Navigating Data Engineering Obstacles in AI Automation

Artificial Intelligence (AI) systems have experienced rapid adoption across sectors like healthcare, finance, and e-commerce. The next evolution in AI's practical application is the automation of these systems. AI automation heavily depends on the quality, volume, and meticulous management of data for training, optimization, and validation of data science models. The success of AI automation is significantly tied to the ability to handle data ingestion efficiently, ensure real-time processing, and respond promptly to data changes.A multifaceted data engineering approach is necessary to help practitioners implement automated AI. Robust data engineering practices are essential to ensure scalability, efficiency, and reliability in AI applications and their automation. Comprehensive investigations into the impact of these practices on AI automation are crucial to developing future-proof, reliable AI systems.This paper highlights key areas of data engineering critical to AI automation, the challenges they present, and their impact on AI system performance. Each problem is accompanied by a solutions section, detailing the scope of automation and human involvement required, along with the current tools in use.