Generative AI has the potential to accelerate intellectual and creative work across various industries, with McKinsey estimating it could add up to $6.1 trillion to the global economy annually in the coming decades. However, generative AI relies on reliable access to data, particularly unstructured data, which is often difficult to integrate due to its varied formats and lack of standardized naming conventions. Automated data integration provides a solution for this challenge, allowing teams to easily connect structured and unstructured data from disparate sources into data lakes. This enables the use of generative AI models that can extract semantic relationships from large volumes of text, offering practical applications such as retrieving information, summarizing content, and acting as a copilot for engineers. By leveraging automated data integration, organizations can unlock the full potential of generative AI while ensuring the quality and governance of their data.