2025, Vol. 6, Issue 2, Part A

As the printing industry transitions into the era of Industry 4.0, traditional quality assurance methods centered on manual inspection and reactive defect handling are increasingly inadequate for the speed, complexity, and customization demands of modern pressrooms. This paper explores the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in real-time monitoring and quality assurance (QA) across print production workflows. Leveraging technologies such as computer vision, IoT sensor networks, and predictive analytics, AI-enabled systems enable proactive defect detection, automated correction, and dynamic process optimization. Applications include in-line visual inspection, root cause analysis, intelligent alerting, and traceable compliance logging. Case studies demonstrate significant gains in defect reduction, throughput, and client satisfaction. However, adoption remains hindered by challenges such as legacy equipment integration, data infrastructure gaps, workforce readiness, and cyber security concerns. Future directions emphasize the role of digital twins, federated learning, cloud-based QA hubs, and sustainability-aware defect prevention. Ultimately, AI transforms quality assurance from a reactive function into a strategic enabler advancing efficiency, brand protection, and environmental responsibility in next-generation print operations.