What once seemed like the future of work is fast becoming present-time reality on factory floors worldwide as artificial intelligence (AI) evolves from experimental technology to practical tools that directly affect daily operations.

In motorsport, performance isn’t defined by a single factor. It’s the sum of countless details, each playing a decisive role when pushing speeds up to 200 mph (320 km/h).

Simply put, we live in a digital world—both in our personal lives and on the job. In manufacturing, challenges abound.

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.

D uring the past decade, manufacturers have wired their plants with sensors, robots, and software. Yet many “AI-driven” systems still miss the mark.

In this article I want to explore an observation on how we make distinctions and what this reveals about the structure of our thinking.

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.

The use of artificial intelligence (AI) models, specifically generative AI, is growing. This has raised concerns about the effects on jobs in various professions. The quality profession is among them.