How to identify the most common mistakes in GenAI data preparation?
Identifying common mistakes in GenAI data preparation is crucial for AI success. The primary errors—insufficient data cleaning, poor structure, inadequate labeling, and misaligned formats—can reduce model accuracy by up to 40% and significantly increase processing time. Rather than costly system rebuilds, managed AI solutions offer a practical alternative. These intelligent systems integrate with existing workflows to automatically identify and fix data quality issues through automated checks, intelligent mapping, and adaptive cleaning processes. By addressing these preparation mistakes incrementally, organisations can improve their AI outcomes within weeks whilst maintaining operational continuity and avoiding the risks of complete system overhauls.