Introduction
Organizations are increasingly using AI to help employees work more efficiently, streamline repetitive tasks, and gain insights more quickly. As AI becomes a bigger part of everyday work, understanding how these workloads interact with device hardware is becoming increasingly important.
This shift is prompting organizations to think more carefully about how and where AI workloads are processed. Some workloads can run locally on a device, while others may rely on cloud computing. The hardware used to process a workload can affect system performance, power consumption, and the overall experience.
Windows Task Manager provides a familiar way to monitor system resources and, on supported devices, AI processing activity. This information can help organizations understand how applications use AI-capable hardware and investigate unexpected resource usage.
In this module, you'll first explore the shift toward local AI processing and the hardware that supports it. You'll then learn what AI-related information Task Manager provides and how to use that information to investigate workloads and understand their impact on the device.
In the next unit, you'll learn about Microsoft's concept of unmetered intelligence and how local AI processing can make better use of hardware already available on employee devices.