The Edge AI Renaissance

by Gina Narcisi, CRN

When Wi-Fi wasn't cutting it, MSP Step CG helped Toyota with a private 5G solution to improve operations on the manufacturer's factory floor and power its edge AI applications.

Now there are 20 more industrial and health-care deals with big-name brands in the pipeline in addition to the deal with Toyota for fast-growing Step, according to Ed Walton, CEO of the Covington, Ky.-based company.

"It's really picking up momentum. Anywhere we're deploying private cellular, it is enabling edge applications with AI," Walton said.

Step has built out a robust private networking practice and has been deploying private cellular networks for its enterprise customers with the help of its technology partners Ericsson and Celona. The MSP is integrating that technology with traditional wired and wireless infrastructure from companies including Hewlett Packard Enterprise and Juniper Networks.

Step has seen a massive uptick in private cellular use cases, especially for industrial environments. Private cellular and 5G, combined with edge computing, are the power behind AI at the edge where fast speeds and low latency are required, Walton said.

Worldwide spending on edge computing is expected to reach $228 billion in 2024, an increase of 15 percent over 2023, according to IDC. The research firm has further forecast global spending on edge computing to skyrocket over the next several years to $378 billion in 2028.

Edge computing adoption has largely been niche, particularly seen in industries such as retail and manufacturing for real-time applications. But AI at the edge is evolving, and solution providers like World Wide Technology (WWT) are increasingly seeing demand for emerging use cases that can't tolerate even seconds of latency, including computer vision and safety in manufacturing.

"If I'm a user executing a chatbot prompt, and my interface to that is text in a browser, I don't really care if that takes one second or 10 seconds. I don't really need edge AI. It can go back to wherever—the cloud or the data center—wherever I've got my LLM inferencing," said Neil Anderson, vice president of cloud, infrastructure and AI solutions for St. Louis-based WWT. "But if I'm trying to do something like computer vision where I've got some cameras locally in a store where I'm trying to do facial recognition or customer behavior recognition, that's where you really need something at the edge where you can process that locally. You're not going to want to drag all that video back to a central place to process it. You need a distributed approach."

WWT has helped customers implement safety measures in industrial manufacturing settings.