Tabular data plays a fundamental role in various industries such as finance, healthcare, and scientific research. Traditional analysis relies heavily on structured queries and predefined models, which are limited by schema variability and the need for manual intervention. Foundation models, trained on large and diverse datasets to learn general patterns, offer an adaptable approach to analyze tabular data without requiring task-specific fine-tuning. These models can generalize across different datasets and apply their understanding of table structures to answer queries, summarize data, and extract insights. Vector databases like Milvus store and quickly search high-dimensional numerical representations of data, allowing AI models to retrieve relevant information and improve structured data analysis. Foundation models like TableGPT2 have been shown to simplify data analysis by providing transparent and verifiable results without manually coding complex queries. However, they still encounter challenges such as schema variability, scalability, interpretability, potential biases, and the lack of standardized evaluation methods. By addressing these limitations, organizations can more confidently and effectively utilize structured data for informed decision-making.