Skip to main content

Why Government Agencies Aren't Ready to Return to the Office

Mike Marks
Riverbed

US government agencies are bringing more of their employees back into the office and implementing hybrid work schedules, but federal workers are worried that their agencies' IT architectures aren't built to handle the "new normal." They fear that the reactive, manual methods used by the current systems in dealing with user, IT architecture and application problems will degrade the user experience and negatively affect productivity.

In fact, according to a recent survey, many federal employees are concerned that they won't work as effectively back in the office as they did at home.

Employees Worry the User Experience Will Suffer

A Swish Data/Riverbed survey of federal IT workers, conducted in April and May by Market Connections, found that about half (47%) expect hybrid schedules that include teleworking two to four days a week to continue long-term, but that 52% think that the legacy IT and on-premises network architectures will struggle with the increased use of on-site collaboration tools such as Microsoft Teams and Zoom.

Those shortcomings manifest themselves in the user experience, the survey found. Forty-four percent of respondents are concerned that their user experience working in the office would fall short of their experience working from home. And a significant reason for the disconnect is the outdated methods agencies use to identify and quantify problems that arise with IT operations, and how those problems affect users.

A full 100% of survey respondents said it is at least somewhat important to measure the employees' user experience of productivity capabilities. But 87% said their agencies still rely — reactively — on waiting for help desk tickets to be generated before addressing problems. In fact, 51% rely on user phone calls as the primary means of quantifying problems.

The result, according to 59% of the feds surveyed, is that agencies aren't aware of the impact that changes in their IT environments are having. They're not measuring business-function productivity in terms of labor costs, latency or rates of success, all of which are tied to user experience. A majority of respondents said that although their organizations compare the business transaction productivity of teleworkers to that of in-office workers, they do so only partially. And measuring and comparing employee productivity is less likely to happen at civilian agencies than within the Department of Defense.

Unified Observability Takes a Proactive Approach

In light of the realities of hybrid work—with flexible home/office schedules, a greater reliance on collaboration tools and the shift toward greater use of digital workflows — federal employees are looking for a balance between collaborative tools and in-person needs.

A proactive approach that combines comprehensive network visibility and effective monitoring tools can provide a clear view of the user experience, which can enable both increased productivity and enhanced user satisfaction.

A Unified Observability platform can provide full-fidelity data from across the enterprise, capturing all transactions, packets and workflows. Using automated artificial intelligence and machine learning tools, it can prioritize actions to help enable cross-domain collaboration and coordination. While greatly improving the ability of IT teams to identify and remediate any problems (ranging from cyberattacks to workflow bottlenecks), Unified Observability enables IT teams to improve service delivery.

The higher quality of IT service will improve employee performance and the delivery of services to constituents and other stakeholders by allowing employees to more seamlessly perform their jobs, whether working from home or in the office. The visibility provided by a Unified Observability platform allows multiple teams across the enterprise to identify and analyze user issues while making use of automation to quickly resolve any problems.

"Government from Anywhere" as a Reality

The impact of the COVID-19 pandemic on top of digital transformations that were already underway has forever changed how agencies operate. The concept of "government from anywhere" is a widespread goal, but it requires a cultural change at most agencies. The survey recipients agreed, with 87% saying that their agency culture played a growing or significant role in driving change.

Abandoning inefficient, reactive methods of measuring the user experience in favor of enterprise-wide visibility with proactive monitoring and analysis will improve user experiences regardless of their location, while also boosting agency performance overall.

Mike Marks is VP of Product Marketing at Riverbed

The Latest

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

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 ...

Why Government Agencies Aren't Ready to Return to the Office

Mike Marks
Riverbed

US government agencies are bringing more of their employees back into the office and implementing hybrid work schedules, but federal workers are worried that their agencies' IT architectures aren't built to handle the "new normal." They fear that the reactive, manual methods used by the current systems in dealing with user, IT architecture and application problems will degrade the user experience and negatively affect productivity.

In fact, according to a recent survey, many federal employees are concerned that they won't work as effectively back in the office as they did at home.

Employees Worry the User Experience Will Suffer

A Swish Data/Riverbed survey of federal IT workers, conducted in April and May by Market Connections, found that about half (47%) expect hybrid schedules that include teleworking two to four days a week to continue long-term, but that 52% think that the legacy IT and on-premises network architectures will struggle with the increased use of on-site collaboration tools such as Microsoft Teams and Zoom.

Those shortcomings manifest themselves in the user experience, the survey found. Forty-four percent of respondents are concerned that their user experience working in the office would fall short of their experience working from home. And a significant reason for the disconnect is the outdated methods agencies use to identify and quantify problems that arise with IT operations, and how those problems affect users.

A full 100% of survey respondents said it is at least somewhat important to measure the employees' user experience of productivity capabilities. But 87% said their agencies still rely — reactively — on waiting for help desk tickets to be generated before addressing problems. In fact, 51% rely on user phone calls as the primary means of quantifying problems.

The result, according to 59% of the feds surveyed, is that agencies aren't aware of the impact that changes in their IT environments are having. They're not measuring business-function productivity in terms of labor costs, latency or rates of success, all of which are tied to user experience. A majority of respondents said that although their organizations compare the business transaction productivity of teleworkers to that of in-office workers, they do so only partially. And measuring and comparing employee productivity is less likely to happen at civilian agencies than within the Department of Defense.

Unified Observability Takes a Proactive Approach

In light of the realities of hybrid work—with flexible home/office schedules, a greater reliance on collaboration tools and the shift toward greater use of digital workflows — federal employees are looking for a balance between collaborative tools and in-person needs.

A proactive approach that combines comprehensive network visibility and effective monitoring tools can provide a clear view of the user experience, which can enable both increased productivity and enhanced user satisfaction.

A Unified Observability platform can provide full-fidelity data from across the enterprise, capturing all transactions, packets and workflows. Using automated artificial intelligence and machine learning tools, it can prioritize actions to help enable cross-domain collaboration and coordination. While greatly improving the ability of IT teams to identify and remediate any problems (ranging from cyberattacks to workflow bottlenecks), Unified Observability enables IT teams to improve service delivery.

The higher quality of IT service will improve employee performance and the delivery of services to constituents and other stakeholders by allowing employees to more seamlessly perform their jobs, whether working from home or in the office. The visibility provided by a Unified Observability platform allows multiple teams across the enterprise to identify and analyze user issues while making use of automation to quickly resolve any problems.

"Government from Anywhere" as a Reality

The impact of the COVID-19 pandemic on top of digital transformations that were already underway has forever changed how agencies operate. The concept of "government from anywhere" is a widespread goal, but it requires a cultural change at most agencies. The survey recipients agreed, with 87% saying that their agency culture played a growing or significant role in driving change.

Abandoning inefficient, reactive methods of measuring the user experience in favor of enterprise-wide visibility with proactive monitoring and analysis will improve user experiences regardless of their location, while also boosting agency performance overall.

Mike Marks is VP of Product Marketing at Riverbed

The Latest

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

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 ...