While attention does seem to be all you need to attain high-performing models, spanning many modalities, human attention is not all you need to prepare datasets at scale. What you need is scalable and flexible data workflows to automate the process. Established solutions to, e.g., computer vision, audio, and text — including the latest advancements in foundation model capabilities — open up new possibilities to transform AI Data workflows. To name but a few automations, high-volume manual actions like cropping, transcription, and audio-video pairing, as well as more complex reasoning tasks such as video insight extraction and content evaluation. By chaining multiple models together, teams can build custom data engines to create novel, high-quality datasets at scale. On a fixed 100hour human labor budget, we showcase how a high level of automation and constrained budget of human attention spent wisely, consistently outperforms the traditional methods for building datasets.