The advent of Large Language Models (LLMs) has transformed traditional practices in product data science. In this presentation, we explore the complete lifecycle of GenAI tools within product data science teams, using the PayPal digital wallet data science team as an example. Specifically, we focus on the GenAI-powered text-to-SQL model we developed to support data scientists. This tool significantly reduces the time spent on ad-hoc data retrieval tasks—critical for business operations but often resource-intensive. We will delve into our modeling approach and demonstrate the tool’s fast and secure implementation. Additionally, we will discuss our user data collection and feedback processes, and how periodic measurement of the tool performance using a unique question bank for PayPal data has ensured the tool’s continuous improvement. Finally, we will address key challenges in adopting GenAI tools in large organizations, including gaps in data catalogs and the inherent complexities of data structures and lifecycles.