AI in CQV is gaining momentum because the pressures on the industry are immediate. Manufacturing must scale faster.

Agentic AI promises to unlock massive productivity and efficiency gains for organizations, but those results are not being realized yet. Many see MCP (Model Context Protocol) as the key, but it will only provide the desired business impact if implemented in the right way. This article explores critical areas that will help organizations achieve profound business results through agentic AI and MCP.

Engineers should rely on AI to automate tedious tasks like healing broken tests automatically and generating boilerplate test cases. But humans should remain in charge of which tests are executed and how to react to complex test failures.

Real-time data transforms AI model drift management from periodic model maintenance into continuous system operations, enabling faster detection, smarter adaptation, and safer deployment of AI at scale.