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The Future of AI in Industrial Automation

The industrial automation industry has always resisted change and the early adoption of cutting-edge technologies. Enterprises in this market sector have a tendency to favor using tried-and-true standards and technology to maintain consistent, secure, and safe operations throughout time. However, the emergence of Industry 4.0 has caused a significant shift in the way things are done. Over the past ten years, incremental technological advancements, quick adoption of new systems, and enhanced networking designs have all had an influence on the industrial sector.

Robotics, the cloud, the Industrial Internet of Things (IIoT), and artificial intelligence (AI) are examples of industrial technology that are becoming more commonplace.

However, this article focuses on the future of AI in industrial automation. Industries are quickly implementing AI-based products and services to transform their operations and boost profitability. Artificial intelligence technologies are increasingly being adopted in the industrial sector to take advantage of new technologies, streamline operations, and make companies more flexible for future changes.

What is Artificial Intelligence (AI)? 

Artificial Intelligence is a field of computer science that deals with the computer simulation of intelligent behavior. In other words, it is a computer system that is capable of carrying out operations that ordinarily need human intellect, such as speech recognition, visual perception, decision-making, and language translation. Through the development and use of algorithms integrated into a dynamic computing environment, artificial intelligence (AI) provides the foundation for imitating human intelligence processes. Simply said, AI aims to recreate human thought and behavior in machines. 

Computerized methods, data and its management, and sophisticated AI algorithms (code) are the three crucial elements necessary to accomplish these AI goals. However, more data and processing power are needed the more humanlike the intended result. 

Siri, Alexa, and other smart assistants, self-driving cars, conversational bots, robot advisers, email spam filters, Netflix suggestions, and self-driving automobiles are some examples of practical AI applications. 

How Does Artificial Intelligence Work? 

Artificial intelligence is a technique for training a computer or a robot that is controlled by a computer, or software to think critically and creatively. AI is achieved through examining the cognitive process and researching the patterns of the human brain. 

Simply said, large-scale, intelligent, iterative processing algorithms are combined to create AI systems. AI tools and software can learn from patterns and characteristics in the evaluated data owing to this combination. An artificial intelligence system checks and evaluates its performance after each cycle of data processing, using the outcomes to gain more knowledge. 

Basic AI Types:

The basic AI types are listed below:

Purely Reactive: These machines, which specialize in a single line of work, have no memory or data to work with. For instance, when playing chess, the computer watches the movements and chooses the move that would give it the best chance of winning.

Limited Memory: These devices gather past information and keep adding it to their memory. Although their memory is limited, they have enough experience or memory to make wise judgments. For instance, using the geographic information that has been acquired, this system can recommend a restaurant.

Theory of Mind: This sort of AI is able to communicate socially and comprehend ideas and emotions.
Self-Aware: Future versions of these new technologies will be self-aware machines. They will be mindful, sentient, and intelligent.

The use of AI can result in decreased human error, 24 x 7 availability as computer system never sleeps, fast processing, and quick solution to complicated situations.

How Will AI Evolve the Industries? 

Artificial intelligence (AI), one of the most important technical developments in recent years, is expected to change how industries operate on a large scale. Although AI is still a relatively new technology, billions of dollars are invested each year in its research and development, which is accelerating its application in various industries. Investment in AI is expected to surpass $500 billion by 2024. With constantly emerging uses and opportunities, AI’s influence on the industry is expected to be nothing short of revolutionary. 

Figure 1:Industrial Automation Future Prediction

Large robotic arms doing repetitive duties on an industrial assembly line are probably what spring to mind when thinking of industrial automation. However, improvements in connectivity technology (such as 5G, Wi-Fi6, etc.), processing power (onboard computing), and AI/machine learning techniques are pushing the boundaries of autonomous robotic capabilities. Robots are getting more agile and maneuverable, which enables them to interact with people more organically. Over the following several years, we’ll witness an increase in the number of autonomous robotic operations, even if traditional form factors (such as static, propelled, or tracked path robots) will continue to predominate in the near future. 

The roadmap to fully automated industries is shown in Figure 1. At stage D, the system must analyze enormous volumes of data due to the high data rates and numerous sensor nodes. This is where the AI will play its role. Due to their innate capacity to enable computers to learn from data, AI (machine-learning) algorithms may uniquely fill this gap. By using data-driven forecasts rather than only responding to direct programming, the abstraction layer enables forecasting. As a result, processing can become more intricate and long-term, which has implications for predictive maintenance using part and device life cycles. 

So, we can say, Artificial intelligence’s impact on the industry is what led to industrial automation. AI reimagines how people and machines live, communicate, and collaborate as it develops new methods for work automation, resulting in a more robust digital economy. Future manufacturers who understand how to use AI applications to empower their employees will produce the most valuable products. Additionally, AI applications in manufacturing facilities allow companies to completely restructure their business interactions. 

AI offers the following benefits in industries. 

1. Data Protection

AI is frequently used by businesses, governments, and the general public to secure sensitive information. Traditional Information technology (IT) techniques may serve as the foundation for maintaining the security of sensitive information, but they must be updated when new risks arise. However, the system gets updated automatically by AI when it detects threats, breaches, and other security concerns, promptly resolving them and protecting the data’s confidentiality.  

2. Automation of Processes:

AI algorithms learn and develop, enabling more effective work, better deep learning network backend operations, and better code. AI process automation is bringing a major revolution to the medical industry. For instance, the technology may shorten the time needed to examine a bacterial sample and recommend an appropriate treatment. With the help of this program, the doctor is better equipped to handle more complex tasks including patient education and clinical evaluation. AI can potentially eliminate some aspects of humanity, such as medical mistakes. According to estimates, medical errors claim the lives of 200,000 people annually and cost the global economy almost $2 billion. 

A growing number of stakeholders are also using AI to automate decision-making and portions of supply chains to improve the efficiency of their financial and administrative tasks in the industries. 


Even while AI uses a lot of computing power, the efficiency gains may often more than offset the cost of energy use. “AI can assist us in producing more out of less. With optimization, waste may be reduced. Growth is possible without increasing consumption.  

We can train an optimization model in 20 minutes and reduce an organization’s annual energy use by tens of millions of euros. The benefits can be enormous. That has already occurred. 


AI can assist plant managers in determining the optimal equipment for each work at each time. These are complex problems that require computer analysis to solve. In factories and warehouses, batch movement between stations may be made more efficient by letting AI/ machine learning systems determine the machine that should carry the batch. 

Simplifying Complex Issues: 

In the end, AI enables plant managers to effectively resolve challenging issues. In AI, emergent behavior is produced. That implies that we can solve more challenging issues. Because of the level of sophistication, we can perceive larger issues and manipulate them. 

Forecasting the Business Value of AI:

In the upcoming years, it is anticipated that the worldwide artificial intelligence (AI) software industry will expand quickly. Applications including machine learning, robotic process automation, and natural language processing are all part of the larger AI business.

By 2025, it is expected that artificial intelligence (AI) technologies will have increased the global GDP by approximately $90 billion.
Between 2019 and 2023, the market for artificial intelligence will increase by $41.95 billion. Due to the consistent acceleration of year-over-year growth, the market’s growth pace will pick up throughout the projection period.

From 2019 to 2023, the AI market share in manufacturing is anticipated to increase at a CAGR of 39.7%, reaching $53.231 billion.

AI Applications in Industries: 

Among the most widespread AI uses in many fields are: 

  • Many industrial applications simply call for repeated operations to be carried out with little variation and motion flexibility. The completion of predetermined portions of a job by people and robots may often be done through human-robot collaboration. The utilization of completely automated factories that can be monitored from a distance is another aspect of AI’s future. 
  • Both of these factories stand to gain significantly from optimization using AI and ML. Machine-vision programs can carry out visual inspections using a pixel-based or feature-based technique, depending on whether faults like scratches, surface roughness, and bubbles are revealed by manipulating pixels, or whether pass/fail assessment is carried out using generic characteristics. In either scenario, an appropriate machine-learning technique can be used to train the classifier to reliably categorize each piece’s flaws in addition to fault detection. 
  • Industrial equipment has always been maintained according to set timetables and procedures. An AI-based system eliminates the need for a timetable for plant maintenance, repairs, or replacements. As a result, there is no longer any chance of a plant being down and no longer any need for further periodic inspection fees. This can be used, for example, in oil and gas refineries to automatically inspect equipment on a regular basis or to inspect industrial machinery on a regular basis, such as huge motors. Through these analytics, the robot equipment itself may be evaluated and maintained. 
  • In order to identify abnormalities in your operation and equipment so that they may be addressed before they cause a potential failure, predictive maintenance is a strategy that uses data analysis tools and procedures. As the expense of machine downtime rises, AI makes the greatest use of data to ensure maximum operational effectiveness. Thus, manufacturers can handle massive volumes of sensor data as quickly as possible, due to AI and ML in production. As a result, manufacturers have a rare chance to enhance current maintenance procedures and even include predictive maintenance to guarantee continuous operations. 


In order to execute a wide range of crucial tasks for industrial applications, AI integrates data science, machine learning, and domain knowledge. These tasks may include everything from monitoring crucial infrastructure to guaranteeing that machinery keeps running as efficiently as possible. Industrial firms must identify which operations may most benefit from AI and concentrate on a narrow range of possible operational economies, quality improvements, and safety advancements if they are to fully realize the benefits of AI. Additionally, industries must recognize and address any obstacles, such as poor data quality and inaccessible data, that stand in the way of an AI adoption. Additionally, it’s critical that workers are informed about the benefits and drawbacks of AI so they can learn to trust and use the technology based on its merits.

This entry was posted on July 13th, 2022 and is filed under Automation, Technology, Uncategorized. Both comments and pings are currently closed.

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