Quality Engineering (QE) is at a critical inflection point. As enterprises accelerate digital transformation, adopt cloud-native architectures, and embed artificial intelligence (AI) into core operations, the traditional models of quality assurance are proving insucient. Testing, once a downstream activity, has become a strategic enabler of innovation, speed, and resilience. Global industry research shows that while organizations are rapidly experimenting with AI-driven QE, only a small percentage have successfully scaled these capabilities across the enterprise . At the same time, automation remains fragmented, test data continues to be a bottleneck, and quality metrics often fail to align with business outcomes.