The next industrial evolution is here – an era where human expertise merges seamlessly with cutting-edge technology. Semi-automation leverages innovations such as the Industrial Internet of Things, Artificial Intelligence, digital twins and customised large language models, alongside industrial robotics. This dynamic partnership between workers and smart machines is transforming industries, enabling faster responses to opportunities, heightened efficiency, and reduced operational costs. Simon Bennett, Director of Innovation and Incubation at AVEVA, explores how semi-automation is shaping the future of work.

In the face of continued supply chain challenges, energy limits, and shifting consumer demands, this blend of human ingenuity and trusted machine-generated insights will bring about safer workplaces, lower production costs, and more sustainable operating models.

Semi-automation will rely heavily on currently available technologies such as the Industrial Internet of Things, Artificial Intelligence, digital twins, and customised large language models, alongside industrial robotics.

With these technologies, massive quantities of raw information can be analysed, contextualised, and visualised to create an end-to-end view of the business value chain, providing actionable insights. Together with human judgement, these insights become industrial intelligence.

In effect, human workers are now partnering with smart machines to do their jobs better, respond to opportunities faster, and achieve more with less. This is the foundation of semi-automation. How do advanced technologies amplify human ingenuity?

Let us look at the four major digital technologies transforming industrial operations today by delivering benefits such as supply chain visibility, predictive warnings, and operational recommendations.

IIoT can be compared to an industrial central nervous system. Industrial devices such as connected sensors, valves, or switches are now equipped with the capabilities to send data to HMI and SCADA systems or the cloud. When these data streams are aggregated into a single source of truth, AI and machine learning can easily analyse and contextualise them, delivering real-time operational oversight and enhanced human decisions thanks to predictive alerts.

For example, teams at Duke Energy used insights from 30,000 sensors to develop 10,000 models to identify plant failures before they occurred. With 385 predictive finds over three years, it saved $ 45 million.

Digital twins, defined as virtual replicas of physical entities or processes, enhance this data

Customised LLMs, unlike their generic counterparts, are fine-tuned for specific industrial enterprises.

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