Pipelines built for analytics break under AI workloads. The data volumes are larger, the transformation logic is more complex, and the tolerance for inconsistency is lower, a bad batch in a dashboard is an inconvenience; the same in a training set compounds downstream. This keynote covers what scalable pipeline architecture looks like when the end consumer is an AI system, from ingestion and transformation through to feature delivery and model serving. We'll also address where conventional pipeline tooling holds up, where it doesn't, and the architectural decisions that determine whether a pipeline scales or becomes the bottleneck. Key takeaways: → The specific ways AI workloads stress pipeline architecture that analytics workloads don't → Where conventional orchestration tooling hits its limits and what teams are doing about it → The architectural decisions at ingestion and transformation that determine downstream model performance