Artificial intelligence (AI) is increasingly embedded in healthcare delivery, yet its strategic value is often misunderstood as a purely technical upgrade rather than an operational capability. This article examines AI-powered diagnostic triage through a case study of a Malaysian healthcare technology provider that deploys pattern recognition systems to prioritize high-risk pneumonia cases from large volumes of chest X-rays. Drawing on interviews with the firm’s CEO and CTO, the article illustrates how AI-enabled triage reshapes clinical workflows, alleviates radiologist bottlenecks, and mitigates institutional risk. Beyond efficiency gains, the case highlights managerial considerations related to workflow customization, data governance, and change management. The findings offer practical insights for hospital administrators, healthcare investors, and technology leaders seeking to integrate AI into scalable diagnostic infrastructure while maintaining clinical trust and operational accountability.