Home / Current Issue / Paper 1715961
Integrating Generative AI into Enterprise Software Architectures: From Data Pipelines to Decision Intelligence Systems
Subject area: Science,Engineering and Technology · Area of research: Agentic AI
DOI: 10.64388/IREV9I10-1715961
Abstract
Generative artificial intelligence has rapidly entered enterprise software environments, transforming how organizations process information, interact with data, and automate knowledge-intensive tasks. While early applications have focused on content generation, summarization, and recommendation, the deeper architectural challenge lies in integrating generative AI into systems where outputs influence operational decisions. Enterprise software does not merely consume information; it executes actions, enforces policies, and produces outcomes that require reliability, traceability, and accountability. This paper examines the architectural transition from traditional data pipelines to decision intelligence systems. It argues that generative AI should not be treated as a standalone decision-maker, but as a probabilistic intelligence component embedded within governed decision pipelines. To address this challenge, the study introduces the concept of a Decision Materialization Layer, an architectural layer that transforms AI-generated outputs into structured, validated, explainable, and auditable decisions. The proposed model reframes generative AI outputs as intermediate signals rather than final answers. These signals are enriched with contextual data, evaluated against business rules, cross-validated when necessary, and converted into decisions that enterprise systems can safely act upon. Through this approach, organizations can bridge the gap between probabilistic AI behavior and deterministic enterprise requirements. By developing a framework for integrating generative AI into enterprise architectures, this paper contributes to the emerging field of decision intelligence systems. It offers a structured pathway for designing AI-enabled software environments that are not only intelligent, but also governable, observable, and dependable.
Keywords
Generative AI, Enterprise Software Architecture, Decision Intelligence, AI Governance, Decision Pipelines
References
[1] Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Nagappan, N., Nushi, B., & Zimmermann, T. (2019). Software engineering for machine learning: A case study. Proceedings of the 41st International Conference on Software Engineering (ICSE), 291–300. https://doi.org/10.1109/ICSE.2019.00039
[2] Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
[3] Fowler, M. (2018). Evolutionary architecture and emergent design. martinfowler.com.
[4] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
[5] Kleppmann, M. (2017). Designing data-intensive applications: The big ideas behind reliable, scalable, and maintainable systems. O’Reilly Media.
[6] Kumar, A. N., et al. (2021). Enterprise AI: Applications, challenges, and future directions. IEEE Engineering Management Review, 49(3), 146–156.
[7] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
[8] Mitchell, T. M. (1997). Machine learning. McGraw-Hill.
[9] Nygard, M. T. (2018). Release it!: Design and deploy production-ready software (2nd ed.). Pragmatic Bookshelf.
[10] Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x
[11] Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V.,
[12] Young, M., Crespo, J. F., & Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in Neural Information Processing Systems (NeurIPS), 28.34
[13] Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction, 36(6), 495–504.
[14] Varshney, K. R. (2019). Trustworthy machine learning. Independently published.
[15] Zaharia, M., Chen, A., Davidson, A., Ghodsi, A., Hong, S. A., Konwinski, A., Murching,
[16] S., Nykodym, T., Ogilvie, P., Parkhe, M., et al. (2018). Accelerating the machine learning lifecycle with MLflow. IEEE Data Engineering Bulletin, 41(4), 39–45.
How to cite this paper
@article{1715961,
author = {Ilker Kanatli},
title = {Integrating Generative AI into Enterprise Software Architectures: From Data Pipelines to Decision Intelligence Systems},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {4517-4534},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715961.pdf},
abstract = {Generative artificial intelligence has rapidly entered enterprise software environments, transforming how organizations process information, interact with data, and automate knowledge-intensive tasks. While early applications have focused on content generation, summarization, and recommendation, the deeper architectural challenge lies in integrating generative AI into systems where outputs influence operational decisions. Enterprise software does not merely consume information; it executes actions, enforces policies, and produces outcomes that require reliability, traceability, and accountability. This paper examines the architectural transition from traditional data pipelines to decision intelligence systems. It argues that generative AI should not be treated as a standalone decision-maker, but as a probabilistic intelligence component embedded within governed decision pipelines. To address this challenge, the study introduces the concept of a Decision Materialization Layer, an architectural layer that transforms AI-generated outputs into structured, validated, explainable, and auditable decisions. The proposed model reframes generative AI outputs as intermediate signals rather than final answers. These signals are enriched with contextual data, evaluated against business rules, cross-validated when necessary, and converted into decisions that enterprise systems can safely act upon. Through this approach, organizations can bridge the gap between probabilistic AI behavior and deterministic enterprise requirements. By developing a framework for integrating generative AI into enterprise architectures, this paper contributes to the emerging field of decision intelligence systems. It offers a structured pathway for designing AI-enabled software environments that are not only intelligent, but also governable, observable, and dependable.},
keywords = {Generative AI, Enterprise Software Architecture, Decision Intelligence, AI Governance, Decision Pipelines},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1715961}
}