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PHYSICAL AI: FROM AUTOMATION TO AUTONOMY
Jan Van Den Bossche, Regional Vice President, Software & Control EMEA, Rockwell Automation, discusses how Physical AI is moving industrial operations from automation to autonomy
A
rtificial intelligence has become one of the most discussed technologies in manufacturing, but the conversation is rapidly
evolving beyond software and analytics. Increasingly, manufacturers are exploring how AI can influence and improve physical operations on the plant floor. This emerging capability is often referred to as Physical AI. At its core, Physical AI describes the application of artificial intelligence to systems that interact directly with the physical world. By combining industrial data, sensors, machine vision, robotics, control systems and AI models, manufacturers can create systems that both monitor conditions and make informed decisions, taking actions in real time.
The evolution of automation Importantly, Physical AI is not a replacement for industrial automation. Rather, it represents the next stage in automation’s evolution. Traditional automation systems excel at executing predefined tasks safely, reliably and consistently. Physical AI builds on that foundation by introducing greater intelligence, contextual awareness and adaptability. The result is a progression from automation toward autonomy, where systems can continuously optimise performance while operating within trusted industrial frameworks. The growing interest in Physical AI is being driven by the convergence of several technological advances. AI models have become more capable, while industrial edge computing now makes it possible to deploy AI closer to production environments where speed, resilience and cybersecurity are critical. At the same
12 September 2026 | Automation
time, developments in robotics, sensors, industrial connectivity and machine vision have created new opportunities for machines to perceive and respond to their
surroundings. Digital twins are also playing an
increasingly important role. Manufacturers can use virtual models to test and validate AI-driven behaviours before introducing them into production environments. The momentum behind these technologies is reflected in industry investment trends. According to Rockwell Automation’s 2026 State of Smart Manufacturing Report, 96% of EMEA manufacturers have either adopted or plan to adopt AI and machine learning technologies. Nearly nine in ten report that AI is already enhancing operational technology systems, demonstrating that AI is becoming a core component of modern industrial operations rather than a future aspiration.
Despite the excitement, widespread adoption of Physical AI is not without challenges. One of the most significant obstacles is operationalising AI safely and at scale. Manufacturers must have confidence that systems will behave predictably under both normal and abnormal conditions. Data readiness remains another critical
factor. Many organisations continue to struggle with fragmented information environments and limited visibility across operations. The State of Smart Manufacturing Report found that
manufacturers effectively utilise only 42% of the data they collect, highlighting a substantial opportunity to unlock additional value. Cybersecurity and workforce readiness are equally important considerations. Successful deployments require not only technology investments but also strong governance, skilled employees and clear change- management strategies. The most effective implementations enhance human expertise rather than attempting to replace it.
Measurable value Today, Physical AI is already delivering measurable value through applications such as predictive maintenance, AI-enabled quality inspection, process optimisation and autonomous material handling. Looking ahead, manufacturers are likely to see increasing levels of bounded autonomy, where AI manages specific operational tasks within clearly defined safety and performance limits. For organisations beginning their Physical AI journey, the best starting point is not the technology itself but the business challenge they are trying to solve. Whether the objective is reducing downtime, improving quality, increasing throughput or optimising energy consumption, the most successful projects focus on measurable outcomes. Those that combine strong data foundations with a clear strategy for people, processes and technology will be best positioned to realise the full potential of this next generation of industrial innovation.
Rockwell Automation
www.rockwellautomation.com/en-gb
automationmagazine.co.uk
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