AI

Understand how resolving context at the source can minimize costs and boost accuracy in AI manufacturing processes.

As agentic AI moves from advising to acting in manufacturing, organizations must be able to explain and defend every decision.

AI pilots demo well but fail to scale because they connect directly to operational data instead of a governed execution layer.

Effective AI in manufacturing depends on a strong MES foundation with live, contextual production data rather than a chatbot layered over dashboards.

You can vibe code an app. The question is whether you can (or should) vibe code a full MES, and the answer has less to do with how good the code generator is than with what an MES actually is.

The recently published eBook Industry 4.0 and AI: What You Can Learn from the Leaders paints a revealing picture of where manufacturing stands today. Progress is real, investment is flowing, and ambition is high.

Most leadership teams spend their days improving today’s output and planning the next round of investments. Necessary priorities, yet increasingly insufficient. The real competitive battle is shifting somewhere far less visible: toward the factories that are quietly learning every single day and those that are not.

The manufacturing floor is undergoing a digital revolution, and at the forefront of this change is the emergence of AI Copilots.

The shop floor is a very complex environment, involving  a whole range of different actors with different perspectives and knowledge domains. One of the biggest hurdles for achieving results lies in bridging the gap between business and production requirements and the ability to deconstruct them into software requirements.