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Top Tricks for Taming Call Center Tickets - Part 1

Tim Flower

"We can't fix it if they don't call."

I can't count how many times I've said those words in my IT career. Users suffering with technology issues often suffer in silence. However, IT teams have struggled for decades with help desk call volumes and ticket counts that are just too high.

So we have competing priorities — We need users to call in their issues, while conversely we need our ticket volumes to decrease. And ironically, despite the myriad of technology advances, the only real tool available to business end users when they have an IT problem is the old-fashioned telephone. It's no wonder call center tickets are on the rise, incidents are escalated to higher and more complex service levels, and both end users and IT support staff are frustrated.

End users lose an average of 20 minutes each day because of device failures

The problem is that technology has simply grown too complex and too quickly for mere humans to effectively monitor and manage it all. Aside from the impact to technology teams and their expenses related to call center tickets, business user productivity is sorely impacted. After all, the mission of IT needs to be completely focused on enabling the user. Even in the most sophisticated IT organization, end users lose an average of 20 minutes each day because of device failures. That's over two weeks per year per user.

So, how can IT lower the amount of call center tickets, quickly resolve those incidents that can't be avoided, and reduce their own costs in the process? Below are three key strategies:

1. Don't wait – Investigate

Waiting for business end users to "call in" their computer issues is old-school. Progressive companies are turning to cognitive or AI-based solutions to analyze and uncover issues that are impacting productivity, prioritize them, and fix them before end users are even aware of the issue.

This strategy represents a major difference in approach, shifting methodologies from reactionary, ticket-based processes to a system that is proactive and fact-based. Leveraging data analytics to uncover issues and trends will allow for improved response times and will also help uncover hidden insights.

2. Don't hope - Get the full scope

If you wait for business end users to call for help, your only option is to hope they actually call. Many end users either try to resolve issues on their own, or wait for the problem to go away. Through experience, users have learned that calling the help desk results in a very lengthy and frustrating process, or having to deal with new issues that surface when trying to fix the first one. And when end users take matters into their own hands, IT is left in the dark and have a very difficult time defining the true scope of the problem.

Identifying issues without user dependency means you can find everyone impacted for a proper response. When coupled with a proactive investigation, finding the full scope of a given issue allows for real prioritization and a full understanding of enterprise health.

3. Don't just remediate - Automate

Even with the full scope of the problem, and its associated business impact identified, it takes human effort to apply the fix. Much like software delivery, an incremental business benefit is achieved when you can automate the fix and apply it to everyone who might be impacted, whether or not they called the Help Desk.

Engaging directly with the end users at the time of the event will let them know that IT is watching out for them and fixing their issues without the need to call the help desk.

IT departments that shift from reactionary fire fighters to becoming proactive business partners find their ticket counts reduced from 20 to 50 percent or more

IT departments that shift from reactionary fire fighters to becoming proactive business partners find their ticket counts reduced from 20 to 50 percent or more. These reductions can help IT with improved Service Level Agreements (SLAs) and significantly reduce their costs. The bigger benefit to the enterprise as a whole is that the IT environment is stabilized, users are productive, and IT is now seen as a strategic business partner.

Read Top Tricks for Taming Call Center Tickets - Part 2

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Top Tricks for Taming Call Center Tickets - Part 1

Tim Flower

"We can't fix it if they don't call."

I can't count how many times I've said those words in my IT career. Users suffering with technology issues often suffer in silence. However, IT teams have struggled for decades with help desk call volumes and ticket counts that are just too high.

So we have competing priorities — We need users to call in their issues, while conversely we need our ticket volumes to decrease. And ironically, despite the myriad of technology advances, the only real tool available to business end users when they have an IT problem is the old-fashioned telephone. It's no wonder call center tickets are on the rise, incidents are escalated to higher and more complex service levels, and both end users and IT support staff are frustrated.

End users lose an average of 20 minutes each day because of device failures

The problem is that technology has simply grown too complex and too quickly for mere humans to effectively monitor and manage it all. Aside from the impact to technology teams and their expenses related to call center tickets, business user productivity is sorely impacted. After all, the mission of IT needs to be completely focused on enabling the user. Even in the most sophisticated IT organization, end users lose an average of 20 minutes each day because of device failures. That's over two weeks per year per user.

So, how can IT lower the amount of call center tickets, quickly resolve those incidents that can't be avoided, and reduce their own costs in the process? Below are three key strategies:

1. Don't wait – Investigate

Waiting for business end users to "call in" their computer issues is old-school. Progressive companies are turning to cognitive or AI-based solutions to analyze and uncover issues that are impacting productivity, prioritize them, and fix them before end users are even aware of the issue.

This strategy represents a major difference in approach, shifting methodologies from reactionary, ticket-based processes to a system that is proactive and fact-based. Leveraging data analytics to uncover issues and trends will allow for improved response times and will also help uncover hidden insights.

2. Don't hope - Get the full scope

If you wait for business end users to call for help, your only option is to hope they actually call. Many end users either try to resolve issues on their own, or wait for the problem to go away. Through experience, users have learned that calling the help desk results in a very lengthy and frustrating process, or having to deal with new issues that surface when trying to fix the first one. And when end users take matters into their own hands, IT is left in the dark and have a very difficult time defining the true scope of the problem.

Identifying issues without user dependency means you can find everyone impacted for a proper response. When coupled with a proactive investigation, finding the full scope of a given issue allows for real prioritization and a full understanding of enterprise health.

3. Don't just remediate - Automate

Even with the full scope of the problem, and its associated business impact identified, it takes human effort to apply the fix. Much like software delivery, an incremental business benefit is achieved when you can automate the fix and apply it to everyone who might be impacted, whether or not they called the Help Desk.

Engaging directly with the end users at the time of the event will let them know that IT is watching out for them and fixing their issues without the need to call the help desk.

IT departments that shift from reactionary fire fighters to becoming proactive business partners find their ticket counts reduced from 20 to 50 percent or more

IT departments that shift from reactionary fire fighters to becoming proactive business partners find their ticket counts reduced from 20 to 50 percent or more. These reductions can help IT with improved Service Level Agreements (SLAs) and significantly reduce their costs. The bigger benefit to the enterprise as a whole is that the IT environment is stabilized, users are productive, and IT is now seen as a strategic business partner.

Read Top Tricks for Taming Call Center Tickets - Part 2

Hot Topics

The Latest

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...