Windows AI Workloads Are Here: Is Your RMM Actually Watching Them?

If you haven't noticed, the hardware under your users' desks is changing. Microsoft’s latest push into "Copilot+ PCs" means that Neural Processing Units (NPUs) are becoming standard specs in enterprise endpoints. As The Register recently reported, Microsoft is even updating the venerable Task Manager to include per-process NPU metrics so users—and admins—can see exactly how these AI workloads are hammering the hardware.

That’s great news for a single user staring at a frozen screen. But for an IT manager or an MSP technician responsible for 500, 5,000, or 50,000 endpoints, the "open Task Manager and look" strategy is a non-starter.

When a user calls complaining that their brand-new AI-powered laptop is crawling because a background process is consuming 100% of the NPU, how do you see that? If your monitoring tool says "Device Online" and your RMM requires you to manually remote into the machine to open Task Manager, you are already losing the game. You are stuck in reactive mode, wasting billable hours and SLA minutes on diagnostics that should be automated.

The Problem in Depth: The Visibility Gap in Modern Windows

The introduction of NPU metrics in Windows highlights a massive gap in traditional IT operations: Tool Sprawl and Siloed Data.

Most IT environments today run on a fragmented stack. You might have a monitoring agent pinging the device for uptime, a separate RMM platform for patching and remote control, and a distinct helpdesk for ticketing.

Why this breaks with AI workloads:

1. Legacy Metrics: Traditional RMMs are excellent at checking CPU, RAM, and Disk space. But they are often blind to specific hardware accelerators like NPUs or specific AI background processes (like "Copilot Runtime" or "Windows AI Component") until they trigger a generic "High CPU" alert.
2. The "Tab-Switching" Tax: When an alert fires for "High Resource Usage," a technician using disparate tools has to open the RMM console, find the device, initiate a remote control session (often loading a heavy ActiveX or HTML5 client), wait for the session to connect, and then open Task Manager to see what's happening. By the time the tech realizes it's an NPU-specific issue, 15 minutes have passed.
3. Disconnected Remediation: You find the rogue process. You kill it. But does that action log back to the monitoring ticket? Does the helpdesk system know the issue is resolved? In most legacy stacks, the answer is "no." The technician has to manually update three separate systems.

The real-world impact is a bloated Mean Time to Resolution (MTTR). Your team isn't fixing infrastructure; they are acting as human data pipelines between disconnected tools.

How AlertMonitor Solves This

AlertMonitor eliminates the friction between "seeing" the problem and "fixing" the problem by unifying Infrastructure Monitoring and RMM (Remote Monitoring and Management) into a single glass pane.

Instead of treating the new Windows NPU metrics as just another number to graph, AlertMonitor treats them as actionable intelligence within your RMM workflow.

The AlertMonitor Difference:

• Unified Context: When an alert triggers for high resource consumption on a Windows endpoint, AlertMonitor correlates that with the device's inventory and patch status. You don't just see "High CPU." You see the device, the user, the pending patches, and the active processes in one view.
• No-Tab Switching: You don't leave the console to fix the issue. From the alert timeline, you can immediately pivot to the integrated RMM tools.
• Script-to-Monitor Feedback: You can deploy a PowerShell script to query NPU usage or identify AI processes across all your endpoints instantly. The results of that script don't just sit in a log file; they feed back into the AlertMonitor timeline, creating a historical record of the remediation.

The Workflow in Practice:

• Old Way: Alert fires -> Log into RMM -> Initiate Remote Desktop -> Wait -> Open Task Manager -> Identify process -> Kill process -> Update Helpdesk ticket manually. (Time: ~20 minutes)
• AlertMonitor Way: Alert fires -> Click "Run Script" in AlertMonitor -> Script identifies and terminates the rogue AI process -> Ticket auto-updates with script output. (Time: ~90 seconds)

Practical Steps: Managing Windows AI Workloads with AlertMonitor

You don't need to wait for your legacy RMM vendor to build a custom "NPU Dashboard." You can use AlertMonitor’s native RMM scripting capabilities to gain visibility into these new workloads today.

Step 1: Audit AI-Related Processes

Use the AlertMonitor RMM script console to run a discovery script across your Windows fleet. This identifies processes that might be consuming new resources (NPU/GPU) associated with Windows AI features.

Run this PowerShell script via AlertMonitor to get a snapshot of the top resource consumers on a target endpoint:

# Get top 5 processes by CPU usage, filtering out system idle
$topProcesses = Get-Process | 
    Where-Object { $_.Name -ne 'Idle' } | 
    Sort-Object CPU -Descending | 
    Select-Object -First 5 Name, CPU, WorkingSet, Id

Write-Output "Top Resource Consumers:"
$topProcesses | Format-Table -AutoSize

Step 2: Automated Remediation of Hung AI Services

If a specific Windows AI component (like a background indexing service for Copilot) hangs and causes user lag, you can create a self-healing trigger in AlertMonitor. Create a script that checks for the service status and restarts it if necessary.

# Target a specific Windows AI or NPU-dependent service (Example name)
$serviceName = "AISvc"
$service = Get-Service -Name $serviceName -ErrorAction SilentlyContinue

if ($service -and $service.Status -ne 'Running') {
    Write-Output "Service $serviceName is not running. Attempting to start..."
    Start-Service -Name $serviceName -Force
    Write-Output "Service started successfully."
} elseif ($service) {
    Write-Output "Service $serviceName is running normally."
} else {
    Write-Output "Service $serviceName not found on this endpoint."
}

Step 3: Centralize the Data

Schedule these scripts to run daily in AlertMonitor. The output is captured centrally, meaning you have a fleet-wide view of AI workload performance without ever opening a remote desktop session. You can proactively identify which hardware models are struggling with the new AI demands before your users open a ticket.

Conclusion

As Windows evolves to include AI-specific hardware metrics, the complexity of endpoint management increases. If you rely on a fragmented stack of tools, every new Windows feature is just another thing to break your workflow.

With AlertMonitor, you gain the visibility of a monitoring tool and the power of an RMM in one platform. Don't let your team learn about AI workload crashes from users. See it, script it, and fix it—before it impacts productivity.

Related Resources

AlertMonitor RMM & Remote Management AlertMonitor Platform Overview Book a Demo RMM & Remote Management Resources