BigQuery has introduced augmented analytics Table-Valued Functions (TVFs) to automate complex data analysis. These functions combine AI, ML, and statistical methods for faster insight discovery directly within your data. Six new TVFs are available: AI.KEY_DRIVERS identifies metric change drivers, AI.CAUSAL_EFFECT quantifies action impacts, ML.CORRELATION measures metric relationships, ML.DETECT_CHANGE_POINTS finds data shifts, ML.TREND separates underlying growth from noise, and ML.SEASONALITY uncovers predictable cycles. These TVFs can be chained together for multi-step analysis. An example demonstrates using ML.DETECT_CHANGE_POINTS, AI.KEY_DRIVERS, and AI.CAUSAL_EFFECT to analyze bikeshare trip data. This chaining process first identifies significant shifts in trip volume, then pinpoints the factors driving those shifts, and finally quantifies the actual business impact. The TVFs are also integrated with Conversational Analytics, allowing natural language queries to trigger complex, multi-step data investigations. Examples illustrate how conversational prompts can leverage these TVFs for correlation and trend analysis on taxi and liquor sales data. These functions significantly enhance the ability to understand and act on data-driven insights efficiently.