Breast cancer screening is crucial in the UK, but a shortage of radiologists threatens the program's future. Research explored the use of AI to aid breast cancer screening, addressing this challenge. Two companion studies analyzed an AI-based detection system, assessing standalone performance and integration feasibility. The first study evaluated the AI system's ability to detect cancer, showing higher sensitivity than human readers. The AI identified 25% of interval cancers missed by traditional methods, with no demographic disparities. A second study compared the standard double-read workflow to an AI-assisted approach. The AI-enabled workflow demonstrated non-inferior sensitivity and specificity, reducing human reading workload. The AI-enabled workflow offered a significant reduction in reading time, potentially easing the burden on radiologists. The study also revealed that human arbitration sometimes overruled correct AI decisions, highlighting the need for improved explainability. These studies suggest that AI can improve cancer detection and reduce workload. AI’s effective deployment requires managing operational challenges and data drift. This work supports the potential for sustainable healthcare via AI and human collaboration.