This chapter explores the practical applications of GenAI in datadata processing and analysis. AI cannot fully replace human researchers yet, but it makes working with large and complex datasetsdatasets easier and less prone to errors. One of the most significant affordances of AI toolsAI tools in data processing is identifying patterns, trends, and anomalies that even the keenest human could miss. This chapter uses real-world examples and custom GPT prompts to illustrate how researchers can leverage this affordance to automate data cleaning and preprocessing tasks. It demonstrates how AI can enhance statistical analysisStatistical analysis, modelling, and visualisation. The chapter also provides an overview of advanced AI techniques such as machine learningmachine learning modelsmodels for predictive analytics and natural language processingnatural language processing for unstructured textual data. In doing all of these, the chapter also addresses the challenges associated with AI-driven data analysis, including issues of transparency, reproducibilityReproducibility, and the potential for algorithmic biasbias. Best practices for handling sensitive data and dealing with privacy are also highlighted.