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As Remote Work Takes Off, Network Visibility Helps IT Keep Pace

Paul Davenport
AppNeta

While remote work policies have been gaining steam for the better part of the past decade across the enterprise space — driven in large part by more agile and scalable, cloud-delivered business solutions — recent events have pushed adoption into overdrive.

For starters, anxieties surrounding the global spread of the COVID-19 virus have encouraged business leaders to let employees collaborate via UCaaS and collaboration tools from remote locations rather than convene in group settings that could make workers vulnerable to exposure. But the remote work movement was gaining steam well before that, as factors like commuting and the environment have simply made allowing flexibility for how and where employees get the job done a more logical and cost-effective policy.

As a result, managing user experience at remote offices has become an integral part of the job for modern enterprise IT. But in most cases, when the number of remote locations the network supports increases, IT operations remain centrally located, as staffing a physical presence at each new office would eat into the cost savings and efficiency that cloud and SaaS tools are meant to enable. While these efficiencies are hugely beneficial to the business, they do fundamentally change the level of visibility IT used to have when teams were centralized and issues could be quickly addressed on-premises.

Without solutions that deliver visibility into remote locations or provide insight into traffic from those locations, IT can become overly dependent on end users to report app performance issues — and usually only after these problems have impacted performance. The trouble with this is that end users may be quick to blame the network for performance issues when the real culprit may be the app itself, not the underlying infrastructure.

When visibility into remote office performance is lacking, IT teams frequently end up wasting time and budget getting to the bottom of issues that are impacting users across the business. When dealing with poorly performing apps, not only do end users become unproductive and start missing deadlines, but IT often gets sidelined because they’re constantly putting out fires rather than getting strategic initiatives off the ground. This will Inevitably start to impact the reputation of the IT team, as performance issues become chronic and remote users are constantly frustrated.

Embracing Automation to Gain Visibility

With an automated monitoring strategy that can deliver a local perspective into issues hindering remote locations, IT can be proactively alerted to network and application performance problems before users are even impacted. This arms IT with the ability to quickly know if performance-impacting issues are caused by flaws with the enterprise infrastructure, service providers, connecting networks or the apps themselves.

Comprehensive visibility into the performance of every app, user, and location is also critical in helping IT ensure their network is equipped with the requirements necessary to support the new breed of cloud and SaaS tools users rely on most. This can help illuminate areas of the network where IT could leverage more cost-effective connectivity options like local Internet breakouts or SD-WAN connectivity instead of MPLS or other private circuits.

When IT can ensure they have complete visibility into their remote locations, they can more predictably ensure end users aren’t meaningfully impacted by performance issues, while also starting to think strategically about how to plan for the future. Visibility empowers teams to more predictably budget for projects and ensure they meet their goals on schedule, even allowing them to investigate and deliver more cost-effective connectivity at remote locations.

Paul Davenport is Marketing Communications Manager at AppNeta

The Latest

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

As Remote Work Takes Off, Network Visibility Helps IT Keep Pace

Paul Davenport
AppNeta

While remote work policies have been gaining steam for the better part of the past decade across the enterprise space — driven in large part by more agile and scalable, cloud-delivered business solutions — recent events have pushed adoption into overdrive.

For starters, anxieties surrounding the global spread of the COVID-19 virus have encouraged business leaders to let employees collaborate via UCaaS and collaboration tools from remote locations rather than convene in group settings that could make workers vulnerable to exposure. But the remote work movement was gaining steam well before that, as factors like commuting and the environment have simply made allowing flexibility for how and where employees get the job done a more logical and cost-effective policy.

As a result, managing user experience at remote offices has become an integral part of the job for modern enterprise IT. But in most cases, when the number of remote locations the network supports increases, IT operations remain centrally located, as staffing a physical presence at each new office would eat into the cost savings and efficiency that cloud and SaaS tools are meant to enable. While these efficiencies are hugely beneficial to the business, they do fundamentally change the level of visibility IT used to have when teams were centralized and issues could be quickly addressed on-premises.

Without solutions that deliver visibility into remote locations or provide insight into traffic from those locations, IT can become overly dependent on end users to report app performance issues — and usually only after these problems have impacted performance. The trouble with this is that end users may be quick to blame the network for performance issues when the real culprit may be the app itself, not the underlying infrastructure.

When visibility into remote office performance is lacking, IT teams frequently end up wasting time and budget getting to the bottom of issues that are impacting users across the business. When dealing with poorly performing apps, not only do end users become unproductive and start missing deadlines, but IT often gets sidelined because they’re constantly putting out fires rather than getting strategic initiatives off the ground. This will Inevitably start to impact the reputation of the IT team, as performance issues become chronic and remote users are constantly frustrated.

Embracing Automation to Gain Visibility

With an automated monitoring strategy that can deliver a local perspective into issues hindering remote locations, IT can be proactively alerted to network and application performance problems before users are even impacted. This arms IT with the ability to quickly know if performance-impacting issues are caused by flaws with the enterprise infrastructure, service providers, connecting networks or the apps themselves.

Comprehensive visibility into the performance of every app, user, and location is also critical in helping IT ensure their network is equipped with the requirements necessary to support the new breed of cloud and SaaS tools users rely on most. This can help illuminate areas of the network where IT could leverage more cost-effective connectivity options like local Internet breakouts or SD-WAN connectivity instead of MPLS or other private circuits.

When IT can ensure they have complete visibility into their remote locations, they can more predictably ensure end users aren’t meaningfully impacted by performance issues, while also starting to think strategically about how to plan for the future. Visibility empowers teams to more predictably budget for projects and ensure they meet their goals on schedule, even allowing them to investigate and deliver more cost-effective connectivity at remote locations.

Paul Davenport is Marketing Communications Manager at AppNeta

The Latest

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...