Hi Jan,

Most AI projects fail because of data issues, not model problems. If your teams are still copying data between systems, fighting vendor lock-in, or can't guarantee data quality at scale, your infrastructure isn't ready for production AI. 

We've published a comprehensive guide on what makes data truly AI-ready—covering everything from open formats like Apache Iceberg to federated architectures that eliminate ETL bottlenecks. Whether you're training LLMs or deploying real-time agents, this breaks down the architecture, governance, and performance requirements that separate successful AI deployments from stalled prototypes.