This guide to video annotation tools for logistics emphasizes the importance of transforming raw visual data into actionable insights using real-world examples, top annotation platforms, and key features that boost accuracy, automation, and operational efficiency in the industry. Video annotation is crucial for training machine learning (ML) models that enable automation, tracking, and operational intelligence in logistics operations. The quality of annotations directly impacts AI performance, and choosing the right annotation tool is essential for building accurate and scalable logistics AI systems. A comprehensive list of video annotation tools specifically designed for logistics applications is provided, including Encord, CVAT, Supervisely, Kili Technology, Label Studio, Labelbox, Labellerr, and more. These tools offer various strengths tailored to different logistics needs, such as multi-object and multi-class tracking, flexible workflow management, collaboration and QA features, scalability, AI-assisted and automated annotation, and support for essential annotation types like bounding boxes, polygons, polylines, and keypoints.