• • • AI • • •
SOLUTIONSPT: TOP TRENDS FOR NEXT GENERATION AI
BY CHARLOTTE SMITH, TECHNICAL MANAGER, SOLUTIONSPT F
or some time now Artificial Intelligence (AI) has been at the top of the agenda for industrial innovation.
While generative tools and AI chatbots have captured public attention, the technology has evolved more quietly for industrial applications, but far more profoundly. Across every sector, 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 solutions that promise value but may only work in isolation. Without cohesion, cutting-edge pilot projects
24 ELECTRICAL ENGINEERING • MAY 2026
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 at the cutting-edge of AI is a new breed of dark factories in China with fully automated systems that optimise themselves without 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.
Scalable solutions
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 as not a single step but its own process that requires: Structured data as AI depends on consistent and contextualised information. The context is important as it reflects how processes operate together rather than in single applications. Ownership of systems for defined accountability across IT and OT, ensuring that any AI insights are acted on by the right people at the right time. 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. Strong Guardrails to ensure that any AI hallucinations do not impact critical production systems.
electricalengineeringmagazine.co.uk
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