INDUSTRIAL AI 101 SERIES
How Does AI Bring Value to Industrial Companies?
Elena Samuylova
Chief Product Officer, Mechanica AI
In our earlier blog in the Industrial AI 101 series, we explained the differences between various concepts related to data analytics. Now, let us look at how exactly AI can bring value to the industrial enterprises – be it energy, oil and gas, metals, mining, chemicals or any other manufacturing company.
The Backbone of the Industry Transformation
In practical terms, AI is a tool that can efficiently make complex, repetitive decisions based on experience. Learning from examples, AI can classify, predict, recommend and prescribe, and thus power all sorts of automation. All it needs are data to train on and a goal set to optimize.

This definition may sound quite broad – not without reason. The range of tasks AI can solve varies from predicting the change of a single metric in a production process a few minutes ahead to powering fully autonomous vehicles.

However, the specific form and speed of AI adoption for different use cases will vary. In some areas, AI can be plugged in easily, replacing existing rule-based systems and expert judgement. In others, AI will become an enabler of new products and business models, powering important features and capabilities but also requiring other building blocks – such as ubiquitous connectivity across the shop floor or new hardware – to rely on.
Operational AI
The somewhat more boring – and definitely less visible than smart robots or self-driving cars – use of AI in the industrial sector comes in the area of operational excellence at the shop floor level. In this case, AI will often affect only the operational decision-making layer while the process itself remains the same.

For example, a machine learning model can be trained to predict the expected product quality at the early stages of the process. The forecast is then channeled into the existing system to replace hand-coded rules that were previously used to trigger selective quality control. AI will then increase the proportion of defects discovered in time, leading to improved lead times and optimizing production costs.

In this example, there is no change to the actual process or new sensors installed – which results in a fast implementation cycle and a compelling business case.

Such applications can be integrated across the shop floor and plant management systems – from optimizing production routes to smartening up real-time process control. By factoring in thousands of variables and learning from experience, machine learning techniques can better account for the uncertainties and fluctuations of the real-world production environment.
This leads to an additional two-, three- or even ten-percent improvement of the core performance metrics for each process affected. When combined, the scale of effect is not to be underestimated.
Where To Start?
AI will affect the industry from top to bottom, and business executives are now faced with the complex task of balancing big-picture concepts with getting real, tangible results. When it comes to the speed of execution and low entrance threshold for the technology, production process optimization has a clear advantage – not to mention that operational efficiency is one of the key value drivers for the industry! An added benefit is the ability to learn from practice and grasp the limitations and important aspects of the technology, which is essential to design a thorough strategy and build the necessary in-house capabilities.
Interested in how AI can bring clear business value to you this year? Reach out to us to explore the possible operational use cases for your industry.
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