“The emergence of AI can help organizations take a large step forward toward hyper-customizing end-user devices for exactly the job they do — from optimizing the installed software to ensuring the right levels of connectivity, access control, and data access privileges are enabled when users unbox their PCs or laptops,” Hochmuth says.
While automated patching, deployment, and alerting have been around for some time, “now we’re seeing vendors lean more heavily into AI,” says Jeremy Roberts, senior director, research and content at Info-Tech Research Group. “I think there is significant overlap with AIOps [AI for IT operations] to facilitate automation across the entire stack, and these tools require input of the sort collected by traditional UEM tools.”
Predictive analytics is the biggest use case for AI in UEM, Roberts says, to address questions such as “when is a device likely to die, [or] what is likely causing a poor experience?” Managers are focused on productivity, “and preemptively remediating issues before they cause productivity problems is definitely an area for UEM to shine,” he says.
