Recently, transformer-based foundation models have excelled across a wide range of recognition and generation benchmarks, yet real industrial impact requires robust tech transfer. Adapting them to heterogeneous industries demands domain-specific fine-tuning, reliable MLOps, and abundant, high-quality data. Conventional IID benchmarks are increasingly saturated, prompting evaluations that probe out-of-distribution and long-tail behavior. Both challenges hinge on curating and exploiting broader, deeper — “Foundation Data.” This workshop gathers academia and industry to examine methods for constructing high-quality datasets, refine model-adaptation pipelines, and design novel evaluation tasks grounded in Foundation Data, aiming to unlock new horizons in AI research and application.