Objective: To investigate the value of artificial intelligence (AI) in grading vascular stenosis on head and neck CT angiography (CTA) and to compare its diagnostic performance with that of frontline and second-line radiologists in a real-world clinical workflow. Methods: A total of 212 patients who underwent both head and neck CTA and digital subtraction angiography (DSA) were retrospectively enrolled. Using 3,816 vascular segments as the unit of analysis, DSA was used as the reference standard. The diagnostic performance of frontline radiologists, second-line radiologists, and the AI system was compared for three binary endpoints: ≥50% stenosis, ≥70% stenosis, and complete occlusion. McNemar tests, receiver operating characteristic (ROC) curve analyses, and DeLong tests were performed. Further analyses compared AI with frontline radiologists of different seniority levels and evaluated its performance in the anterior and posterior circulation. Results: Second-line radiologists achieved the best overall diagnostic performance across all three endpoints. The overall performance of the AI system was comparable to that of frontline radiologists but remained inferior to that of second-line radiologists (P < 0.001). For the three endpoints, the AUCs of frontline radiologists, second-line radiologists, and the AI system were 0.841, 0.910, and 0.838 for ≥50% stenosis, 0.893, 0.950, and 0.877 for ≥70% stenosis, and 0.931, 0.964, and 0.914 for complete occlusion, respectively. No significant differences in AUC were observed between AI and frontline radiologists (P > 0.05), whereas AI differed significantly from second-line radiologists for all endpoints (P < 0.001). After stratification, no significant AUC differences were found between AI and either residents or attending radiologists (P > 0.05). However, for the ≥50% and ≥70% stenosis endpoints, AI showed better paired classification performance than residents. AI performed relatively stable in the anterior circulation, while its sensitivity decreased in the posterior circulation. Conclusion: AI demonstrates adjunctive value in grading vascular stenosis on head and neck CTA. Its overall performance is comparable to that of frontline radiologists but remains inferior to that of second-line radiologists. At the current stage, AI is better suited as an assistive tool for frontline interpretation and as a prompt before second-line review.