What is edge AI?

In essence, edge AI (or “AI on the edge”) combines edge computing and artificial intelligence (AI) to perform machine learning (ML) tasks directly on interconnected edge devices. 

Edge computing allows data to be stored near the device, and AI-powered algorithms enable processing at the network edge, with or without an internet connection. This capability facilitates data processing within milliseconds, providing immediate feedback.

Self-driving cars, wearable devices, security cameras, smart home appliances and advanced robotics are among the technologies that use edge AI capabilities to deliver users with real-time information. Agentic AI systems also depend on edge AI to act and respond instantly, without having to send data to the cloud for analysis.

The growing demand for instant data processing, combined with advances in AI and ML algorithms, is driving the adoption of edge AI across enterprise settings. In 2025, Grand View Research valued the global edge AI market at USD 24.91 billion. The firm expects it to reach USD 118.69 billion by 2033, growing at a compound annual growth rate (CAGR) of 21.7% from 2026–2033.¹

Organizations are implementing edge AI to optimize workflows, automate business processes and foster innovation. Simultaneously, edge AI helps deliver low-latency, heightened security and cost reduction.

Edge AI uses neural networks and deep learning frameworks to train models to accurately recognize, classify and describe objects. This training process usually takes place in a centralized data center or the cloud to process the large volume of data needed for model training.

After deployment, edge AI models improve over time. For instance, when the AI encounters an issue, data is transferred to the cloud for further training of the initial AI model, which ultimately replaces the AI inference engine at the edge. 

Advances in small language models (SLMs)—which are more compact and efficient than large language models (LLMs)—and the increasing use of generative AI (gen AI) are expanding what edge devices can do locally. They enable more processing to happen on‑device without depending on the cloud.

The critical edge AI components are:

• Edge devices/nodes: Industrial IoT machinery, IoT sensors and smart cameras that capture data.

• Edge gateway: A router, server or other networking device that sits between edge devices and the cloud or centralized data center.

• Edge servers: Specialized computers or clusters of computers located at the edge that handle processing, storage, networking, security and other computing resources.

• Edge processors and AI accelerators: Specialized hardware includes neural processing units (NPUs) and graphics processing units (GPUs). These key AI infrastructure components optimize AI inferencing at the edge, providing high computation with low power consumption. AI accelerators use parallel-processing capabilities that allow them to perform billions of calculations at once. NVIDIA, IBM and other major technology companies offer edge AI solutions designed for local inferencing.

• Machine learning models: Models that enable real-time decision-making at the edge, acting on data at its source. As a real-world example, AI algorithms (often pretrained in the cloud) can help detect a malfunction on a factory floor so repairs can be carried out right away.

Edge AI doesn’t operate in isolation; it works in concert with distributed AI and cloud computing. Knowing how the three relate is key to understanding deployment decisions.

Edge AI enables onsite decision-making, eliminating the need to constantly transmit data to a central location and wait for processing, which streamlines the automation of business operations.

Scaling AI across numerous locations and diverse applications brings challenges, such as data gravity, heterogeneity, scale and resource constraints. This approach is where distributed artificial intelligence (DAI)—an approach to large-scale AI tasks where workloads are spread across multiple devices or processors—comes in.

DAI helps overcome edge scaling obstacles by integrating intelligent data collection, automating the data and AI lifecycles, adapting and monitoring spokes and optimizing data and AI pipelines.

In practice, edge AI and distributed AI work together, with edge AI handling on-the-spot processing on local devices while DAI coordinates and scales AI workloads across many locations.

Cloud computing and application programming interfaces (APIs) are commonly used to train and deploy machine learning models. With edge AI, machine learning tasks (for example, predictive analytics, speech recognition, anomaly detection) take place closer to the user. They are processed on IoT devices rather than in a data center or cloud.

Edge AI is a better option whenever real-time prediction and data processing are required, such as in self-driving vehicle technology. To secure navigation and avoid potential dangers, these vehicles must be able to rapidly detect and respond to factors like traffic signals, erratic drivers and lane changes. In addition, they must account for pedestrians, curbs and numerous other variables.

By carrying out local processing within the vehicle, edge AI reduces the risk of connectivity problems that might arise from sending data to a remote server.

Cloud AI, by contrast, refers to the deployment of AI algorithms and models on cloud servers. This method offers increased data storage and processing power capabilities, facilitating the training and deployment of more advanced AI models.

When combined, cloud AI and edge AI complement each other. As an example, data related to customer preferences can be sent to the cloud for analysis, while immediate customer queries are handled at the data source on the edge.

To learn more about how cloud AI and edge AI compare, check out “Edge versus Cloud AI: What’s the difference?”

With AI becoming more critical to enterprise business, edge AI is evolving as an integral part of how organizations build and scale end-to-end AI infrastructure. A 2026 IBM Institute for Business Value study found that 79% of executives expect AI to significantly drive revenue by 2030.

The primary benefits of edge AI include the following benefits:

• Diminished latency: Through complete on-device processing, users get faster responses—no round-trips to a distant server required.

• Decreased bandwidth: Edge AI processes data locally, reducing the amount of data transmitted over the internet and freeing up bandwidth. This reduction allows the network to handle more data traffic at once.

• Real-time analytics: Users can perform real-time data processing on devices without the need for system connectivity and integration. This capability enables them to make faster decisions by analyzing data where it’s generated.

• Data privacy and security: Edge AI increases privacy because data is not transferred over to another network, where it becomes vulnerable to cyberattacks. Through processing information locally on the device, edge AI reduces the risk of mishandling sensitive data. Moreover, for industries subject to data sovereignty regulations, edge AI systems help maintain compliance by locally processing and storing sensitive information within designated jurisdictions.

• Enhanced automation: Edge AI automates onsite data analytics, eliminating the need for continuous human oversight. This feature is a key in applications like industrial automation and remote monitoring, which support modern manufacturing and robotics.

• Operational resilience: In edge AI settings, devices process data locally, so operations continue even when the network goes down or becomes unstable.

• Scalability: Edge AI helps organizations scale AI workloads by combining cloud-based platforms with AI-integrated hardware. This approach makes it easier to add devices and expand operations without disrupting the network. Even when parts of the network go down, local edge devices can keep running independently.

• Reduced costs: Cloud-based AI services can be costly, particularly for workloads that require continuous high computing power. Edge AI offers the option of using cloud resources as a repository for storing and processing data that doesn’t require immediate action. This reduction eases the workloads of cloud computers and networks.

• Lower energy consumption: By filtering and processing data locally on devices like sensors and cameras, edge AI also provides an energy-efficient environment compared to transferring all data to the cloud.

Everyday examples of edge AI include smartphones, real-time traffic updates on autonomous vehicles, connected devices and smart appliances. Various industries rely on edge AI applications and edge AI deployments to cut down costs, support IT automation, make fast decisions and optimize operations.

These examples highlight several industry‑specific use cases.

Healthcare providers use edge AI and state-of-the-art devices to create smarter healthcare systems while safeguarding patient privacy and lowering response times.

Using AI models embedded locally, wearable health monitors evaluate metrics like heart rate, blood pressure, glucose levels and respiration. These wearable edge AI devices can also detect when a patient falls suddenly and alert caretakers, a feature already included in common smartwatches on the market.

Integrating edge AI also helps facilitate the immediate exchange of critical health information. Through equipping emergency health vehicles with quick data processing capabilities, paramedics can extract insights from health monitoring devices and consult with physicians to determine effective patient stabilization strategies. Simultaneously, emergency room staff can prepare to address patients’ specific care requirements.

Manufacturers use edge AI technology to optimize manufacturing operations, increase efficiency and improve productivity. Sensor data can identify anomalies and forecast machine failures, which is known as predictive maintenance, alerting management to crucial repairs before operational downtime occurs. This process speeds resolution and reduces operational downtime.

Edge AI applies to other areas of manufacturing, such as quality control, worker safety, yield optimization, supply chain analytics and floor optimization.

In both brick-and-mortar retail and e-commerce, technologies like sensor-equipped smart carts and automated checkout systems process transactions and recognize items instantly. These solutions all use edge AI technology to improve the overall customer experience.

The home marketplace has experienced a proliferation in intelligent devices, such as doorbells, thermostats, refrigerators, entertainment systems and controlled light bulbs. These smart homes contain device ecosystems that use edge AI to enhance the quality of residents’ lives.

Whether a resident needs to identify someone at their door or control their house temperature through their device, edge AI technology can rapidly process data onsite. This strategy eliminates the need to transmit information to a centralized remote server, helps maintain resident privacy and reduces the risk of unauthorized access to personal data.

Speed is crucial for security video analytics in home, business and smart city settings. Many computer vision systems transmit captured images and videos to a cloud-based machine rather than processing them locally, which creates latency issues that slow response times.

Edge AI’s computer vision and object detection capabilities on smart security devices can identify suspicious activity and immediately notify users and trigger alarms, helping keep homes, businesses and public spaces safer.