As we approach 2025, edge artificial intelligence (edge AI) is set to revolutionize various industries by bringing advanced computational capabilities closer to data sources. This shift will enable real-time data processing and decision-making, reducing latency and enhancing efficiency, as part of the Fourth Industrial Revolution across multiple sectors. From this article, you will learn how edge AI is driving Industry 4.0 in 2025 by enabling real-time data processing, reducing latency, and increasing efficiency.
Industry 4.0
Depending on who you ask, we are either entering the Fourth Industrial Revolution or already living in it. Which one it is really depends, as with all cutting-edge concepts, on the assumed definitions. The First Industrial Revolution was brought about by steam power; the second by electricity; and the third by computing, robotics, and automation. Some say that we entered the fourth with the advent of the personal computer, but most definitions emphasize networking as the key component of Industry 4.0. However, others might say that it is only with the full integration of automation, networking, AI models, deep learning algorithms, and other cutting-edge technology that we will fully enter into the era of Industry 4.0.
Manufacturing is naturally a major, albeit not the sole, component of Industry 4.0, which imagines networked edge devices—robots and sensors—all sharing information and adapting to changing conditions in real time to provide faster, cheaper, and more agile production processes. Industry 4.0 also involves adaptive supply chains, machine-to-machine communication on a large scale, and concepts like the Internet of Things (IoT)—or, as it may be, the Industrial Internet of Things (IIoT). The IoT concept refers to devices equipped with sensors, processing capabilities, software, and other technologies that enable them to connect and exchange data with other devices and systems over the Internet or other communication networks. In fact, projections indicate that there will be over 75 billion such connected devices by 2025.
On the far end of its definitional scope, Industrial Revolution 4.0 demands sophisticated data processing that transcends traditional computational architectures. As manufacturing and industrial systems increasingly rely on complex data-driven decision-making and big data analytics, the limitations of centralized servers and cloud computing are becoming increasingly apparent, at least in some, especially time-sensitive, operational environments. This is where Industry 4.0’s core principle—the comprehensive digital transformation of industrial processes through interconnected systems and intelligent technologies—finds a critical enabler in edge computing and its extension, edge AI.
Edge computing: bringing computational capacity to the network edge
Edge computing represents a change in how data is processed and analyzed across networks, wherein local devices that collect data, like sensors, cameras, and IoT endpoints, also process and analyze data locally, at least to some extent, rather than sending streams of raw data to centralized cloud servers or data processing centers. By strategically moving computational capabilities closer to where data is generated, this technology allows devices to make rapid independent decisions without constantly relying on centralized computational resources. In this way, it reduces latency, improves response times, and reduces the risk of losing important or sensitive data.
In industrial manufacturing, edge computing enables instant quality control through computer vision systems that can detect product defects or other abnormalities in milliseconds. Smart cities can leverage this technology to mine sensor data to create more responsive urban infrastructure, with real-time traffic management and environmental monitoring. Healthcare technology could benefit from wearable sensors that can instantly analyze patient data, potentially enabling faster medical interventions. An insulin pump combined with a glucose monitor is an example of this approach (although it is not so much an alternative to centralized computing but to a patient monitoring their blood sugar and delivering medicine). Perhaps most notably, autonomous transportation relies on edge computing to process complex sensor data, allowing self-driving vehicles to make navigational decisions that could be critical to passenger safety.
Edge AI: intelligent computing at the network edge
Edge AI represents the convergence of artificial intelligence technologies with edge computing, creating a new powerful paradigm for data processing. By embedding compact, optimized machine learning models directly into local devices like the aforementioned industrial sensors, and IoT endpoints, as well as smartphones, smart home devices, and specialized hardware, it is possible to have autonomous decision-making at the point of data generation. Current AI approaches rely on high-capacity centralized computational resources, usually in the form of cloud data centers. In the context of Industry 4.0, this might be seen (according to some definitions) almost as a regression to the paradigms of the Third Industrial Revolution, when large, centralized mainframes were used to process data and control machinery. Edge AI, in contrast, focuses on deploying models specifically designed to operate with limited computational resources that can process data locally and make on-the-spot decisions, effectively balancing high-performance computing capabilities with efficiency.
Edge AI can find application anywhere that edge computing resources are used, providing more sophisticated data processing (locally, without connecting to a cloud AI server or data center) and bringing deep learning capacity closer to the data source. However, it should be noted that deploying edge AI technology may not always be the optimal solution. Even with highly optimized AI models, edge AI devices will need more computing power to process data locally than edge devices running simpler algorithms, which in some cases may be perfectly sufficient. It is important to carefully think through which edge devices will benefit from artificial intelligence.
Benefits of edge AI devices
One of the benefits of edge AI is reduced dependence on cloud connectivity. Processing data directly on local devices using edge AI models reduces the need for constant, high-bandwidth Internet connections. This liberates organizations from the constraints of requiring reliable, high-speed Internet infrastructure, particularly in remote or challenging operational environments. This also leads to lower latency, as executing artificial intelligence computations directly on edge devices cuts out the upload and download steps from response times, potentially reducing them from hundreds to just a few milliseconds (even if the computations themselves might take a bit longer than on more powerful centralized cloud computing servers). This near-instantaneous processing is critical in scenarios demanding split-second decisions, such as autonomous driving, industrial quality control, or emergency response systems.
Not only does sending data from the edge network to centralized servers take time, but it can lead to data loss if network connection is unstable. What is more, if sensitive data is not transmitted through a network to centralized cloud servers, there are fewer points where it could be intercepted or misused. This enhanced data privacy provided by edge AI devices is particularly important when the sensitive data comes from end users, who may not want it to be sent or stored anywhere other than on the edge device itself. This may be the case, for instance, with medical monitoring, smart home devices, or self-driving vehicles.
Edge AI systems also provide enhanced technological resilience by maintaining autonomous functionality during network interruptions, ensuring continuous operation in challenging environments like remote industrial sites or moving platforms (i.e., vehicles). In addition, edge devices are by nature more energy efficient than the infamously energy-hungry cloud AI systems. By processing and filtering data locally, edge AI also dramatically reduces bandwidth costs by transmitting only critical, condensed information to central systems. Thus, edge AI creates a more sustainable, efficient, and robust computational ecosystem.
Edge AI challenges
Edge AI represents a paradigm shift in computational intelligence but, like any emerging technology, it comes with significant implementation challenges. Nowadays, edge devices, especially IoT endpoints, do not necessarily meet the demands of edge AI. Many of them lack the computational capacity to process data to the extent necessary for AI algorithms, even optimized ones. They are also often woefully insecure. Many IoT devices lack proper cybersecurity measures to begin with, and this is exacerbated by a lack of automatic updates that patch known exploits. While independence from cloud computing is one of the benefits of edge AI devices, a lack of properly secured communication is a known issue with IoT devices, which are often seen as vulnerabilities at the network edge.
While AI models can be optimized for edge devices using a variety of techniques, this is a complex task that requires advanced machine learning expertise. Additionally, deploying and managing distributed edge devices is inherently more cumbersome than using centralized systems, since each device requires individual monitoring, updates, and security, increasing the risk of vulnerabilities and simply requiring more resources and staff-hours. Similarly, scaling up or adapting edge AI infrastructure is limited by the need for physical hardware, on-site configuration, and time, unlike cloud-based systems that allow for rapid virtual provisioning.
Other challenges stem from data and cost considerations. Processing data locally on edge devices can create fragmented, inconsistent datasets that are harder to aggregate and analyze comprehensively. This can limit the breadth and depth of insights generated from data processing and analysis.
Finally, the initial investment in specialized hardware, personnel training, and custom software for edge AI deployments can be substantial. Redesigning computational workflows to accommodate these systems can be a challenging task.
Edge AI use cases
Computer vision and AI inspection: AI-based visual quality inspection
Working for a leading company in China, Advantech has revolutionized power grid inspections using AI and computer vision. By integrating drones, cameras, and autonomous robots, they have significantly enhanced the accuracy and efficiency of defect recognition in electrical equipment. The AI-powered system, featuring Advantech’s AIR-030 modules, processes high-resolution images in real time, achieving an 85% accuracy rate in defect detection. This innovation not only reduces manual inspection requirements but also lowers maintenance costs and improves safety. The robust design and local technical support ensure reliable operation in demanding environments, marking a significant advancement in intelligent inspection solutions.
Edge AI in security and surveillance
Worker safety. For one of their customers, Advantech designed a factory operator safety system using edge AI technology. The development team deployed IP cameras at the entrance and security cameras throughout the factory floor to capture live footage, which is analyzed by an advanced edge AI system. In this way, the factory could streamline group employee check-ins, manage access to specific work zones, and verify that operators of specialized machinery and production lines are properly equipped with the required safety gear while minimizing unnecessary idle time. Furthermore, the system is designed to instantly notify the manager of any irregularities involving personnel during the operation of critical equipment.
Traffic safety. A leading Japanese industrial solution provider improved traffic safety by implementing edge AI technology. They installed smart cameras and AI inference systems at critical intersections to monitor traffic in real time. This system detects traffic violations, manages congestion, and enhances pedestrian safety by providing instant alerts to drivers and traffic authorities. The robust design of the AIR-030 Compact, High-Performance Edge AI Box used in the implementation ensures reliable operation in various weather conditions, contributing to safer and more efficient urban traffic management. This innovative approach has reduced accidents and improved overall traffic flow.
Unmanned Gas Stations. A leading Japanese gas station brand took advantage of the same High-Performance Edge AI Box to overcome challenges caused by a shortage of field service personnel and high maintenance costs. The implemented edge AI system analyzes vehicles and drivers to ensure safety and efficiency; monitors users for potential hazards, such as cigarette smoking or improper fuel nozzle usage; controls fuel dispensers and suspends fuel supply when hazards are detected; and activates an alarm to alert drivers of any detected hazards. This system significantly enhances operational safety and reduces the need for trained personnel. By removing the need to invest time and resources in training new personnel for each station, the solution streamlined operations. With regional management centers supervising multiple stations running AI, the brand has simplified the setup of small, unmanned self-service locations while efficiently advancing its goal of rapidly expanding its network of gas stations.
Edge AI devices in medical applications
Neurosurgery robots. Advantech’s EPC-B5587 embedded PC integrated into surgical robots performs diverse preoperative and intraoperative tasks with precision. The system uses AI to create accurate surgical plans from CT or MRI scans and marks critical structures to avoid during operations. Its powerful computing performance ensures zero-latency data exchange and reliable operation. This technology enhances diagnostic accuracy, reduces operating times, and improves clinical outcomes.
Endoscopy. In another case, edge AI technology is used to enhance endoscopic diagnostics and treatment by integrating advanced computing capabilities directly into endoscopy machines. The MIO-5377R embedded board processes high-resolution images in real time, improving diagnostic precision and reducing human error. It also enables real-time image stitching and enhancement, providing clearer and more detailed visuals for doctors. An expansion module boosts graphics performance, aiding in the visualization of internal structures and enhancing the effectiveness of medical procedures. This integration of edge AI ensures reliable operation, even in confined spaces, and significantly improves the overall efficiency and accuracy of endoscopic examinations.
Edge AI: predictions for 2025
As we move into 2025, these advancements in edge AI can be predicted to continue, allowing for many innovations and optimizations in various industries. The main source of progress can be expected to come from optimized AI models and improved capabilities of edge devices. The ever-growing computational needs of cloud data centers, large language models, generative AI algorithms, and other AI applications has led developers to seek ways to limit the size and energy demands of artificial intelligence. The techniques they develop can also be expected to find their use in edge AI devices, allowing for lightweight yet powerful AI algorithms to be installed in them.
As with all new technologies, the usefulness of edge AI should grow as edge AI applications become more widespread and accepted. One of the benefits of edge AI proliferation should be the development of dedicated edge devices. As edge AI becomes more common, we can expect many current problems to be resolved, especially in specialized AI-enabled edge devices. For instance, issues with low computational power can be solved by building more-powerful edge computing devices, as well as by optimizing less-powerful chips specifically for artificial intelligence applications.
If you are planning your next IIoT project, we are ready to support you. In collaboration with our partner Advantech, we can design and implement your IIoT network from scratch, addressing both hardware and software layers. Reach out to us at sales@fabrity.pl to explore the possibilities.
