Gravitational-wave astronomy has entered a new phase of discovery, with detectors recording hundreds of compact binary coalescences and preparing for even more sensitive observing runs. As the scale and complexity of the data continue to grow, so do the demands on search pipelines to be fast, efficient, and robust. Recent advances in artificial intelligence, particularly deep learning and neural networks, present exciting opportunities to enhance gravitational-wave detection capabilities. In this talk, I will highlight emerging machine learning approaches, discuss their current limitations, and explore what it would take for artificial intelligence methods to integrate seamlessly into production-level pipelines.