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Organisations are turning to digitalisation and Industrial AI to develop autonomous and semi-autonomous processes and tools that enable new levels of safety, sustainability, and profitability.  Some of the most difficult challenges for local industries include maximising throughput while minimising changeover costs and ensuring on-time delivery of products to customers. AI can help through its ability to consider a multitude of variables at once to identify the optimal solution.  

At AIML, Industrial AI is about pushing the frontiers of what can be done with AI and machine learning, by making new discoveries that will ultimately benefit industry. By working with local industries and using AI to help increase safety, enhance supply chain management, improve performance, and cut downtime, our goal is to help businesses reach the highest levels of operational efficiency.

The Industrial AI program is supported by the South ×îÐÂÌÇÐÄVlogn Government through the Department of State Development’s Research and Innovation Fund. 

Pivotal needs in Industrial AI 

According to a 2021 article in the MIT Technology Review, there are three pivotal needs driving capital-intensive industries to digitise and implement purpose-built AI systems: ¹ 

Generational shifts in the workforce are creating a loss of operational expertise. Veteran workers with years of institutional knowledge are retiring, replaced by younger workers taught on technologies and concepts that don’t match the reality of many organisations’ workflows and systems. This dilemma is fuelling the need for automated knowledge sharing and intelligence-rich applications that can close the skills gap. 

Industrial organisations are accumulating massive volumes of data but deriving business value from only a small slice of it. Organisations are switching their focus from mass data accumulation to strategic industrial data management, homing in on data integration, mobility, and accessibility—with the goal of using AI-enabled technologies to unlock value hidden in these unoptimised and underutilised sets of industrial data.

Adopting new technologies unlocks new business models that are integral to sustainability, market competitiveness, and new corporate strategies. The more that competitors digitally transform to reap these advantages, the more organisations that don’t transform will be left behind.

Some of the use cases for industrial AI include:

• Self-aware, that can independently measure performance to generate alerts when degradation reaches a critical point or performance is reduced for any reason. 
• Creating for regulatory compliance monitoring in banks and financial institutions.  
• Robotics and that can replace human involvement, thereby increasing efficiency and boosting production while improving human safety.
• Complex supply chain management that increases visibility into every step of the process, including tracking raw materials, inventory, warehouse management, logistics, and last-mile distribution.