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The Unsung Importance of Device Health in Remote Work

Mike Marks
Riverbed

As the global pandemic continues, it has become increasingly clear that companies across every industry are planning the "next normal" of their workplace with a much longer-term view. They have moved from serially extending temporary work-from-home (WFH) arrangements to establishing permanent policies focused on empowering people to WFE — work-from-everywhere.

Based on responses from over 100 business leaders polled by Aternity last month on their return-to-office plans, 93.4% said they plan to either remain remote or adopt a hybrid model. Enterprises are giving employees the option to work remotely on a permanent basis and paying one-time bonuses to relocate and set up home offices. They are also reprioritizing technology investments to support a digital employee experience tailored for the work-from-everywhere enterprise.

In our latest volume of the Aternity Global Remote Work Productivity Tracker, we examined data aggregated from millions of employee devices from hundreds of global companies managed via our platform to explore WFE initiatives. Specifically, we wanted to see how work-from-everywhere is influencing technology investments and impacting productivity. Here’s what we found.

Organizations Invested Heavily in Laptops

With the shift to remote work becoming permanent, IT must invest in laptops and related technologies to equip employees to be true digital nomads who can work from everywhere. The figure below illustrates the trend in usage away from office-based desktop computers toward laptops. It shows that the vast majority of employees used laptops in all industries except education even prior to the pandemic, and laptop usage naturally increased with the shift to remote work.


Education realized a huge jump in laptop usage in July, coinciding with the spike in remote work. This is when districts across the country prepared for the remote learning options or mandates now in place for the current school year.

While the percentage of employees using laptops is lowest in healthcare, the current upward trend provides more evidence that telehealth is here to stay.

It’s useful to examine what devices employees use by the generation of the CPU on which they are built. Although Gartner forecasts that spending on devices would drop 11% in 2020 due to budget constraints, our data provides a different view.


Enterprises are accelerating the deployment of newer generation devices since shifting to remote work. Normally, organizations deploy new devices that are two generations ahead of those being replaced. Since March, use of devices with 8th, 9th, and 10th generation CPUs increased by 13.7% as compared to before the pandemic. This corresponded with a 10.8% decline in use of 6th or earlier generation devices, and a small 2.9% drop in usage of 7th generation CPU-based machines.

The Positive Effect of Device Modernization on Employee Productivity

Web page load time is a key metric that affects employee productivity. Many factors impact the amount of time it takes a web page to load — ie. the performance of the user's device, the network they use to connect to the page, and the design and performance of the web page or application itself. The abrupt shift to remote work required adjustments to be made by employees, corporate IT departments, and network and content providers.

As this graph shows, these adaptions follow a distinct pattern, for every generation of user device.


Panic (mid-March) - Page load time increases as employees are sent home with the laptops and WiFi services on hand. Network congestion increases due to the volume of remote work and the likely massive increase in Netflix content consumed by people confined to their homes.

Fast fixes (next two weeks) - Tactical actions produce improvements. IT acquires whatever laptops they can lay their hands on, and scales and optimizes VPNs. Employees enhance their WiFi connections. Network and content providers make reactionary improvements, such as Netflix reducing screen resolution to improve network congestion.

Continuous improvement (May to now) - CIOs are now planning for long-term WFE as the next normal by optimizing application performance and investing in new devices for employees who need them most.

Employees working on devices based on newer generation CPUs spend 37% less time waiting for pages to load, as compared to those on 6th generation or older devices. This results in a productivity gain between 3 minutes to an hour per employee per day, depending on the extent to which employees rely on web-based applications for their jobs. Investing in newer generation devices is a way to counteract the remote work productivity tax we saw emerging over the summer.

As businesses continue down this path, it’s important to note that average performance doesn’t tell the whole story. Each employee will have their own unique digital experience based on many factors, including CPU, WiFi signal strength, ISP speed, device characteristics and the performance of the applications they are accessing. Our data provides proof that IT leaders must not only ensure the right apps are delivered to employees within their digital workplace but that their device is in good enough shape to handle them.

Mike Marks is VP of Product Marketing at Riverbed

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 ...

The Unsung Importance of Device Health in Remote Work

Mike Marks
Riverbed

As the global pandemic continues, it has become increasingly clear that companies across every industry are planning the "next normal" of their workplace with a much longer-term view. They have moved from serially extending temporary work-from-home (WFH) arrangements to establishing permanent policies focused on empowering people to WFE — work-from-everywhere.

Based on responses from over 100 business leaders polled by Aternity last month on their return-to-office plans, 93.4% said they plan to either remain remote or adopt a hybrid model. Enterprises are giving employees the option to work remotely on a permanent basis and paying one-time bonuses to relocate and set up home offices. They are also reprioritizing technology investments to support a digital employee experience tailored for the work-from-everywhere enterprise.

In our latest volume of the Aternity Global Remote Work Productivity Tracker, we examined data aggregated from millions of employee devices from hundreds of global companies managed via our platform to explore WFE initiatives. Specifically, we wanted to see how work-from-everywhere is influencing technology investments and impacting productivity. Here’s what we found.

Organizations Invested Heavily in Laptops

With the shift to remote work becoming permanent, IT must invest in laptops and related technologies to equip employees to be true digital nomads who can work from everywhere. The figure below illustrates the trend in usage away from office-based desktop computers toward laptops. It shows that the vast majority of employees used laptops in all industries except education even prior to the pandemic, and laptop usage naturally increased with the shift to remote work.


Education realized a huge jump in laptop usage in July, coinciding with the spike in remote work. This is when districts across the country prepared for the remote learning options or mandates now in place for the current school year.

While the percentage of employees using laptops is lowest in healthcare, the current upward trend provides more evidence that telehealth is here to stay.

It’s useful to examine what devices employees use by the generation of the CPU on which they are built. Although Gartner forecasts that spending on devices would drop 11% in 2020 due to budget constraints, our data provides a different view.


Enterprises are accelerating the deployment of newer generation devices since shifting to remote work. Normally, organizations deploy new devices that are two generations ahead of those being replaced. Since March, use of devices with 8th, 9th, and 10th generation CPUs increased by 13.7% as compared to before the pandemic. This corresponded with a 10.8% decline in use of 6th or earlier generation devices, and a small 2.9% drop in usage of 7th generation CPU-based machines.

The Positive Effect of Device Modernization on Employee Productivity

Web page load time is a key metric that affects employee productivity. Many factors impact the amount of time it takes a web page to load — ie. the performance of the user's device, the network they use to connect to the page, and the design and performance of the web page or application itself. The abrupt shift to remote work required adjustments to be made by employees, corporate IT departments, and network and content providers.

As this graph shows, these adaptions follow a distinct pattern, for every generation of user device.


Panic (mid-March) - Page load time increases as employees are sent home with the laptops and WiFi services on hand. Network congestion increases due to the volume of remote work and the likely massive increase in Netflix content consumed by people confined to their homes.

Fast fixes (next two weeks) - Tactical actions produce improvements. IT acquires whatever laptops they can lay their hands on, and scales and optimizes VPNs. Employees enhance their WiFi connections. Network and content providers make reactionary improvements, such as Netflix reducing screen resolution to improve network congestion.

Continuous improvement (May to now) - CIOs are now planning for long-term WFE as the next normal by optimizing application performance and investing in new devices for employees who need them most.

Employees working on devices based on newer generation CPUs spend 37% less time waiting for pages to load, as compared to those on 6th generation or older devices. This results in a productivity gain between 3 minutes to an hour per employee per day, depending on the extent to which employees rely on web-based applications for their jobs. Investing in newer generation devices is a way to counteract the remote work productivity tax we saw emerging over the summer.

As businesses continue down this path, it’s important to note that average performance doesn’t tell the whole story. Each employee will have their own unique digital experience based on many factors, including CPU, WiFi signal strength, ISP speed, device characteristics and the performance of the applications they are accessing. Our data provides proof that IT leaders must not only ensure the right apps are delivered to employees within their digital workplace but that their device is in good enough shape to handle them.

Mike Marks is VP of Product Marketing at Riverbed

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 ...