AI is already changing the way industrial organisations think about maintenance. Analysing equipment data, identifying patterns and detecting potential failures before they occur are now realistic capabilities. But, as with most things, the sum of the parts determines the outcome. For AI to reach its potential in an industrial setting, infrastructure, data and people must also fall into place. This holds particularly true in South Africa, where many industrial facilities still comprise older equipment and communication systems.
“South Africa has a vast legacy of industrial installations, and the challenge lies in integrating these with modern technology,” says Paul Steyn, field services projects manager for sub-Saharan Africa at Schneider Electric. “We are still heavily dependent on human intervention to operate equipment correctly. AI cannot simply be added to an industrial environment and expected to deliver immediate results.”
Three layers for effective AI
Effective AI implementation in an industrial environment requires three critical layers: intelligent equipment, a robust communication network and a common operating system capable of converting large volumes of data into usable information.
“In a large plant, thousands of data points are constantly being sent to a central system. The challenge is deciding how to use that data. How do you translate it into information that makes sense for the operator on the floor, while also presenting it in a way that a chief executive officer or chief financial officer can connect to the financial performance of operations?” says Steyn.
New industrial facilities can be designed around modern Ethernet, fibre and other communication technologies. Older facilities, however, may have accumulated multiple communication topologies and network infrastructures over decades. “The first step is to properly assess the communication network infrastructure on site. From there, decide which elements you want monitored through the AI interface and predictive maintenance systems. Finally, make sure you have a robust, end-to-end communication backbone that connects every point reliably,” Steyn explains.
“Companies that want to integrate AI and intelligent equipment need a strong onsite communication backbone, which often means upgrading or migrating to a modern topology that can support AI-enabled systems. Schneider Electric can help facilitate this assessment to ensure the infrastructure is ready for next-generation technology.”
Unlocking the early warning system
Where the foundations are in place, predictive maintenance can deliver tangible operational benefits. Traditional planned maintenance works according to fixed intervals, with technicians conducting scheduled inspections and servicing. These interventions are necessary but can require planned downtime even when equipment does not actually need attention at that point.
“An AI predictive maintenance system can flag potential failures in electrical equipment before they happen. This allows you to service equipment when needed, rather than on fixed intervals,” says Steyn. It is also important, however, to note that AI can identify potential failure but cannot independently resolve it. “If an electrical motor shows signs of failure and AI predicts it will break down soon, human judgement is still needed to decide the best time to shut the plant for repair or replacement.”
“Predictive maintenance shifts downtime from sudden breakdowns to planned intervention, providing a window to plan more effectively. It is an early warning system, not a guarantee against downtime.”
Building a skilled workforce
There is understandable concern among people working in industrial environments that automation and intelligent systems could eventually replace their roles. Steyn argues that the more realistic future is one where skills evolve alongside the technology. Rather than removing people from the equation, organisations need to consider how their existing workforce can be equipped to work alongside increasingly intelligent systems.
“We are seeing larger organisations with extensive installations prioritising personnel training to both support systems and gain the most from AI integration,” says Steyn. “In the end, industrial workers will still need to understand equipment, maintenance and safety, but they will also need to understand how software, data and AI interact with physical systems.”
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