Understanding how the spine moves is fundamental to improving injury prevention approaches, expediting detection, and optimising care. However, the most important internal mechanics are difficult to observe directly in real-world settings. This presentation will trace a translational pathway from controlled laboratory experiments and in-silico modelling towards AI-assisted approaches that make complex biomechanical information more measurable, interpretable, and actionable. Using examples from spinal injury biomechanics and related pre-clinical and translational research, I will explore how emerging tools such as computer vision, machine learning, and advanced modelling can extend what we are able to measure, reveal patterns that are difficult to identify using conventional approaches, and bridge the gap between experimental observations and real-world application. Used properly, AI provides an enabling layer that can augment biomechanical research across the translational pipeline. By combining rigorous biomechanics with increasingly capable AI methods, we have an opportunity to move beyond simply describing spinal injury and treatment response towards practical tools that can better inform prevention, research, training and clinical care.