Integrating AI with Data Engineering Pipelines: Enhancing Decision-Making in Real-Time Systems

Abstract

The integration of Artificial Intelligence (AI) with data engineering pipelines is transforming 
decision-making processes in real-time systems. This paper explores the synergistic potential of 
AI-driven techniques in optimizing data flows, enhancing the accuracy of predictive models, and 
ensuring timely insights for critical operations. By embedding AI algorithms within data pipelines, 
organizations can process large volumes of streaming data, detect anomalies, and make dynamic 
decisions with minimal latency. The paper also delves into the challenges of scalability, data 
consistency, and latency reduction in real-time environments, highlighting key strategies for 
integrating AI into traditional data engineering frameworks. The results demonstrate how this 
integration improves system efficiency, decision-making precision, and responsiveness in 
industries such as finance, healthcare, and manufacturing