High data quality (DQ) is essential for analytics, compliance, and AI performance, yet its management remains complex, often manual, and resource intensive. This study investigates the extent to which existing tools support AI-augmented data quality management (DQM) in data warehouse environments. To this end, we conduct a systematic review of 151 DQ tools to evaluate their automation capabilities, particularly in detecting and recommending DQ rules in data warehouse - a key component of data ecosystems. Using a multi-phase screening process based on functionality, trialability, regulatory compliance (e.g., GDPR), and architectural compatibility with data warehouses, only 10 tools met the criteria for AI-augmented DQM. Most tools emphasize data cleansing and preparation for AI, rather than leveraging AI to improve DQ itself. Although metadata- and ML-based rule detection techniques are present, features such as SQL-based rule specification, reconciliation logic, and explainability of AI-driven recommendations remain scarce. The study contributes practical guidance for tool selection and identifies critical design requirements for next-generation AI-driven DQ solutions, advocating a complementary shift in focus from “data quality for AI” to “AI for data quality management.”