Background Studies into factors influencing CVS achievement depend on large volumes of video data and granular anatomical annotations, which are often limited by the burden of inefficient manual work. Artificial intelligence (AI) has the potential to radically scale the size of clinical studies by automating operative video analysis. Methods 481 videos of laparoscopic cholecystectomy were recorded at Christchurch Hospital. AI algorithms analysed the videos, marking time points where the cystic duct and cystic artery were visible, and where the key phases of the surgery began. Nine timing metrics were derived based on the outputs of the AI algorithms and stratified by surgical experience and case complexity as graded using the North Shore Grading scale. Results The time from the start of the Dissection of Calot’s Triangle (DCT) phase to simultaneous identification of the cystic duct and cystic artery increased with complexity grade. There was a significant increase in the proportional duration when the cystic duct and cystic artery were simultaneously identified in complex patients performed by consultants compared to trainees. The cystic duct was commonly identified prior to the cystic artery independent of complexity grade. Across all intraoperative grades, consultants identified anatomical structures faster than trainees. Conclusion Surgical video review offers the potential of significant new insights with substantive benefits to patients but is often limited by the costly effort of manual analysis. This paper correlates AI-derived analytics with clinical factors as a demonstration of the real-world utility of AI video analysis, within the context of critical structure detection in laparoscopic cholecystectomies.