46 n ARTIFICAL INTELLIGENCE September 2026 www.drivesncontrols.com Which way is AI heading? For some time now AI (artificial intelligence) has been at the top of the agenda for industrial innovation. While generative tools and AI chatbots have captured the public imagination, the technology has evolved for industrial applications more quietly, but far more profoundly. AI software is already influencing how assets are maintained, processes being optimised, and workforce planning transformed, but seeking and achieving these benefits are two different things. The market is saturated with AI systems that promise value, but may only work in isolation. Without cohesion, cutting-edge pilot projects typically do not translate well into industrial reality. To overcome this, operators need a clear view of how AI is maturing, what defines a scalable project, and how to make multiple initiatives work together to unlock benefits. One example of AI at the cutting-edge is a new breed of dark factories in China with fully automated lines that optimise themselves without needing any human input. Previously this approach was only viable for small-scale pilot operations. The development shows how industrial AI can apply to enterprises in the UK and Ireland that want to improve efficiency across previously unrelated systems. Although China’s dark factories have set a benchmark for industrial AI, they were built from the ground up under controlled conditions and designed around a specific manufacturing model. However, most engineering and industrial leaders are working with brownfield and legacy sites, so the success of next-generation industrial AI will be defined by their readiness to scale. Businesses need to build the necessary foundation before deploying AI. Without this, pilot projects will end at the initial scope, without delivering further value or integrating into other operations. It’s important to view AI readiness not as a single step, but as its own process that requires: n Structured data AI depends on consistent and contextualised information. The context is important because it reflects how processes operate together, rather than in single applications. n Ownership of systems This is needed for defined accountability across IT and OT, ensuring that any AI insights are acted on by the right people at the right time. n Alignment between digital strategies and OT realities AI initiatives must be grounded in real operational constraints so that insights are relevant to the day-to-day needs of operators. n Strong guardrails These are necessary to ensure that any AI hallucinations do not impact critical production systems. Putting this foundation in place means that AI will be able to operate across fragmented environments, accounting for legacy systems working alongside new assets, as well as data and workforce silos, and complicated supply chains. Achieving this requires confidence that models can be refined to meet changing needs. Nextgeneration systems will build on small and discrete pilot projects and integrate with strategy to apply across an enterprise. Semi-autonomous AI Fully autonomous operations are possible, but only under specific conditions – so far. Semi-autonomous operations however are within reach, using the building blocks that companies have created with modernisation projects in recent years. In semi-autonomous operations, an AI agent is deployed to monitor conditions, identify optimisation, and recommend actions in real time. This offers the best of both worlds as a business can start seeing the benefits of AI without a complete overhaul. It’s a case of AI systems supporting human decisions rather than taking over responsibility. The important distinction between semiautonomous AI and a traditional pilot project is that the AI agent is not solving a specific problem in a controlled environment. Instead, it can have a wider impact – for AI software is already influencing how industrial assets are maintained, processes are optimised, and workforce planning is transformed. Charlotte Smith, technical manager at SolutionsPT, outlines how AI is shifting from a future ambition, to a scalable technology that is reshaping industrial operations.
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