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AI is Moving to the Edge Featured

AI is Moving to the Edge person standing on mountain cliff

Edge computing has attracted a lot of interest and new use cases, especially with the rising adoption of 5G. According to the report by the Linux Foundation titled The 2021 State of the Edge report, the global market cap of edge computing infrastructure is expected to be worth more than $800 billion by 2028. Companies are also investing heavily in artificial intelligence, with a McKinsey survey of 2021 showing that 50% of the respondents have already implemented AI in at least one business function. Here are the developments in edge AI and how it will impact modern enterprises.

What is edge AI?

Edge AI is a technology that allows faster computing and insights, enhanced security and efficient control over the continuous operation. It is the deployment of Artificial Intelligence (AI) in devices in the physical world. It is called edge AI because the computation is carried out at the network's edge. This is close to where data is located.

How edge AI will transform enterprises

Efficient adoption of Edge AI models and infrastructure can positively affect businesses. For instance, edge computing can allow the handling of bulk workloads on edge and near the edge, which is one way of improving performance, efficiency and scalability in the use of data. Because of this, many global businesses have already adopted and are reaping big from this technology. With edge AI, companies can efficiently perform tasks like monitoring the production of an assembly line and driving autonomous cars, among other applications in different industries. Furthermore, the recent 5G rollout in most countries is proving to be a timely boost for edge AI. This has increased the applications for technology across industries. Some key benefits of edge computing in collaboration with AI to organizations include efficiency in predictive maintenance and asset management, reduction of field issues, better customer satisfaction and faster inspection span. Implementing edge AI is a good decision for a business, with reports indicating that there is an average 5.7% return on investment from the industrial edge over three years.

Advantages of machine learning on edge

Machine learning (ML) is a subset of AI that simulates the human learning process using data and algorithms. With the aid of edge, ML can help businesses that rely on IoT devices. Some advantages of machine learning on edge computing include:


Information and data have turned into the most valuable asset in the modern age. As such, consumers are cautious of where their data is located. With AI-enabled personalized features in applications, companies can allow users to understand how their data is being collected and kept. This is crucial for brand loyalty.

Reduced latency

The majority of data processes carried out on the network are affected by latency. However, edge AI eliminates the need to send massive data amounts across the network because processing is done at the edge. This minimizes latency and improves user experience significantly.

Minimizes bandwidth

IoT devices within a network process massive amounts of data and transmit it to the cloud. They have to transmit huge amounts of data to the cloud and then analyze and retransmit the analytics to the device. Without adequate bandwidth and enough cloud storage, it would be an impossible task. There is also a possibility of exposing sensitive information during the process. With edge AI, the cloudlet technology, which is a small-scale cloud storage, is enabled at the edge of the network. This enhances mobility while reducing the load of transmission of data. It also brings down the cost of data services while enhancing its flow speed and reliability.

Low cost of digital infrastructure

A report by Amazon indicates that up to 90% of digital infrastructure costs originate from inference which is a crucial process of generating data in ML. However, with edge AI, it is possible to eliminate exorbitant expenses incurred on AI and ML processes that are done on cloud-based data centers. 

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