Current Volume 9
Integrating Generative AI into Enterprise Software Architectures: From Data Pipelines to Decision Intelligence Systems
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
Citations
IRE Journals:
Ilker Kanatli "Integrating Generative AI into Enterprise Software Architectures: From Data Pipelines to Decision Intelligence Systems" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 4517-4534 https://doi.org/10.64388/IREV9I10-1715961
IEEE:
Ilker Kanatli
"Integrating Generative AI into Enterprise Software Architectures: From Data Pipelines to Decision Intelligence Systems" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1715961
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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