Ian Knight, Chief Information Officer at PP Control & Automation (PP C&A) challenges the prevailing, often vague narrative around AI adoption and reframes the conversation around a more practical starting point: operational constraints.

Artificial intelligence is no longer a distant concept for manufacturers. It is already being explored across quotation, production planning, engineering, quality, supply chain and customer service functions. Yet, for many organisations, the gap between experimentation and meaningful operational impact remains difficult to close.

A leading aquaculture specialist – that is changing the way fish are vaccinated safely – has signed a major manufacturing deal.

On paper, defined discipline-specific suppliers can look organised. However, every additional supplier introduces another handoff, and every handoff creates another point where time, quality, communication and accountability can be lost.

Very rarely does growth not surface because an OEM lacks ambition. Shortcomings arise because operating models built to support such ambition don’t evolve quickly enough.

Recent weeks have brought two important industry moments into sharp focus, concluding that demand for AI and automation is rising, but investment, skills, and long-term thinking must follow.

For decades, one question has sat at the heart of operational strategy for machine builders and OEMs: make or buy? It’s a familiar debate and it isn’t the wrong question by any means, but perhaps it is an incomplete one.

In most machine building businesses, change is still treated as an exception. A late-stage drawing revision, component substitution, or wiring tweak discovered during build. Each one is handled, resolved, and signed off. And then everyone moves on. But what if that’s the wrong way to think about it? What if change isn’t the disruption to the system but the system itself?