What Support Tickets are Reaching Humans

• What types of support issues are still reaching human agents as AI and self-service improve?
• Are the tickets reaching human agents becoming more technical?
• Why do some support issues still require direct access to the end user's device?

IT Leadership, Help Desk Directors and Managers, Customer Support Leaders, IT Operations

AI and self-service are increasingly capable of handling straightforward issues where there is a clear cause and a known fix. Our experience with ScreenMeet customers gives us visibility into what agents actually do once an issue reaches a human. A significant portion of that work involves inspecting and changing the actual state of an end user's device, something that can be difficult to resolve efficiently through a chatbot alone. The problem is often not "What is the fix?" but "What is the user reporting, and what is actually happening on the machine?"

AI works well when a problem can be clearly described and the answer is already known. Technology support does not always work that way. Windows may push an update that changes behavior, a software package may introduce a new issue, or two otherwise similar machines may behave differently because of configuration, drivers, permissions, or something else in the local environment. In my experience, many of the issues that eventually reach a human require seeing what is actually happening on the device.

Take a simple example. 

• A user opens an AI chatbot provided by their IT help desk and says, "My computer is slow." 
• That could mean a network issue, failing hardware, an application consuming too many resources, an operating system issue, a driver problem, malware, or something else entirely. 
• The user may also say the computer is slow when what they really mean is the application they spend most of their day using is slow. 
• A chatbot can work through those possibilities, but it still must determine which path matters, often by asking a non-technical user to gather information they may not understand or may not have permission to access.

Compare that with a user reporting a specific application and error code. If the error has a known cause and documented resolution, AI has a much stronger signal and may be able to provide the correct steps immediately. The difference is the quality of information available. A specific error can map directly to a known fix, while a vague symptom requires investigation of the actual environment.

At ScreenMeet, our enterprise customers use ScreenMeet Remote Support to assist their own customers and end users with technical issues. Our Remote Support telemetry gives us a view into what agents actually do once they are connected. Some of the most common actions include Task Manager, Programs and Features, PowerShell or Terminal, privileged access, Registry Editor, Services, and Device Manager. These are not primarily tools for looking up answers. They are used to inspect what is happening, test a theory, make a change, and verify whether that change fixed the issue.

From what we see, the support process increasingly looks like Inspect -> Test -> Change -> Verify. In enterprise environments, there is often another step as well: gaining permission to make the change. End users may intentionally lack administrator access, so even when AI knows what should be done, the user may not be authorized or technically equipped to do it. For enterprise support specifically, I think the bottleneck is gradually moving from knowledge to access. 

We analyzed 500 Remote Support session records representing 8,188 executions to better understand what support agents do once they are actively working on an end user's machine.

In summary, AI can assist with the reasoning, but many of the issues reaching human agents still require endpoint context, tools, permissions, and someone authorized to act on the machine.

Customer Examples

Here are three concrete examples that are grounded in the sampling of 500 Remote Support sessions over the last 30 days.  

• Inspecting the endpoint  
  
  • Task Manager was launched 1,847 times in the export. The telemetry does not identify the original ticket or root cause, but it shows that agents frequently inspect live endpoint state before deciding what to do next.

• Changing the endpoint  
  
  • Programs and Features, PowerShell, Terminal, Services, Registry Editor, and Device Manager account for thousands of recorded actions. These tools let support teams modify software, services, drivers, and system configuration instead of only providing instructions.

• Gaining authority to make the change  
  
  • Privileged and just-in-time access tools account for 2,195 executions. In enterprise environments, the correct remediation may already be known, but the support agent still needs an approved elevation path to perform it.

About ScreenMeet

ScreenMeet is redefining remote support with AI-powered technology that helps IT help desk and customer service teams resolve issues faster and more efficiently. Combining secure, cloud-based remote support with agentic AI, ScreenMeet helps support professionals understand problems, diagnose issues, take action, and document outcomes—all within the platforms they already use.  

ScreenMeet enables organizations to reduce resolution time, increase technician productivity, automate repetitive support work, and deliver better experiences for employees and customers.

Brien Grosinsky is a Senior Technical Support Engineer at ScreenMeet with more than eight years of experience in technical support, enterprise SaaS, and support operations. At ScreenMeet, he works directly with enterprise IT, networking, security, Salesforce, and ServiceNow teams to troubleshoot complex issues and improve how support is delivered at scale. 

LinkedIn:  linkedin.com/in/briengrosinsky

Email: bgrosinsky@screenmeet.com