Overview on ongoing machine learning projects in QCD

Artificial intelligence (AI) is playing an increasingly central role in nuclear physics, offering powerful tools to enhance every stage of experimental workflows in Quantum Chromodynamics (QCD). Applications include, but are not limited to, optimizing detector design, enabling real-time data acquisition, event selection and reconstruction, improving analysis pipelines, and accelerating simulations. This talk will provide an overview of ongoing AI and machine learning (ML) efforts across the QCD division's experimental programs, highlighting practical successes, persistent challenges, and opportunities for cross-cutting collaboration. By showcasing diverse applications and bridging the communication gap between ML experts and experimentalists, the talk aims to contribute to a shared foundation for future innovation.