AI's rise necessitates flexible, real-time data architectures, but traditional tools struggle with massive, multi-modal data. Data lakes offer flexibility but lack consistency, while other architectures create silos and require costly data integration. The industry is shifting towards open lakehouses, with Apache Iceberg emerging as a key open-source table format. Google Cloud, along with partners like Confluent, Databricks, dbt, Fivetran, Informatica, and Snowflake, is committed to this open standard. Iceberg's metadata layer enables efficient query planning, time travel, and data pruning, accelerating insights. Google Cloud has introduced innovations like BigLake tables and a REST Catalog API for Iceberg on GCS. Databricks and Snowflake also offer Iceberg support, allowing interoperability and reducing data latency. This open standard enables any Iceberg-compatible engine to operate on a single copy of data, bridging analytical and operational workloads. By adopting Iceberg, organizations can share data across platforms, reducing data copies and complex pipelines. Customers like Global Payments and Unilever have already benefited from Iceberg's flexibility and efficiency in managing diverse datasets. This interoperability fosters greater efficiency, security, and faster time-to-insight for AI-driven data strategies.