Skip to main content
Articles

An Adaptive AI-Driven Decision Intelligence Architecture for Multi-Domain Enterprise Systems in Cloud Ecosystems

Subrahmanyasarma Chitta

Sr Software Engineer, Access2care LLC (MTM), Saint Louis, MO, USA

Abstract

The rapid digitization of enterprise ecosystems has resulted in increasingly complex, distributed, and heterogeneous computing environments spanning cloud, edge, on-premises, and hybrid infrastructures. Traditional decision-support systems are no longer sufficient to address the dynamic, real-time, and multi-domain demands of modern enterprises. Autonomous Artificial Intelligence (AI)-enabled Decision Intelligence (DI) frameworks represent a transformative paradigm that integrates machine learning, knowledge graphs, causal reasoning, automation, and adaptive control mechanisms to enable self-optimizing, context-aware, and cross-domain decision-making.

 

This research proposes a comprehensive architectural and methodological framework for designing Autonomous AI-Enabled Decision Intelligence systems tailored for multi-domain enterprise computing environments. The framework integrates data fabric principles, federated intelligence, reinforcement learning-based orchestration, explainable AI (XAI), digital twins, and governance-by-design mechanisms to ensure scalability, trust, compliance, and operational resilience.

The study addresses key enterprise challenges, including fragmented data silos, real-time analytics constraints, cross-functional interoperability, regulatory compliance, cybersecurity threats, and sustainability considerations. By leveraging autonomous agents capable of perception, reasoning, learning, and execution across distributed systems, the proposed framework enhances predictive, prescriptive, and adaptive decision-making capabilities.

The research methodology combines systems engineering principles, architectural modeling, algorithmic design, simulation-based validation, and performance benchmarking across domains such as finance, supply chain, healthcare, manufacturing, and IT operations. The framework emphasizes modularity, domain abstraction layers, policy-driven automation, and continuous feedback learning loops to enable enterprise-wide intelligence orchestration.

Key contributions include:

  1. A reference architecture for autonomous decision intelligence.
  2. A multi-layer governance and trust model.
  3. An AI orchestration methodology for heterogeneous computing environments.
  4. A performance evaluation model integrating operational, financial, and sustainability metrics.

The findings demonstrate that autonomous decision intelligence frameworks significantly improve operational efficiency, agility, resilience, and strategic alignment while reducing latency, risk exposure, and resource wastage. This work provides a foundational blueprint for enterprises seeking to operationalize AI-driven autonomous decision ecosystems within complex, multi-domain digital infrastructures.

Article Information

Journal

International Journal of Advanced Engineering Science and Information Technology (IJAESIT)

Volume (Issue)

Vol. 8 No. 6 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT)

DOI

https://doi.org/10.15662/IJAESIT.2025.0806006

Pages

17727-17738

Published

December 10, 2025
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Subrahmanyasarma Chitta (2025). An Adaptive AI-Driven Decision Intelligence Architecture for Multi-Domain Enterprise Systems in Cloud Ecosystems. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 8 No. 6 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 17727-17738. https://doi.org/10.15662/IJAESIT.2025.0806006

References

1. Ananthakrishnan, V., Kondaveeti, D., & Mohammed, A. S. (2025). GenAI-driven semantic ETL: Synthesizing self-optimizing SQL & PL/SQL. Journal of Knowledge Learning and Science Technology, 4(2), 29–43.
2. Ambati, K. C. (2025). An event-driven architecture for autonomous supply chain risk detection and decision automation. International Journal of Computer Technology and Electronics Communication (IJCTEC), 8(1), 1202–1211.
3. Mudunuri, P. R. (2024). Operational transparency as a compliance mechanism in federal DevOps ecosystems. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(3), 8131–8142.
4. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder-decoder architecture. Biomedical Signal Processing and Control, 108, 107932.
5. Grandhe, K. (2025). Transforming insight into action: The symbiotic relationship between big data analytics and data visualization. International Journal of Emerging Trends in Computer Science and Information Technology, 125–129.
6. Devi, C., Manivannan, P., & Pachyappan, R. (2024). Differentially private canary optimization via Thompson sampling for SQL performance fixes. Journal of Artificial Intelligence General Science (JAIGS), 3(1), 532–550.
7. Ramidi, M. (2025). Continuous delivery pipelines for mobile health applications in regulated environments. Journal of Engineering and Computer Sciences, 4(8), 534–544.
8. Sheta, S. V. (2023). The importance of software documentation in the development and maintenance phases. REDVET - Revista Electrónica de Veterinaria, 24(3), 609–618.
9. Vijayaboopathy, V., Yakkanti, B., & Surampudi, Y. (2023). Agile-driven quality assurance framework using ScalaTest and JUnit for scalable big data applications. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 3, 245–285.
10. Kamadi, S. (2024). GenAI data engineering: Synthetic data and feature engineering framework for cloud analytics. Retrieved from https://www.researchgate.net/profile/Sandeep-Kamadi/publication/398922494_GenAI_Data_Engineering_Synthetic_Data_and_Feature_Engineering_framework_for_Cloud_Analytics/links/6948e3a327359023a00edbf1/GenAI-Data-Engineering-Synthetic-Data-and-Feature-Engineering-framework-for-Cloud-Analytics.pdf
11. Devarajan, R., Prabakaran, N., Vinod Kumar, D., Umasankar, P., Venkatesh, R., & Shyamalagowri, M. (2023, August). IoT Based Under Ground Cable Fault Detection with Cloud Storage. In 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) (pp. 1580-1583). IEEE.
12. Akhtaruzzaman, K., MdAbulKalam, A., Mohammad Kabir, H., & KM, Z. (2024). Driving US business growth with AI-driven intelligent automation: Building decision-making infrastructure to improve productivity and reduce inefficiencies. American Journal of Engineering, Mechanics and Architecture, 2(11), 171–198. http://eprints.umsida.ac.id/16412/1/171-198%2BDriving%2BU.S.%2BBusiness%2BGrowth%2Bwith%2BAI-Driven%2BIntelligent%2BAutomation.pdf
13. Gowda, M. K. S. (2024). Leveraging Machine Learning to Enhance Accuracy and Efficiency in Regulatory Compliance. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10683-10692.
14. Jagadeesh, S., & Sugumar, R. (2017). A Comparative study on Artificial Bee Colony with modified ABC algorithm. European Journal of Applied Sciences, 9(5), 243-248.
15. Kuttuva Ganesan, G. B. (2025). Smart grid enterprise integration: Security and analytics framework. In International Conference of Global Innovations and Solutions (pp. 600–609). Springer Nature Switzerland.
16. Dhanorkar, T., Ponnoju, S. C., & Kunju, S. S. (2024). Cloud-native wallet fabric: Engineering scalable, multicurrency e-wallet platforms. Journal of Artificial Intelligence General Science (JAIGS), 6(1), 766–776.
17. Poornachandar, T., Latha, A., Nisha, K., Revathi, K., & Sathishkumar, V. E. (2025). Cloud-based extreme learning machines for mining waste detoxification efficiency. In 2025 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) (pp. 1348–1353). IEEE.
18. Gopinathan, V. R. (2024). Meta-learning–driven intrusion detection for zero-day attack adaptation in cloud-native networks. International Journal of Humanities and Information Technology, 6(01), 19–35.
19. Vimal Raja, G. (2024). Intelligent data transition in automotive manufacturing systems using machine learning. International Journal of Multidisciplinary and Scientific Emerging Research, 12(2), 515–518.
20. Gaddapuri, N. S. (2024). AI based cloud computation method and process development. Power System Protection and Control, 52(2), 38–50.
21. Uttama Reddy Sanepalli. (2024). Operationalizing MLOps with Databricks pipelines: Scalable machine learning in cloud environments. International Journal of Scientific Research in Science, Engineering and Technology, 10(6), 2544–2552. https://doi.org/10.32628/CSEIT25113573
22. Suddala, V. R. A. K. (2024). Driving Innovation and Compliance in Global Payment Platforms through Predictive Analytics and DevOps Automation. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10662-10672.
23. Pothireddy, S. R. (2025). AI-powered copilots are revolutionizing low-code development in the Power Platform. International Journal of Communication Networks and Information Security, 17(2), 86–115.
24. Sampath Kumar Konda. (2024). Distributed AI infrastructure orchestration: A hyperscale multi-cloud framework for geographic load balancing with renewable energy optimization. International Journal of Scientific Research in Science, Engineering and Technology, 11(4), 522–533. https://doi.org/10.32628/IJSRSET242438
25. Anumula, S. R. (2024). Ethical design frameworks for automated decision-making platforms. International Journal of Future Innovative Science and Technology, 7(1), 12035–12047.
26. Manikandan, P., Saravanan, S., & Nagarajan, C. (2024). Intelligent irrigation system with smart farming using ML and artificial intelligence techniques.
27. HV, M. S., & Kumar, S. S. (2024). Fusion based depression detection through artificial intelligence using electroencephalogram (EEG). Fusion: Practice & Applications, 14(2).
28. Ireddy, R. K. (2024). Deep learning architecture for banking risk management: Cloud and AI-driven predictive analytics solution. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(5), 1194–1206.*
29. Mulla, F. (2024). Choosing the Best Architecture for Mobile Applications. International Journal Of Research In Computer Applications And Information Technology, 7, 2350–2363. https://doi.org/10.34218/IJRCAIT_07_02_173
30. Rengarajan, A., & Rajagopalan, S. (2021). Chaos Blend LFSR-Duo Approach on FPGA for Medical Image Security. Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2020, Volume 3, 3, 155.
31. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
32. Anand, L., & Neelanarayanan, V. (2019). Liver disease classification using deep learning algorithm. BEIESP, 8(12), 5105–5111.
33. Sridevi, V., Azath, H., Vijayakumar, R., Anbuselvan, N., Amirthalingam, V., & Arunkumar, S. (2024, April). Augmented Reality Shopping and IoT-Enabled Virtual Try-On with Cloud Services for Interactive Product Displays. In 2024 10th International Conference on Communication and Signal Processing (ICCSP) (pp. 880-885). IEEE.
34. Ambati, K. C. (2025). An event-driven architecture for autonomous supply chain risk detection and decision automation. International Journal of Computer Technology and Electronics Communication (IJCTEC), 8(1), 1202–1211.
35. Panda, S. S. (2024). Delivering Scalable Cloud Services in China: Microsoft and 21Vianet Collaboration. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(6), 11325-11333.
36. Jovith, A. A., Ranganathan, C. S., Priya, S., Vijayakumar, R., Kohila, R., & Prakash, S. (2024, April). Industrial IoT Sensor Networks and Cloud Analytics for Monitoring Equipment Insights and Operational Data. In 2024 10th International Conference on Communication and Signal Processing (ICCSP) (pp. 1356-1361). IEEE.
37. Mohana, P., Muthuvinayagam, M., Umasankar, P., & Muthumanickam, T. (2022, March). Automation using Artificial intelligence based Natural Language processing. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1735-1739). IEEE.
38. Fazilath, M., & Umasankar, P. (2025, February). Comprehensive Analysis of Artificial Intelligence Applications for Early Detection of Ovarian Tumours: Current Trends and Future Directions. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1-9). IEEE.
39. Prasanna, D., Ahamed, N. A., Abinesh, S., Karthikeyan, G., & Inbatamilan, R. (2024, November). Cloud based automatically human document authentication processes for secured system. In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1-7). IEEE.