The blog discusses the integration of Google's Population Dynamics Foundation Model (PDFM) embeddings into CARTO Workflows, enhancing spatial analytics without requiring coding skills. It presents two use cases demonstrating how these embeddings improve predictions, specifically total liquor sales in Iowa and product-specific sales of Hawkeye Vodka. The integration allows users to leverage high-dimensional spatial representations that capture behavioral, demographic, and functional patterns, offering an alternative to traditional sociodemographic data. The results show that combining PDFM embeddings with demographic data yields the most accurate predictions, highlighting the embeddings' ability to capture latent spatial patterns not easily discernible through conventional data sources. This development aims to democratize access to advanced geospatial modeling, making spatial AI more accessible and impactful for various industries.