/role-notification-scheduling-improving-patient

Kumar, A. (2019). The convergence of predictive analytics in driving business intelligence and enhancing DevOps efficiency. International Journal of Computational Engineering and Management, 6(6), 118-142. Retrieved from https://ijcem.in/wp-content/uploads/THE-CONVERGENCE-OF-PREDICTIVE-ANALYTICS-IN-DRIVING-BUSINESS-INTELLIGENCE-AND-ENHANCING-DEVOPS-EFFICIENCY.pdf

Lu, J., Wang, X., Cheng, X., Yang, J., Kwan, O., & Wang, X. (2022). Parallel factories for smart industrial operations: From big AI models to field foundational models and scenarios engineering. IEEE/CAA Journal of Automatica Sinica, 9(12), 2079-2086.

Martini, A., Bosch, J., & Chaudron, M. (2015). Investigating architectural technical debt accumulation and refactoring over time: A multiple-case study. Information and Software Technology, 67, 237-253.

McClelland, D. C., & Burnham, D. H. (2017). Power is the great motivator. In Leadership Perspectives (pp. 271-279). Routledge.

NAEEM SYED, A. A., BAIG, Z., & ZEADALLY, S. (2018). Artificial Intelligence as a Service (AIaaS) for Cloud, Fog and the Edge: State-of-the-Art Practices.

Newman, S. (2019). Monolith to microservices: evolutionary patterns to transform your monolith. O'Reilly Media.

Nyati, S. (2018). Transforming telematics in fleet management: Innovations in asset tracking, efficiency, and communication. International Journal of Science and Research (IJSR), 7(10), 1804-1810. Retrieved from https://www.ijsr.net/getabstract.php?paperid=SR24203184230

Pasquier, T., Han, X., Goldstein, M., Moyer, T., Eyers, D., Seltzer, M., & Bacon, J. (2017, September). Practical whole-system provenance capture. In Proceedings of the 2017 Symposium on Cloud Computing (pp. 405-418).

Poth, A., Kottke, M., & Riel, A. (2020). The implementation of a digital service approach to fostering team autonomy, distant collaboration, and knowledge scaling in large enterprises. Human Systems Management, 39(4), 573-588.

Rainer, R. K., Prince, B., Sanchez-Rodriguez, C., Splettstoesser-Hogeterp, I., & Ebrahimi, S. (2020). Introduction to information systems. John Wiley & Sons.

Raju, R. K. (2017). Dynamic memory inference network for natural language inference. International Journal of Science and Research (IJSR), 6(2). https://www.ijsr.net/archive/v6i2/SR24926091431.pdf

Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., & Harnisch, M. (2015). Industry 4.0: The future of productivity and growth in manufacturing industries. Boston consulting group, 9(1), 54-89.

Singh, V. (2022). Advanced generative models for 3D multi-object scene generation: Exploring the use of cutting-edge generative models like diffusion models to synthesize complex 3D environments. https://doi.org/10.47363/JAICC/2022(1)E224

Singh, V. (2023). Large language models in visual question answering: Leveraging LLMs to interpret complex questions and generate accurate answers based on visual input. International Journal of Advanced Engineering and Technology (IJAET), 5(S2). https://romanpub.com/resources/Vol%205%20%2C%20No%20S2%20-%2012.pdf

Šmite, D., Moe, N. B., Šāblis, A., & Wohlin, C. (2017). Software teams and their knowledge networks in large-scale software development. Information and Software Technology, 86, 71-86.

Sukhadiya, J., Pandya, H., & Singh, V. (2018). Comparison of Image Captioning Methods. INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH, 6(4), 43-48. https://rjwave.org/ijedr/papers/IJEDR1804011.pdf

Suzic, B., & Latinovic, M. (2020, March). Rethinking Authorization Management of Web-APIs. In 2020 IEEE International Conference on Pervasive Computing and Communications (PerCom) (pp. 1-10). IEEE.

Tahir, Y., Khan, I., Rahman, S., Nadeem, M. F., Iqbal, A., Xu, Y., & Rafi, M. (2021). A state‐of‐the‐art review on topologies and control techniques of solid‐state transformers for electric vehicle extreme fast charging. IET power electronics, 14(9), 1560-1576.

Tapia, F., Mora, M. Á., Fuertes, W., Aules, H., Flores, E., & Toulkeridis, T. (2020). From monolithic systems to microservices: A comparative study of performance. Applied sciences, 10(17), 5797.

Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., ... & Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2(4).

Zhong, C., Li, S., Huang, H., Liu, X., Chen, Z., Zhang, Y., & Zhang, H. (2024). Domain-driven design for microservices: An evidence-based investigation. IEEE Transactions on Software Engineering.