Artificial Intelligence has been the center of conversation in the tech world for the past couple of years, with noticeable practical applications emerging from AI-driven tools like ChatGPT, Midjourney, and others.

Nevertheless, the focus has largely remained on generative AI and large language models (LLMs), which do not cater well to the needs of sectors that depend on immediate and localized decision-making, such as manufacturing.

Edge AI aims to overcome this limitation by analyzing extensive datasets on compact computing devices, deploying smaller and more efficient machine learning models to provide timely AI insights.

As experts suggest, the processes of AI computation should occur where they yield the greatest benefit for the business. For numerous industrial companies, this means operating close to the source of data generation – directly at the edge.

There is an unprecedented push for innovation at the edge, highlighted in NTT Data’s 2023 Edge Advantage Report, which conducted a survey of 600 enterprises across various industries. The findings revealed that approximately 70% of enterprises are presently utilizing edge solutions to tackle their business challenges.

So, what exactly is edge AI, and how will it shape the future of AI computing?

Edge AI refers to the processing of data at its origin, or at the network’s edge, instead of depending solely on distant cloud servers. Advocates of this method assert that by relocating computing capabilities to the edge, organizations can gain valuable insights that enhance their operational efficiency.

Kaihan Krippendorff, the founder of Outthinker Networks and a co-author of Proximity, expresses the viewpoint that “AI compute isn’t just the future; it’s already present.” He notes that “manufacturing enterprises, in particular, are transitioning AI systems from the cloud to the edge, which allows for quicker data processing while lowering latency and costs associated with cloud servers.”

“Shifting AI systems from the cloud to edge environments, especially in manufacturing, leads to quicker data processing from Internet of Things (IoT) devices and sensors connected through local networks in factories. This transition minimizes latency, lowers cloud-server expenses, and allows for innovative ideas to flourish locally.”

But it’s not solely Krippendorff who perceives the arrival of edge AI. Other industry professionals, including Paul Bloudoff, senior director of edge services at NTT, echo this view.

“We are witnessing many enterprises advocating for these solutions,” Bloudoff remarks. “The reason lies in Edge AI’s capability to provide actionable insights and facilitate real-time decision-making within operational technology settings, like the factory floor.”

Currently, the edge AI market is experiencing rapid growth, with significant contributors such as NTT Data, Siemens, IBM, and Microsoft, each bringing a distinct strategy to the landscape.

NTT has recently introduced an ultralight edge AI platform, which serves as a fully managed edge AI solution designed to eliminate IT-OT barriers. This advancement paves the way for sophisticated AI applications tailored for industrial and manufacturing settings.

Meanwhile, Siemens has created an industrial edge platform that empowers manufacturers to implement AI applications right on the factory floor. In a similar vein, IBM’s edge application manager aims to provide AI solutions that cater to edge devices across various sectors, including healthcare, telecommunications, and automotive.

Edge AI is currently being utilized in a wide range of industries, with early adopters reporting notable enhancements in operational efficiency, reduced costs, and boosted innovation capabilities. One of the standout areas benefiting from edge AI is manufacturing.

By leveraging edge AI, manufacturers can process data locally, facilitating real-time monitoring, predictive maintenance, and smart decision-making right on the factory floor.

For example, IoT sensors installed on production machines produce continuous data streams that are promptly analyzed by edge AI systems to foresee potential equipment malfunctions. This proactive approach enables manufacturers to conduct maintenance ahead of time, preventing breakdowns and minimizing downtime, ultimately boosting overall productivity.

A notable example of successful edge AI implementation in manufacturing is Haier, a worldwide leader in home appliances and electronics. By utilizing edge AI in its local factories, Haier has reportedly streamlined its production processes and enhanced its capacity to tailor products to meet local market demands more effectively.

This effective integration of cloud computing with edge-based applications has led Haier to provide its AI systems to other manufacturers through COSMOPlat, a company established by Haier in 2017.

However, the influence of edge AI extends beyond manufacturing. Smart cities are also adopting edge AI technologies to improve urban infrastructure and services, optimize traffic management, enhance public safety monitoring, and track environmental conditions such as air quality and weather in real-time.

Despite the increasing interest in edge AI, its adoption has faced several hurdles. The 2023 Edge Advantage Report reveals that nearly 40% of those intending to implement edge solutions have concerns regarding their current infrastructure’s ability to support this technology.

Bloudoff recognizes these issues, indicating that many businesses encounter substantial difficulties when trying to integrate edge solutions with their existing IT and operational technology infrastructure.

A significant obstacle lies in the necessity to ensure that data from IoT devices, sensors, and machinery can be effortlessly collected, processed, and analyzed at the edge. This demands powerful hardware and software solutions that can manage the volume and complexity of data produced in real-time operational settings.

Solutions like NTT’s ultralight edge AI platform have been developed to simplify the deployment and management of AI applications at the edge. The platform features an auto-discovery capability that scans the entire IT and OT environment to catalog assets, pinpoint vulnerabilities, and enhance data collection.

“By automating the discovery and collection of various IoT and OT devices into a unified data plane for real-time decision-making, we are eliminating barriers to edge adoption,” states Bloudoff.

In addition to boosting operational efficiency and promoting innovation, edge AI provides a distinctive opportunity for companies to achieve their sustainability objectives. Since edge AI processes data locally, it diminishes the reliance on energy-heavy cloud computing, thereby reducing the carbon footprint linked to long-distance data transfer and cloud storage.

Bloudoff emphasizes that businesses implementing AI applications at the edge are not only improving productivity but also making strides towards more sustainable operations. “By decreasing energy use and network congestion, Edge AI is enabling organizations to lessen their environmental impact while facilitating digital transformation,” he concludes.

As sustainability becomes an increasingly important consideration for businesses worldwide, edge AI’s ability to deliver both operational and environmental benefits will likely be a driving force behind its continued adoption.

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