Implementing a new quality management system (QMS) is no small task, especially for life science companies faced with stringent regulatory requirements and a high validation burden.

The arrival of artificial intelligence (AI) in quality management has been met with a mixture of hype and skepticism. Is it just a faster anomaly detector, or is it truly transformative?

The role of quality leaders, and quality itself, is expanding. It includes thinking strategically, solving problems, implementing improvements, and driving change throughout the organization.

When organizations implement an enterprise quality management system (EQMS), the instinct is often to begin with high-visibility processes like corrective and preventive action (CAPA) or supplier quality.

My June 2025 article, “How to Avoid FDA Warning Letters,” points out that inadequate corrective and preventive action (CAPA) is a major reason

I had a great conversation with a friend of mine. He was bemoaning the fact that his company was almost completely dependent on one huge customer. He saw the inherent risks in that relationship but confessed that his organization had a bad habit it couldn’t kick.

We’d be willing to bet your key collaborators aren’t all in the same building. Your team members, contract partners, clients, and suppliers are likely scattered across the globe.

Ninety days to implementation vs. 12 to 18 months with traditional systems: That’s not just an incremental improvement—it’s a complete reimagining of what’s possible in life sciences quality management.

When an issue arises, it’s important to take quick action. Whether that means launching a software patch, pulling a batch, or halting the use of a reagent, it’s critical to tackle the immediate problem.

Today, manufacturing is largely shaped by supply chain volatility, complex labor dynamics, and—like most global industries—the rise of AI.