Edge AI can provide industry-leading performance and limitless scalability, enabling businesses to use their data efficiently. That’s why many global companies are already investing in and reaping the benefits of Edge AI.
Edge AI can significantly benefit numerous industries, from driving autonomous vehicles to improving production monitoring of assembly lines.
On top of this, the recent 5G roll-out in many countries offers Edge AI an extra boost as more industrial Edge AI applications continue to emerge.
Overall, implementing Edge AI is a wise decision for many businesses.
Edge technologies are booming
A substantial interest in Edge technologies has blossomed upon the emergence of the latest use cases, particularly following the debut of 5G.
According to the Linux Foundation’s 2021 State of the Edge report, it’s foreseen that by 2028, the global market capitalization of Edge computing infrastructure will be worth well over 800 billion US dollars.
Since Edge computing can economize on bandwidth and quicken response times, it’s beyond doubt that retail, marketing, and healthcare companies will broaden their scope to include Edge computing in their plans by 2028.
Gartner believes that, by 2025, 75% of enterprise-created data will be processed outside the cloud.
In addition to focusing on Edge computing, enterprises are also making massive investments in artificial intelligence (AI). According to last year’s survey done by McKinsey, 56% of the respondents claim that they have put AI into action within a minimum of 1 business operation.
A good number of companies are moving forward with these individual tech investments as an important step in their digital transformation. However, cloud companies and organizations that have a more progressive mindset see possibilities offered by combining Edge computing and AI (Edge AI).
What is Edge AI for enterprises?
Firstly, what makes Edge Computing so innovative is how it takes machine learning and AI to the network’s edge, placing powerful processing capabilities geographically closer to users.
Now, consider AI, which primarily performs calculations based on complicated machine learning algorithms.
Combine these two concepts, and voila! You have Edge AI.
This tremendous new combo provides many benefits to enterprises, including superior data security, effective operations control, and speedy processing. Since Edge AI runs at the Edge, its operating costs are lower, and apps can perform better than ever.
On top of this, Edge AI paves the way for increasingly advanced IoT devices, machine learning at the Edge, and the autonomous application of deep learning models, without any need for cloud services.
Edge AI is set to transform enterprises
Any productive Edge AI model can process hefty AI workloads at the edge of a network because they run on advanced infrastructure made for Edge computing.
Edge AI results in cutting-edge performance, which gives businesses the best bet when dealing with their data. That’s why countless global enterprises are now harnessing the powers offered by Edge AI.
Additionally, now that 5G technology has been made available in most nations, Edge AI is amplified as more industrial applications keep showing up.
Given that Insight calculates an estimated 5.7% ROI, or return on investment, from industrial Edge AI deployments spanning the next 3 years, investing in Edge AI is playing it smart in business.
How do businesses benefit from Edge AI?
Edge AI comes with many advantages for businesses. Some of the most compelling revolve around creating a superb customer experience and offering a significant upgrade in efficiency. The other major benefits include the following:
1. An upgrade in privacy
Since data processing is performed in a local area using Edge AI, data is less likely to be misappropriated or mishandled as it doesn’t require transmission over long distances.
With Edge AI, companies can rest easy knowing that their and customers’ data is safe and secure.
2. The ability to process data in real-time
With Edge AI, powerful AI algorithms can be placed directly onboard devices out in the field.
Autonomous vehicles are a prime example of this. For instance, Edge AI running onboard an autonomous vehicle can thwart accidents in real-time by processing data in mere milliseconds.
3. Low consumption of power
A good number of Edge applications are deployed in far-off areas. Edge technologies must maintain a proper balance between power and performance.
Most Edge computing devices are cleverly equipped with efficiency and power-saving features that help enterprises in the long run.
Businesses also save money on energy expenses because a constant connection to the cloud isn’t needed when data is processed locally.
4. Low bandwidth usage
It can get costly for businesses to perform AI computations in the cloud, but with Edge AI, the cloud serves only as a storage unit for any previously processed data.
With Edge AI, businesses save a great deal of money on internet bandwidth since less data is transmitted through the internet.
5. Responsiveness is enhanced considerably
Compared to cloud computing, Edge AI is far more responsive because of its capacity to process data locally. Cloud computing must gather data and ship it to the cloud for processing purposes. After that, there can be a delay before that data makes a U-turn to return to its point of origin.
Edge computing can get data processing done in milliseconds.
As a result, decisions are made faster, and action can be taken quickly.
Additionally, devices that need immediate feedback, like some industrial robots and autonomous vehicles, can’t operate without the speed of Edge AI, making it indispensable in these cases.
Transformative Edge AI trends businesses need to be aware of
Edge AI is becoming more popular in enterprise environments, generating trends while piggybacking off others.
Here are the most noteworthy current and upcoming Edge AI trends that are having a transformative impact on different industries.
IoT-generated data is at an all-time high
As sensors and IoT devices create enormous amounts of data, gathering data generated by these devices has become difficult. Take the most recent Airbus A350 aircraft, which has about 50,000 sensors that accumulate 2.5 terabytes of data daily!
If the data collected possesses no metadata, which usually helps to explain it, the data itself cannot provide enough information to form actionable insights. In this case, simply collecting data isn’t enough.
It’s only through Edge AI that IoT data can be fully utilized, as Edge AI is equipped to figure out what’s essential and what’s not.
In some cases, Edge AI saves only the most important data in a cloud data center. This, in turn, paves the way for automating more operational decisions.
Consumer satisfaction is paramount
GPU processors, sensors, cameras, and the like are becoming more and more affordable. Keeping that in mind, customized Edge AI solutions are also becoming more accessible for the average joe.
Now, customers not only expect but demand a smooth and continuous service experience. Edge computing guarantees this since there are no delays in transferring data.
5G boosts Edge AI
5G networks are gradually being set up in densely populated regions, at least for now.
5G is especially pressing in these regions because they typically employ self-driving vehicles, require mission-critical applications, and are beginning to engage in real-time VR.
Edge computing hits the sub-millisecond requirements of 5G apps while lowering energy consumption by about 40%.
Edge computing also enhances how well 5G networks can perform to sustain and deploy various real-time AI apps.
Thanks to 5G networks, more extensive and quicker data streams can be collected efficiently. And, excitingly, Edge AI tech goes up in value the moment these data streams are put to use as it allows data to be analyzed as close as possible to devices connected to the 5G network.
Incredible Edge AI use cases for enterprises
According to a 2021 Rackspace survey, 34% of respondents claimed that low data quality was the main reason for AI R&D failure.
Similarly, a recent Alation report noted that 87% of employees cited data quality issues as the cause of not being able to execute machine learning and AI in their organizations successfully.
Let’s explore some enterprise-specific Edge AI use cases to see how Edge AI addresses data quality issues, among other problems.
Edge AI is essential to autonomous vehicle use cases
Edge AI makes transportation safer and more secure.
For example, Edge AI-enabled autonomous vehicles can efficiently navigate travel routes and identify traffic conditions. Thanks to Edge AI, these vehicles can do this because of their speedy and exact ability to make accurate decisions.
Edge AI technology helps with virtual assistants
Two excellent examples of virtual assistants that have reaped the benefits of recent Edge AI developments are Siri and Alexa.
Edge AI provides the means for their machine learning algorithms to accomplish deep learning at an incredible speed. Moreover, Siri and Alexa don’t need to rely on data stored in the cloud but can manage their operations based on data stored on the device itself.
Edge AI brings benefits to automated product inspection processes
Automated product inspections can detect defective parts of assembled units in a production line. This is achieved through Edge AI visual analysis and is vital as quality control is a central concern in manufacturing lines.
With Edge AI, the automated quality inspection doesn’t depend on a sizeable cloud-based data transmission, which makes super fast, precise data analysis a reality.
Edge AI is making its way into many areas of business
Edge AI use cases continue to grow daily, and it won’t be long before it becomes an integral part of many enterprise functions and daily work life.
As this article has demonstrated, Edge AI is transforming enterprise environments in several significant ways. Soon, it will become something that many businesses can’t live without.