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Waste Not, Want Not - Raising VDI Performance at the Endpoint

Jeff Kalberg

A recent story in APMdigest revealed the amount of operational waste enterprises are experiencing as IT devotes significantly more time to performance issues related to digital transformation initiatives. The research study detailed in the story finds that IT professionals are losing over 2 hours every business day, or 522 hours per year. Study respondents noted a more complex technology environment was a leading culprit in these performance issues.

Complex technology isn't going away. In fact, more than likely, digital transformation will continue to add technical complexity. One area that enables enterprises to reduce complexity and streamline operations is their virtual desktop infrastructure (VDI). Virtualization is a linchpin of digital transformation and effectively optimizing an enterprise's VDI is essential to moving forward with digital technologies.

Delivering the best possible VDI performance means taking a fresh look at what "desktop" means today. The endpoint, or desktop, now can be a physical thin client, a software-defined thin client, a traditional laptop, a phone or tablet.

To reduce operational waste and achieve better performance across the desktop environment, consider these five actions:

1. Accommodating Self-Serve Access

Employees should be able to access certain applications without having to contact an IT help desk. Enabling "self-serve" application access, as appropriate, allows employees to access their personal desktop workspaces, and needed applications, without using valuable IT time.

However, there is a flip side to this: IT needs to control how far employees can take self-service. If employees are spending too much time onboarding more advanced applications, and less time being work-productive, then those applications may need to be controlled by IT.

2. Paying Attention to the Edge

Using centralized management software, IT can control and manage edge devices' use of applications residing in the data center.

For example, software managing thin clients can retrieve a user profile and populate the endpoint with applications that a user needs to be productive. This centralized approach can result in the economies of a single IT person managing as many as 30,000 endpoints – a great reduction in IT time and resources.

3. Thinking Software, not Hardware

Enterprises are moving away from endpoint hardware investments to software that supports the pace of digital transformation. Improving endpoint performance means being able to quickly onboard new employees, deliver custom configurations to a remote workforce using a variety of devices, and to quickly populate new applications for ready use. Endpoint software such as thin client firmware is a means of delivering profiles and applications via a single pane of glass, regardless of device.

4. Understanding User Expectations

Your average worker today wants to use many devices, with the expectation the device will deliver what they need to do their job. The "desktop" of today can range from software-driven thin clients to USB devices. Endpoint management must be able to manage all these devices, control application access and mitigate security risk. It is challenging since, for example, there are many versions of Android and iOS in use, with the threat that users are loading up applications that can pose risk to the network.

USB devices pose one solution, freeing the user from physical boundaries, yet delivering the desired level of endpoint security. A new employee, for example, can plug the device into their personal laptop, and securely receive the configuration and applications they need, without IT ever having to touch the device.

Enterprises are searching for these types of solutions that deliver an optimal user experience without adding to operational complexity.

5. Looking at the Bigger Picture

Getting ahead of digital transformation technology needs, and advancements, is critical to winning the digital game. The alternative is never really catching up with technology and being overwhelmed by the complex IT environments that are becoming standard today.

In the study of operational waste, IT professionals said, if they could reclaim those two hours a day, they would spend more time researching and deploying new systems/technologies.

Staying up to speed on virtualization technology is essential to digital transformation succeeding. Companiesmare innovating technology that plays right into the enablement of high VDI performance. Remote display technology that accommodates workers using graphics intensive applications is an example of delivering innovation that users expect. Freeing up IT time to continue to integrate these enhancements in the user experience has to be part of a thorough digital transformation.

Conclusion: Move Digital Transformation Forward with Optimized VDI

Enterprises are grappling with the challenges of digital transformation, from figuring out cloud deployment, data storage, and BYOD security threats to how to deliver an endpoint experience that optimizes performance.

These five actions will help IT deliver VDI performance that supports digital transformation initiatives. Improvements such as enabling workers to be more self-sufficient, and streamlining endpoint management will reduce operational waste, reduce both operational and capital expenditures, and maps to the market trend toward centralized endpoint management software that can accommodate a variety of devices.

Freeing up IT time will allow IT to better plan for more integration of digital technologies which in turn, increases the enterprise's competitive strength. After all, this is the purpose of digital transformation!

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Waste Not, Want Not - Raising VDI Performance at the Endpoint

Jeff Kalberg

A recent story in APMdigest revealed the amount of operational waste enterprises are experiencing as IT devotes significantly more time to performance issues related to digital transformation initiatives. The research study detailed in the story finds that IT professionals are losing over 2 hours every business day, or 522 hours per year. Study respondents noted a more complex technology environment was a leading culprit in these performance issues.

Complex technology isn't going away. In fact, more than likely, digital transformation will continue to add technical complexity. One area that enables enterprises to reduce complexity and streamline operations is their virtual desktop infrastructure (VDI). Virtualization is a linchpin of digital transformation and effectively optimizing an enterprise's VDI is essential to moving forward with digital technologies.

Delivering the best possible VDI performance means taking a fresh look at what "desktop" means today. The endpoint, or desktop, now can be a physical thin client, a software-defined thin client, a traditional laptop, a phone or tablet.

To reduce operational waste and achieve better performance across the desktop environment, consider these five actions:

1. Accommodating Self-Serve Access

Employees should be able to access certain applications without having to contact an IT help desk. Enabling "self-serve" application access, as appropriate, allows employees to access their personal desktop workspaces, and needed applications, without using valuable IT time.

However, there is a flip side to this: IT needs to control how far employees can take self-service. If employees are spending too much time onboarding more advanced applications, and less time being work-productive, then those applications may need to be controlled by IT.

2. Paying Attention to the Edge

Using centralized management software, IT can control and manage edge devices' use of applications residing in the data center.

For example, software managing thin clients can retrieve a user profile and populate the endpoint with applications that a user needs to be productive. This centralized approach can result in the economies of a single IT person managing as many as 30,000 endpoints – a great reduction in IT time and resources.

3. Thinking Software, not Hardware

Enterprises are moving away from endpoint hardware investments to software that supports the pace of digital transformation. Improving endpoint performance means being able to quickly onboard new employees, deliver custom configurations to a remote workforce using a variety of devices, and to quickly populate new applications for ready use. Endpoint software such as thin client firmware is a means of delivering profiles and applications via a single pane of glass, regardless of device.

4. Understanding User Expectations

Your average worker today wants to use many devices, with the expectation the device will deliver what they need to do their job. The "desktop" of today can range from software-driven thin clients to USB devices. Endpoint management must be able to manage all these devices, control application access and mitigate security risk. It is challenging since, for example, there are many versions of Android and iOS in use, with the threat that users are loading up applications that can pose risk to the network.

USB devices pose one solution, freeing the user from physical boundaries, yet delivering the desired level of endpoint security. A new employee, for example, can plug the device into their personal laptop, and securely receive the configuration and applications they need, without IT ever having to touch the device.

Enterprises are searching for these types of solutions that deliver an optimal user experience without adding to operational complexity.

5. Looking at the Bigger Picture

Getting ahead of digital transformation technology needs, and advancements, is critical to winning the digital game. The alternative is never really catching up with technology and being overwhelmed by the complex IT environments that are becoming standard today.

In the study of operational waste, IT professionals said, if they could reclaim those two hours a day, they would spend more time researching and deploying new systems/technologies.

Staying up to speed on virtualization technology is essential to digital transformation succeeding. Companiesmare innovating technology that plays right into the enablement of high VDI performance. Remote display technology that accommodates workers using graphics intensive applications is an example of delivering innovation that users expect. Freeing up IT time to continue to integrate these enhancements in the user experience has to be part of a thorough digital transformation.

Conclusion: Move Digital Transformation Forward with Optimized VDI

Enterprises are grappling with the challenges of digital transformation, from figuring out cloud deployment, data storage, and BYOD security threats to how to deliver an endpoint experience that optimizes performance.

These five actions will help IT deliver VDI performance that supports digital transformation initiatives. Improvements such as enabling workers to be more self-sufficient, and streamlining endpoint management will reduce operational waste, reduce both operational and capital expenditures, and maps to the market trend toward centralized endpoint management software that can accommodate a variety of devices.

Freeing up IT time will allow IT to better plan for more integration of digital technologies which in turn, increases the enterprise's competitive strength. After all, this is the purpose of digital transformation!

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...