Geoff Jackson, Business Development Director at Bowers Group
For years, measurement was about proving quality. Today, it's becoming a source of operational insight. Manufacturers want a clearer understanding of what's happening on the shop floor, and AI is one of the tools helping them get there.
But AI isn't where that journey starts. Before manufacturers can benefit from artificial intelligence, they first need information they can trust. Reliable information starts with consistent measurement.
I've lost count of the number of factories where we've discussed AI before anyone has asked how the inspection data is being collected. That's because the conversation has changed. Customers aren't just asking about measurement anymore, they're asking how it fits into production, how inspection information can be shared across the business and how skilled engineers can spend less time collecting data and more time improving processes.
That changes what's expected from measurement. Manual inspection inevitably introduces variation between operators, shifts and recording methods. Automating measurement removes much of that inconsistency, giving manufacturers greater confidence in the information they're using every day.
Once inspection results are available in a consistent digital format, they become useful for far more than traceability. Patterns that would be almost impossible to spot in paper records or disconnected spreadsheets suddenly become visible. Measurement stops being something that's reviewed after production and becomes part of improving the process itself.
The real value of automation isn't replacing skilled engineers, it's making better use of their expertise. By reducing repetitive inspection and manual data collection, engineers have more time to focus on the decisions that require experience and judgement.
A conversation with one customer brought that into focus. They came to us wanting to explore AI and advanced analytics, but it quickly became clear the real issue wasn't AI at all. Inspection results were still being collected manually, making it difficult to build a consistent picture of process performance. Before AI could deliver any real value, the measurement process itself needed to be addressed.
AI doesn't replace good measurement. It depends on it.
Artificial Intelligence will continue to evolve, but it isn't a shortcut around poor measurement. If the underlying information can't be trusted, neither can the conclusions. Good measurement isn't becoming less important, it's becoming essential.