Over the past decade, while the data engineering field has seen significant advancements such as the widespread use of cloud warehouses, the development of reverse ETL tools, and the integration of software engineering best practices like dbt, the process of data migrations has remained largely unchanged and still heavily reliant on manual efforts. This traditional approach involves highly skilled engineers spending extensive time manually converting code, often encountering unforeseen complexities and errors that can compromise data integrity and extend project timelines beyond expectations. Despite the evolution of data infrastructure and the availability of AI technologies that could potentially streamline these migrations, human-led processes persist, leading to inefficiencies and increased risks. The text argues that while humans excel in understanding business contexts and solving unique problems, they are not ideally suited for the repetitive and error-prone nature of data migrations, suggesting a shift towards leveraging AI for more reliable and efficient outcomes.