Traditional data quality systems struggle with modern enterprise data, which often comes from unstructured sources and changes rapidly. Rule-based checks can't keep up with the complexity and messiness of this data. AI-augmented data quality engineering steps in to fill this gap by shifting from deterministic checks to probabilistic and self-learning systems. Techniques like deep learning, generative models, and trust scoring are at the forefront of this transformation. Tools like Sherlock and Sato use deep learning to classify data columns without relying on rigid rules. Sherlock analyzes 1,588 features to achieve high classification accuracy, while Sato enhances this by using context across entire tables. For schema alignment, BERTMap leverages transformer models to create consistent mappings between different data schemas, even when labels differ significantly. Generative AI plays a crucial role in data cleaning and anomaly detection. For instance, Jellyfish is an LLM designed for tasks like error detection and missing value imputation. GANs and VAEs are employed for identifying anomalies and handling missing data, providing more robust solutions than traditional methods. Dynamic trust scoring quantifies data reliability based on factors like validity and freshness. This score adapts based on the specific needs of different applications, ensuring that various priorities—such as compliance or real-time analysis—are met. Tools like SHAP and LIME enhance explainability, making it clear why AI systems take certain actions. This transparency is vital for organizations, especially those in regulated industries, to maintain trust in automated data quality processes. By integrating these advanced techniques, organizations can build more reliable data systems that require less human oversight.