In most cases, edge implementations are driven by business units with a specific issue that needs to be addressed. IDC has identified more than 400 use cases for edge computing across various industries and domains, with the largest investments in 2023 targeting production asset management, autonomic operations, omnichannel operations, freight monitoring, and augmented customer service agents.

Here are some leading-edge data center use cases in major industries:

Manufacturing: By deploying sensors, plant managers can quickly detect problems in real time (such as "this engine is overheating") and step in to avoid disruptions to manufacturing processes. Edge data centers can analyze data over time to provide recommendations for predictive maintenance. At higher levels, edge data centers enable real-time quality assurance, optimized supply chains and automated processes. Edge data centers are an integral part of an organization's industrial IoT (IIoT) strategy.

Healthcare: Remote patient monitoring via cameras, telemedicine, the deployment of wearable devices that gather medical information, medical imaging systems, and disease detection and prevention analytics are data-intensive use cases that benefit from the deployment of edge data centers in healthcare.

Retail: A retail edge data center might initially be deployed to support video cameras designed to detect theft. The organization might quickly discover that in-store cameras can also collect data about customer behavior. This can lead to better product placement, even new customer experience applications, such as sending an instant discount coupon to customers' phones based on what product they're looking at.

Energy and utilities: Edge data centers let energy and utility companies conduct remote monitoring of equipment to detect problems, avoid outages, and increase system efficiency and reliability.

Transportation: Organizations with fleets of vehicles can deploy edge data centers to provide real-time alerts to drivers and maintenance crews, and to enable predictive maintenance.