Unlocking the Power of Edge AI for Business Agility and Innovation

Recent advances in artificial intelligence (AI) have taken the world by storm in ways we haven’t seen since the dawn of the internet. Organizations spanning almost every industry are fast becoming aware of the endless possibilities AI provides for business growth and innovation. Yet, there is one subset of the AI ecosystem that may have even bigger implications for the business landscape: edge AI. In fact, Forbes magazine proclaimed "Edge AI may be the most important development for the future of business."

But what is edge AI, and why is it such a game changer for businesses?

Edge AI is the process of running artificial intelligence (AI) and machine learning (ML) algorithms on devices at the network’s edge (periphery), rather than on large cloud servers. In simple terms, edge AI is the fusion of devices and artificial intelligence.

Cloud computing vs. edge computing

Running AI algorithms on devices enables a wide range of possibilities to solve unique problems. 

How Edge AI is Transforming Business

Edge AI adoption is accelerating across various industries, driven by the need for localized processing, improved data privacy, and enhanced operational efficiency. With its capacity to deliver flexibility, agility, and speed, edge AI is a disruptor unlike any other. 

Moreover, edge AI presents distinct advantages over traditional centralized approaches. Unlike traditional AI architectures, edge AI brings intelligence closer to the source of data generation, typically on edge devices or computing nodes.

Imagine for instance, scenarios involving first responders or industrial workers facing life-threatening situations where they can’t be reached. Under normal circumstances, cloud-based systems can monitor vital signs but if for any reason these workers “go dark” and are inaccessible via the cloud, that’s where real-time decision making powered by edge AI has a huge impact. 

Without the need for connectivity, algorithms running directly on existing hardware can provide instantaneous feedback on the risk of heat exhaustion in our example. SlateSafety, a company that helps leaders identify and prevent heat stress and lone worker injuries before they occur, pondered whether their award-winning BAND V2 could deliver real-time alerts through fully on-device processing. Through a collaboration with Edge Impulse, they honed in on key parameters and created an accurate, compact algorithm for predicting heat exhaustion.

Consider real-time decision making as one of the superpowers of edge AI — a critical capability for applications such as autonomous vehicles, industrial automation, and smart cities where split-second decisions have life and death implications. 


The versatility and adaptability inherent in edge AI make it perfectly suited for a myriad of use cases and environments, allowing organizations to deploy and tailor to meet evolving business needs. By fine-tuning algorithms and models to address specific requirements and objectives, customization empowers businesses to extract maximum benefits from edge AI.

Seamless integration with existing infrastructure

Whether you’re leveraging systems, IoT devices, or cloud platforms, edge AI complements and enhances these technologies without requiring significant modifications, thus minimizing disruptions to operations. This way, you can leverage your existing investments while tapping into the advantages edge AI offers. 

Cost reduction

Edge AI has forever transformed the way data is processed and analyzed, presenting significant advantages in terms of costs and resource optimization. By leveraging computing power at the edge of the network, the expense associated with sending large volumes of data to the cloud for processing is drastically reduced. This results in minimizing bandwidth constraints and latency. An added benefit stems from edge AI’s non-reliance on centralized cloud resources, which can be costly to maintain and scale.

Privacy and security

Whenever the topic of data is broached, concerns regarding privacy and security inevitably follow. Data breaches pose significant risks to individuals and organizations alike, jeopardizing sensitive information, and in the case of companies, undermine customers' trust. Unfortunately, anyone and any business can be vulnerable to a data breach or cyber attack.

Because traditional AI architectures often centralize the processing of data in cloud environments, transmitting potentially sensitive information over networks where it may be susceptible to hackers or others with unauthorized access is inescapable. 

In one of the largest data breaches in U.S. history, JP Morgan Chase disclosed that cyberattacks compromised over 76 million households and 7 million small businesses. Similarly, over 30,000 U.S. companies, (60,000 worldwide) were affected by an attack on the Microsoft Exchange email servers, one of the largest email servers in the world. Edge AI offers a compelling alternative to traditional architectures. By performing AI inference and analytics at the edge, businesses can minimize the exposure of sensitive data to external threats, reducing the likelihood of data breaches.

The decentralized nature of edge AI empowers businesses to gain better control over their data and implement robust security measures tailored to specific use cases. Furthermore, edge AI solutions can capitalize on advanced encryption techniques and anomaly detection algorithms to safeguard data integrity and confidentiality.

“AI has shifted from simply being a cost-saving lever to a revenue driver, with 69% of respondents now using AI/ML to create new revenue streams.”
S&P 2023 Global Trends in AI report

Examples of edge AI systems

Surveillance cameras and smart video systems are increasingly utilizing the power of edge AI for video analytics. Instead of streaming raw video to the cloud, the edge device (the camera itself) runs AI models to detect and recognize people, objects, behavior etc in the video feed.

Smart home devices like cameras, thermostats, and speakers represent another common form of edge AI systems. They use edge AI chips to run audio/video/sensor processing locally. This enhances privacy, and improves responsiveness.

The Role of Edge Computing in Empowering Edge AI

Edge computing — a computer networking strategy where data is processed and stored at the periphery of the network — plays a pivotal role in galvanizing the future of AI at the edge. IDC predicts worldwide spending on edge computing is expected to be $232 billion in 2024, an increase of 15.4% over 2023. Combined enterprise and service provider spending across hardware, software, professional services, and provisioned services for edge solutions will sustain strong growth through 2027 when spending will reach nearly $350 billion.

Anything that runs locally on your computer or smartphone is considered edge computing. That includes word processing, spreadsheets, most programming development environments, and many video games. The following highlights from Accenture’s research report, “Leading with Edge: How to Reinvent with Data and AI,” make the case for why edge computing is crucial to edge AI and the future of business:

Edge AI in Action

Edge AI has emerged as a transformative force and many industries are experiencing its profound impact. In the manufacturing sector, predictive maintenance has quickly become one of the top use cases, where systems powered by edge AI monitor the health of industrial machinery and predict potential failures before they occur. 


The Sony Spresense MCU, CommonSense sensor board, and Edge Impulse technology created a predictive maintenance solution for a print shop facing two distinct challenges: extending the life of an essential, aging copier, and safeguarding air quality for workers. This use case offers a step-by-step guide to implementing predictive maintenance for the copier and monitoring volatile organic compounds (VOCs) in the workplace environment. 

In applications such as industrial inspection, medical imaging, and logistics, anomaly detection is critical. Training visual anomaly detection models involves developing algorithms to identify unusual patterns or anomalies in image data that do not conform to the expected behavior. With edge AI, organizations now have access to visual anomaly detection for production-ready edge AI pipelines.


Edge AI is also making a tangible difference in the healthcare industry where technology is becoming increasingly vital to enhancing patient care and driving better clinical outcomes. AI systems can analyze vast amounts of medical data, including images, genetic information, and patient records, to assist healthcare professionals in diagnosing diseases and predicting outcomes with unprecedented accuracy.

In many instances, healthcare interactions speak to symptoms, not the cause. The founders of Hyfe, a respiratory wellness company that tracks and monitors coughs, needed a solution for streamlining their ML models for full on-device processing. The founders felt that there could be valuable, actionable insights stored in the sound of our coughs, so they set out to find a system that could unlock those learnings to help address illness more directly.


Electric power grids consist of complex interconnections that sustain our modern way of life. Even a minor infrastructure failure can lead to catastrophic consequences, evidenced by events like the California wildfires, which have caused numerous human casualties and extensive property damage. With an increase in energy demand, it's more important than ever to preemptively predict potential outages and failures. With edge AI, you can monitor what’s happening with the power grid in real-time. Discover how edge AI built one of the world’s most advanced grid monitoring systems.

Key Challenges in Implementing Edge AI

Implementing edge AI comes with challenges that organizations must navigate to unlock its full potential. 

Lack of industry standards The absence of standardization, especially in hardware, exacerbates compatibility issues according to Wevolver’s State of Edge AI 2024 report. Varying computing capabilities across different edge devices make it difficult for developers striving to create universally compatible edge AI applications. The report suggests establishing industry-wide hardware standards is essential to facilitate seamless integration and scalability of edge AI solutions across diverse environments and devices. However, there is some good news with certain software standards.

Data management — As edge AI involves managing and processing large amounts of data at edge devices rather than the cloud, this presents challenges around data and storage. Data management is the top technical inhibitor to AI adoption according to the S & P 2023 State of AI report. 

Resource constraints — Edge devices like sensors, cameras and IoT devices are typically designed for compact and mobile or remote deployment, which imposes strict limitations on their processing power, memory, capacity, and energy efficiency. 

Unlike powerful cloud servers, these devices must operate within stringent hardware constraints, often relying on low-power processors, minimal RAM and battery or energy harvesting power sources. Ultimately, striking the right balance between model complexity, accuracy, and resource utilization is critical to pushing the limits of edge devices.

Integration challenges — Organizations looking to adopt edge AI must address legacy systems that may rely on outdated interfaces, monolithic architectures, proprietary tools, and address hardware or software compatibility issues. Bridging the gap demands careful planning, gradual migration strategies, stakeholder buy-in, close collaboration between IT teams, AI experts, and domain-specific professionals.

Data security and privacy concerns

As intelligent systems move closer to the edge, the point of data collection, they handle vast amounts of personal, operational and proprietary information, increasing the risk of exposure. Stringent security measures must be implemented. 

Embedded.com suggests the following steps to mitigate these risks: encryption of data both at rest and in transit, regular security audits, and regular software updates to address vulnerabilities. Additionally, techniques, such as federated learning and differential privacy (a mathematical technique for publicly sharing information about a dataset without including information about individuals in the dataset), can be used to ensure that sensitive data is protected while still enabling the benefits of AI. 

Skills Needed to Create and Build Edge AI Systems

Building edge AI systems require a unique blend of interdisciplinary skills which can make it challenging for some businesses. Proficiency in AI, machine learning, edge computing, and data analytics are critical. However, expertise in embedded systems, hardware and software design, to tackle the stringent constraints of edge devices are also needed. 

Fortunately, there are systems designed to solve well-known edge AI challenges so engineers and developers can save time and focus on their domain expertise.

Best Practices for Implementing Edge AI

Start with clear objectives Begin with a strategic approach by identifying high-value use cases that align with your business objectives and can leverage the strength of edge computing.

Level up data management strategies — Robust data management is essential, beginning with techniques for efficient data collection, processing, and storage at the edge, while maintaining high security and privacy standards.

Prioritize the development of edge architecture — this is needed to account for diverse hardware requirements, connectivity constraints, and scalability for edge deployments. Your architecture should incorporate mechanisms for secure over-the-air updates, device management, and real-time monitoring for seamless operation and maintenance.

Choose the right edge devices


Edge AI is already revolutionizing business, particularly in how companies deliver value to their customers. As we enter an era of unprecedented connectivity, the ability to extract, process, and act upon data generated from billions of devices and machines generating data is paramount to successfully harnessing the power of edge AI. 

The future is here, and we’ll continue to see businesses unlock new revenue streams, drive innovation, get to market faster, and stay competitive in an increasingly data-driven world. Embracing edge AI is no longer an option but a necessity for those seeking to thrive in the era of intelligent, connected systems.



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