Augmented reality has been used in sports broadcasting since the 1990s to enhance viewer engagement through virtual overlays. A key application is virtual advertising, which replaces physical advertisement banners with dynamic digital content, enabling targeted and region-specific advertisements. This technology optimizes advertising space and increases monetization opportunities for broadcasters. However, traditional augmented reality solutions require specialized hardware, such as instrumented cameras and virtual-ready LED panels, along with manual calibration and prior environmental knowledge. These constraints make its implementation costly and less adaptative. In this work, we propose a first fully automated end-to-end pipeline that seamlessly integrates augmented reality advertising into sports broadcasts using only the main camera feed. Our approach leverages state-of-the-art deep neural networks to identify the advertisement banner, estimate camera motion, and dynamically composite virtual content without additional hardware or manual intervention. We validate our pipeline on football broadcasts using our novel SoccerNet-banner dataset, the first dataset for training and evaluating banner segmentation models, and demonstrate high-quality virtual banner replacement on SoccerNet videos. Therefore, our pipeline unlocks new possibilities for personalized content and advances AI-powered sports broadcasting by eliminating hardware dependencies and manual calibration. Our code and dataset are available at https://github.com/SoccerNet/sn-banner.