Objective Advances in artificial intelligence (AI) have demonstrated potential to improve medical diagnosis. We piloted the end‐to‐end automation of the mid‐trimester screening ultrasound scan using AI‐enabled tools. Methods A prospective method comparison study was conducted. Participants had both standard and AI‐assisted US scans performed. The AI tools automated image acquisition, biometric measurement, and report production. A feedback survey captured the sonographers' perceptions of scanning. Results Twenty‐three subjects were studied. The average time saving per scan was 7.62 min (34.7%) with the AI‐assisted method (p < 0.0001). There was no difference in reporting time. There were no clinically significant differences in biometric measurements between the two methods. The AI tools saved a satisfactory view in 93% of the cases (four core views only), and 73% for the full 13 views, compared to 98% for both using the manual scan. Survey responses suggest that the AI tools helped sonographers to concentrate on image interpretation by removing disruptive tasks. Conclusion Separating freehand scanning from image capture and measurement resulted in a faster scan and altered workflow. Removing repetitive tasks may allow more attention to be directed identifying fetal malformation. Further work is required to improve the image plane detection algorithm for use in real time. Key points What is already known about this topic? Artificial intelligence has shown great promise in medical diagnosis, including in antenatal settings Most published work has been based on retrospective data, with very little work exploring how AI might be used in real‐life clinical practice What does this study add? We have shown that real time use of AI in obstetric ultrasound scanning is feasible and can fundamentally disrupt how sonographers perform the scan AI‐assisted scans were significantly faster than standard manual scans Automatically measured fetal biometry was highly accurate The performance of automatic standard plane acquisition needs to be improved before these tools can enter mainstream clinical use