AI-Enhanced Data Engineering for Complex Workflows: Lessons from Retail Intelligence and Financial Risk Models

The eruption of data and the need to have real-time analytics have highlighted the importance of data engineering in the current AI-based systems. In the paper, an AI-empowered data engineering paradigm is described, aimed at facilitating the nature of complex data flows, where applied articles are based on retail intelligence systems and financial risk modelling. The proposed solution in the combination of machine learning pipelines, automated quality checks on the data, and adaptive ETL (Extract, Transform, Load) processes enables a wider level of scalability, accuracy, and decision latency in various domains. The framework is viable in the retail industry, where it facilitates dynamic inventory optimization, personalized recommendations and forecasting in sales. In case of financial risk management, it can enable real-time anomaly detection, credit scoring and compliance. The experience revealed in these areas outlines the necessity of strong data governance, explainable AI elements, and real-time orchestration. Compared to the architecture, the proposed one presents substantial gains both in throughput and data integrity and responsiveness of the model, which provides a scalable blueprint that can be implemented in the future to integrate the AI-data engineering application into the high-stakes decision-making environments.