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How the Proliferation of Cloud, Internet and Remote Work Impacts Network Operations

Jeremy Rossbach

Efficiency is a highly-desirable objective in business. Efficiency, after all, often translates to measurable savings of all kinds — cost, time, effort, etc. But, when the push for efficiency interferes with other important goals, a business may find itself looking at diminishing returns rather than the efficiency gains it was banking on.

We're seeing this scenario play out in enterprises around the world as they continue to struggle with infrastructures and remote work models with an eye toward operational efficiencies. In contrast to that goal, a recent Broadcom survey of global IT and network professionals found widespread adoption of these strategies is making the network more complex and hampering observability, leading to uptime, performance and security issues. Let's look more closely at these challenges.

Image
Broadcom

 

Cloud and Internet Reliance

According to the survey, 98% of companies are using or planning to use cloud infrastructure and 95% are still supporting remote workers. As a result, the network has become increasingly more complex, noted by 78% of respondents.

Consider that the modern IT environment now includes cloud — public, private and hybrid — virtual machines and network devices, and numerous applications and resources connected across the internet. Network endpoints are spread far and wide and often exist in workers' homes, which makes it challenging to gain the visibility necessary to ensure uptime, performance, and security.

Digging deeper, when asked what specifically is making network operations more challenging, the top answer was cloud environments (62%). Close behind at 55%, respondents cited overall scale, including physical and virtual devices and those not directly controlled by the IT teams, such as public cloud infrastructure and personal devices.

This reliance on the cloud and public internet means much of the network is hidden from view and out of network operators' control. In fact, 80% of respondents claim internet and cloud environments create network blind spots which can often create delays in issue remediation.

Teams Lack Critical Data

When network operations teams don't have the information they need to ensure uptime and performance, it's a problem that can lead to costly downtime. In fact, 76% of respondents said slow or missing data directly impedes resolution times. Yet, 95% of respondents say they do not get the information they need from ISPs and cloud providers, indicative of the information challenge network teams are facing. What's worse, 84% of network professionals indicated that they regularly learn about issues from users.

Asked to elaborate on the information they need but aren't getting from ISPs, survey respondents cited path latency and node or hop issues, information about route changes, DDoS attack locations, DNS issues, historical performance by path, and path packet loss. This is critical information network operations teams could use proactively to prevent network performance or availability incidents and improve issue resolution speeds.

Despite expectations for better information flow from CSPs, respondents offered a list of information they need but don't get from these providers, including security events and infrastructure issues, authentication and access issues, node and hop issues, and path latency.

This lack of visibility into cloud and internet network issues is a problem with potentially costly repercussions.

Poor network operations tools exacerbate issues

Tooling is a common approach to managing an increasingly complex network as evidenced by the 84% of organizations that use five or more network management tools. Likely purchased to support new technologies such as cloud or remote employees, the use of numerous tools adds additional complexity and costs. Interestingly, over 30% of respondents directly called out poor network operation tools for making network operations more challenging. It seems clear that organizations are struggling to find the right network operations tools to meet their needs leading to tool sprawl.

A more efficient network means better business performance

For efficient and effective network operations, observability is paramount. Lack of observability makes ensuring uptime, performance, and security more challenging and also creates delays in issue remediation. With demand for reliable IT networks at an all-time high as the workplace continues to expand and adopt remote and transitory work models, the need for end-to-end observability cannot be understated. Modern network operation tools can help network teams overcome blind spots by directly pulling in information from cloud and internet providers and consolidating network information in one place. Efficiency may be a business objective, but a reliable network must take precedence. After all, if the network goes down, so does the business.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.

How the Proliferation of Cloud, Internet and Remote Work Impacts Network Operations

Jeremy Rossbach

Efficiency is a highly-desirable objective in business. Efficiency, after all, often translates to measurable savings of all kinds — cost, time, effort, etc. But, when the push for efficiency interferes with other important goals, a business may find itself looking at diminishing returns rather than the efficiency gains it was banking on.

We're seeing this scenario play out in enterprises around the world as they continue to struggle with infrastructures and remote work models with an eye toward operational efficiencies. In contrast to that goal, a recent Broadcom survey of global IT and network professionals found widespread adoption of these strategies is making the network more complex and hampering observability, leading to uptime, performance and security issues. Let's look more closely at these challenges.

Image
Broadcom

 

Cloud and Internet Reliance

According to the survey, 98% of companies are using or planning to use cloud infrastructure and 95% are still supporting remote workers. As a result, the network has become increasingly more complex, noted by 78% of respondents.

Consider that the modern IT environment now includes cloud — public, private and hybrid — virtual machines and network devices, and numerous applications and resources connected across the internet. Network endpoints are spread far and wide and often exist in workers' homes, which makes it challenging to gain the visibility necessary to ensure uptime, performance, and security.

Digging deeper, when asked what specifically is making network operations more challenging, the top answer was cloud environments (62%). Close behind at 55%, respondents cited overall scale, including physical and virtual devices and those not directly controlled by the IT teams, such as public cloud infrastructure and personal devices.

This reliance on the cloud and public internet means much of the network is hidden from view and out of network operators' control. In fact, 80% of respondents claim internet and cloud environments create network blind spots which can often create delays in issue remediation.

Teams Lack Critical Data

When network operations teams don't have the information they need to ensure uptime and performance, it's a problem that can lead to costly downtime. In fact, 76% of respondents said slow or missing data directly impedes resolution times. Yet, 95% of respondents say they do not get the information they need from ISPs and cloud providers, indicative of the information challenge network teams are facing. What's worse, 84% of network professionals indicated that they regularly learn about issues from users.

Asked to elaborate on the information they need but aren't getting from ISPs, survey respondents cited path latency and node or hop issues, information about route changes, DDoS attack locations, DNS issues, historical performance by path, and path packet loss. This is critical information network operations teams could use proactively to prevent network performance or availability incidents and improve issue resolution speeds.

Despite expectations for better information flow from CSPs, respondents offered a list of information they need but don't get from these providers, including security events and infrastructure issues, authentication and access issues, node and hop issues, and path latency.

This lack of visibility into cloud and internet network issues is a problem with potentially costly repercussions.

Poor network operations tools exacerbate issues

Tooling is a common approach to managing an increasingly complex network as evidenced by the 84% of organizations that use five or more network management tools. Likely purchased to support new technologies such as cloud or remote employees, the use of numerous tools adds additional complexity and costs. Interestingly, over 30% of respondents directly called out poor network operation tools for making network operations more challenging. It seems clear that organizations are struggling to find the right network operations tools to meet their needs leading to tool sprawl.

A more efficient network means better business performance

For efficient and effective network operations, observability is paramount. Lack of observability makes ensuring uptime, performance, and security more challenging and also creates delays in issue remediation. With demand for reliable IT networks at an all-time high as the workplace continues to expand and adopt remote and transitory work models, the need for end-to-end observability cannot be understated. Modern network operation tools can help network teams overcome blind spots by directly pulling in information from cloud and internet providers and consolidating network information in one place. Efficiency may be a business objective, but a reliable network must take precedence. After all, if the network goes down, so does the business.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.