Two years ago, the dominant conversation around AI in enterprise software was about pilots.

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.

Manufacturers have spent millions investing in safer equipment, smarter automation, and increasingly sophisticated operational technology.

Businesses running equipment that depends on actuators, especially in high-cycle or nonstop operations, already know this: It’s common for actuators to fail without showing any warning signs.

We often think of AI as a technological revolution that will transform industries, disrupt jobs, and change the nature of competitive advantage.

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

During a recent visit to a brewery in Dublin, I was stopped by one statement displayed on the tour: “The quality of our advertising must be equal to the quality of our beer.”

All too often, leaders seek to build support for an idea by talking—a lot. They may go on and on about why the decision is a good one, detailing its benefits, the reasons others should support it, and the path forward.

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.

Teams building software as a medical device (SaMD) tend to think of ISO 14971 as the hardware team’s problem. Risk management files, FMEA tables, severity scores: all quality and regulatory territory, while the engineers close Jira tickets.