AI can dramatically accelerate data science workflows without replacing analytical thinking. Exploratory Data Analysis: prompt Claude/GPT-4o with dataset schema and sample rows, ask for: "Given this sales dataset, what are the 10 most interesting analyses to perform? Include statistical tests and expected insights for each." Code generation: "Write Python code to perform chi-square test between categorical columns and identify top 5 most significant associations. Use seaborn for visualization." Always review and understand generated code before running. Statistical interpretation: paste model output, ask "Interpret these regression results in plain language for a business audience. Identify the most important predictors and explain the practical significance of the coefficients." Visualization: describe desired insight, AI suggests chart type and generates matplotlib/plotly code. Critique generated charts and request improvements iteratively. Data storytelling: provide key findings as bullet points, ask AI to structure narrative for executive audience with appropriate hedging and caveats. Workflow integration: Jupyter AI (JupyterLab extension) enables inline AI assistance in notebooks. GitHub Copilot for data science handles boilerplate code. Limitations: AI cannot discover patterns you do not ask about, may suggest statistically incorrect methods - always validate. Treat AI as a smart intern, not an expert statistician.