AI-Driven Optimization of Enterprise Data Pipelines for Real-Time Predictive Analytics

Abstract

The increasing volume, velocity, and variety of enterprise data require advanced data pipelines capable of supporting real-time predictive analytics. Traditional pipelines rely on predefined rules and static resource allocation, limiting their ability to adapt to changing workloads and data characteristics. This research proposes a conceptual AI-driven optimization framework that integrates real-time data ingestion, distributed stream processing, intelligent resource allocation, data quality monitoring, anomaly detection, and predictive model execution. Using a design-oriented literature review methodology, the study defines the framework's architecture and evaluation criteria. Its effectiveness is assessed conceptually against conventional rule-based pipelines using metrics such as latency, throughput, resource utilization, data quality, fault recovery, and predictive freshness. The research highlights the potential of AI to improve pipeline adaptability, reliability, and scalability while acknowledging challenges such as model drift, optimization overhead, operational complexity, and governance. The proposed benefits remain hypotheses requiring empirical validation, and the framework provides a foundation for future implementation and performance evaluation.