Skip to main content

Businesses Must Avoid the Remote Work Productivity Tax

Mike Marks
Riverbed

During the past three months of the COVID-19 pandemic, the business world has grappled with the question of whether to or when to reopen offices. Numerous companies have publicly announced permanent remote work options or signaled a move to hybrid work models, but most have yet to determine their plans. While the decision-making fundamentally rests on how well countries and states manage the virus, CEOs also need to consider the long-term impact on employee productivity.

With companies in Europe, the Middle East and Asia (EMEA) reopening offices based on declining rates of infection, data is starting emerge that helps inform this decision-making. When comparing EMEA and North America, and specifically the U.S., there is evidence of a remote work productivity tax.

In the U.S. and countries in Europe where the share of remote work remains extremely high, employee productivity continues to fall. At the same time, productivity is rising in European countries where the share of in-office work is accelerating. Figure 1 illustrates hours spent on business applications from the beginning of February through July 10, with a breakdown of in-office vs. remote work. Our initial data showed that productivity initially increased in North America (+23% by the end of March), while it declined in Europe by 8.2% in the same period.

That initial trend has not held, however. Between March 26 and July 10, productivity in North America declined by 14%. With a near constant 85% of employees continuing to work from home, this suggests that workers are getting less productive the longer the remote work shift continues – imposing a remote work productivity tax on companies. In Europe, overall productivity increased by 2% from March 26 to July 10.


Figure 1

Evidence of the Remote Productivity Tax

Figure 2 provides greater insights into this trend. The entire productivity decline in North America was driven by the 14% decrease in productivity among U.S. employees since the shift to remote work started in mid-March. Canadians have better adapted to remote work, maintaining about the same overall productivity during the same period.


Figure 2

Additional evidence of a remote work productivity tax is shown in data from European countries where remote work is still the dominant model. In Spain, Belgium, and Switzerland, where remote work is at least twice as high as in-office work, overall productivity has decreased since March 26, despite increasing in-office work.


Figure 4

These numbers showcase why employee experience is more important than ever. With employees working from home, business and IT leaders must address fundamental questions around whether the workforce has the right IT resources to get their jobs done.

Does sufficient VPN capacity exist to handle the remote employee population?

Do employees have laptops with sufficient resources and performance to do their jobs properly?

To what extent does application performance vary from expected baselines when employees access those apps remotely?

Without insights into how employees are interacting with their digital workplace, businesses won’t be able to proactively identify tech issues that can hinder productivity.

Of course, it’s not just about the availability of technology. The performance of technology is what matters to employees attempting to do their jobs remotely. IT must ensure that employees experience fast response for the business-critical applications on which they depend on, collaboration tools must be reliable enough to foster effective communication, and device and connectivity performance must be reliable. IT must also keep pace with an increasing volume of end user issues, all at time when the pressure is on to control costs.

In this era of remote work, technology performance plays an outsized role in employee experience. Companies need insights into where to invest and where to cut costs in order to maintain business continuity.

Mike Marks is VP of Product Marketing at Riverbed

The Latest

IT organizations have historically measured success by how quickly they can respond when something goes wrong. The entire discipline of Incident Management has been optimized around mean time to resolution, first-response SLAs and ticket closure rates. But new research suggests that even though this is a well-executed playbook, it's no longer enough to retain customers ...

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Businesses Must Avoid the Remote Work Productivity Tax

Mike Marks
Riverbed

During the past three months of the COVID-19 pandemic, the business world has grappled with the question of whether to or when to reopen offices. Numerous companies have publicly announced permanent remote work options or signaled a move to hybrid work models, but most have yet to determine their plans. While the decision-making fundamentally rests on how well countries and states manage the virus, CEOs also need to consider the long-term impact on employee productivity.

With companies in Europe, the Middle East and Asia (EMEA) reopening offices based on declining rates of infection, data is starting emerge that helps inform this decision-making. When comparing EMEA and North America, and specifically the U.S., there is evidence of a remote work productivity tax.

In the U.S. and countries in Europe where the share of remote work remains extremely high, employee productivity continues to fall. At the same time, productivity is rising in European countries where the share of in-office work is accelerating. Figure 1 illustrates hours spent on business applications from the beginning of February through July 10, with a breakdown of in-office vs. remote work. Our initial data showed that productivity initially increased in North America (+23% by the end of March), while it declined in Europe by 8.2% in the same period.

That initial trend has not held, however. Between March 26 and July 10, productivity in North America declined by 14%. With a near constant 85% of employees continuing to work from home, this suggests that workers are getting less productive the longer the remote work shift continues – imposing a remote work productivity tax on companies. In Europe, overall productivity increased by 2% from March 26 to July 10.


Figure 1

Evidence of the Remote Productivity Tax

Figure 2 provides greater insights into this trend. The entire productivity decline in North America was driven by the 14% decrease in productivity among U.S. employees since the shift to remote work started in mid-March. Canadians have better adapted to remote work, maintaining about the same overall productivity during the same period.


Figure 2

Additional evidence of a remote work productivity tax is shown in data from European countries where remote work is still the dominant model. In Spain, Belgium, and Switzerland, where remote work is at least twice as high as in-office work, overall productivity has decreased since March 26, despite increasing in-office work.


Figure 4

These numbers showcase why employee experience is more important than ever. With employees working from home, business and IT leaders must address fundamental questions around whether the workforce has the right IT resources to get their jobs done.

Does sufficient VPN capacity exist to handle the remote employee population?

Do employees have laptops with sufficient resources and performance to do their jobs properly?

To what extent does application performance vary from expected baselines when employees access those apps remotely?

Without insights into how employees are interacting with their digital workplace, businesses won’t be able to proactively identify tech issues that can hinder productivity.

Of course, it’s not just about the availability of technology. The performance of technology is what matters to employees attempting to do their jobs remotely. IT must ensure that employees experience fast response for the business-critical applications on which they depend on, collaboration tools must be reliable enough to foster effective communication, and device and connectivity performance must be reliable. IT must also keep pace with an increasing volume of end user issues, all at time when the pressure is on to control costs.

In this era of remote work, technology performance plays an outsized role in employee experience. Companies need insights into where to invest and where to cut costs in order to maintain business continuity.

Mike Marks is VP of Product Marketing at Riverbed

The Latest

IT organizations have historically measured success by how quickly they can respond when something goes wrong. The entire discipline of Incident Management has been optimized around mean time to resolution, first-response SLAs and ticket closure rates. But new research suggests that even though this is a well-executed playbook, it's no longer enough to retain customers ...

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...