AI-Augmented Data Engineering Pipelines: Enhancing Automation, Data Quality And Human Decision-Making

Abstract

Enterprise data engineering pipelines increasingly operate at a scale and complexity that outpaces rule-based governance. This article examines how artificial intelligence and machine learning techniques can be integrated across the pipeline lifecycle — spanning intelligent ingestion, automated quality enforcement, predictive failure monitoring, and human-AI collaborative governance — to build adaptive infrastructure that scales without sacrificing practitioner oversight. A layered architectural model is proposed, grounded in applied patterns from healthcare data exchange and financial transaction processing environments. The framework is organized around a suggest–review–approve–learn collaboration model that preserves human accountability at high-consequence decision points while delegating routine validation and monitoring to trained models. Evidence from recent peer-reviewed literature suggests that AI-augmented pipeline designs may offer meaningful improvements in processing efficiency and quality consistency relative to static rule-based baselines. A phased adoption roadmap is provided for organizations in regulated industries where auditability and compliance are non-negotiable constraints on automation design.