The U.S. Food and Drug Administration scrutiny of AI and machine learning in medical devices is intensifying. Yet most companies still apply failure mode and effects analysis (FMEA) methods designed for deterministic hardware failures.

Artificial intelligence (AI) workloads are reshaping the scale, speed, and risk profile of data center construction.

Midtier life sciences companies are spending more than ever on quality and regulatory technology, yet many are paying enterprise prices for capability they never use.

For years, many medical device manufacturers approached U.S. Food and Drug Administration (FDA) inspections through a familiar lens: Prepare documents, review subsystem requirements, rehearse likely questions, and demonstrate compliance against a known framework.

ISO has set its focus on revising three key management system standards this year. It started with ISO 14001, which covers environmental management and was published on April 15, 2026. ISO 9001 for quality management will follow in the fall.

A line operator in a chemical packaging plant notices a torque inconsistency on a capping head.

Audit week has a way of revealing gaps that stay invisible during normal operations. Your methods are sound, and your data look clean. Then the assessor asks for the calibration certificate on the torque wrench used last Tuesday, and someone heads off to find it.

In the larger organizations I worked for as a quality leader, supplier auditing was almost always calendar-driven.

FDA inspectors don’t just check your records. They bring their own thermometer.

When a training matrix is built and managed correctly, it’s the backbone of compliance. It ensures that every employee knows how to perform their job and creates the traceability auditors love to see.