Only 12% of AI-using manufacturers fully integrate AI

The article frames manufacturing's low integration rate against a decade of heavy automation: the International Federation of Robotics reports the global factory robot workforce has more than doubled in the last decade, so robots on the line are nothing new. What is new is the demand that those machines, sensors, and business systems share one continuous flow of data. The article draws a sharp line between an automated factory, which executes pre-programmed instructions in rigid, siloed workflows, and a smart factory, where AI sits as an intelligence layer across Operational Technology and Information Technology.

The obstacles it lists are structural rather than technological. Legacy MES and ERP platforms were not designed to communicate with cloud-native AI applications, and connecting the OT world of PLCs and industrial protocols to the IT world of APIs and databases requires specialized expertise. Factory data is also fragmented across sensors, machines, quality stations, and manual logs, meaning AI models can be fed poor-quality information and produce unreliable output. Because a control-system failure can halt production, cause financial losses, or create safety risks, manufacturers are described as understandably cautious about deploying insufficiently tested software into core operations.

The article's suggested path runs through partners such as SOFTECH that can build the integration and data layers, with the promised payoff in predictive maintenance, AI vision inspection, and more flexible production scheduling. Whether that promise materializes appears to hinge on how quickly the legacy-to-cloud gap can be closed without disrupting live production, and on whether integration projects can demonstrate measurable ROI — a test that will fall first on the manufacturers now running isolated AI pilots and the vendors bidding to connect them.