Integrating Generative AI with Real-Time Data Pipelines for Operational Decision Intelligence
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I2P115Keywords:
Generative AI, real-time data pipelines, decision intelligence, streaming architecture, AI operations, machine learning engineering, data-driven decision makingAbstract
This paper presents a comprehensive conceptual framework for integrating generative artificial intelligence (AI) with real-time data pipelines to enable operational decision intelligence. As organizations increasingly adopt AI-driven solutions, the ability to leverage real-time data streams with generative AI models becomes crucial for timely, informed decision-making. We propose a multi-layered architecture that encompasses data ingestion, AI processing, and decision intelligence generation, complemented by continuous feedback loops for model improvement. The framework addresses key challenges including latency management, model deployment strategies, and ethical considerations in AI-driven operations. Through examination of existing literature and emerging best practices, we demonstrate how this integration can enhance operational agility and decision quality across diverse domains. The paper discusses implementation considerations, trade-offs between performance metrics, and governance requirements. Finally, we outline future research directions including advanced interpretability methods, federated learning approaches, and multi-model orchestration strategies. This work contributes to the growing body of knowledge on AI systems architecture and provides practitioners with actionable insights for building intelligent operational systems.
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