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Lack of Infrastructure Visibility Puts Businesses at Risk

Len Rosenthal

Most enterprises lack the complete visibility required to avoid business-impacting application outages and slowdowns – resulting in nearly 90 percent of enterprises being unable to consistently meet service level agreements (SLAs) for their business-critical applications, according to a recent survey conducted by Dimensional Research and Virtual Instruments. This research indicates a serious gap in IT operations teams' ability to monitor their enterprises' highly virtualized, multi-vendor hybrid data center environments, and the results show that this lack of visibility is significantly impacting business.

Blind Spots, Slowdowns and Outages Abound

59 percent of application outages and performance problems are related to infrastructure

The reality is that large enterprises endure a substantial number of application outages and performance issues every year, and an overwhelming number of those surveyed indicated that a slowdown impacts businesses just as much as a full outage.

86 percent of users experience two or more significant outages a year, with 61 percent suffering from four or more in the same period.

59 percent of application outages and performance problems are related to infrastructure, which begs the question: why can't IT teams see these problems coming, and what's getting in the way of timely resolution?

Too Many Cooks in the Kitchen

There are many dozens of infrastructure and application monitoring tools available to enterprises, so why does this visibility gap still exist?

This research showed that it's not necessarily a lack of tools that may be causing the problem, but rather the combination of too many silo-specific tools. In fact, more than 70 percent of respondents use more than five IT infrastructure monitoring tools, and 15 percent use more than 20!

But despite this plethora of tools, 54 percent of companies lack full visibility into their infrastructure and application workload behavior, and 42 percent of companies operate primarily in "reactive mode" when managing their infrastructure.

Teamwork Makes the Dream Work

When it comes to the modern enterprise, there's no single internal team that can accurately manage and assess application performance requirements. However, less than half of enterprises take a collaborative approach to establishing performance requirements for new data center infrastructure. With no collective understanding of how applications relate to the underlying infrastructure, the resulting blind spots cause chain reactions that leave enterprises highly exposed.

79 percent of application outages and other issues directly impact customers

Nearly 40 percent of enterprises say that performance issues related to infrastructure are the most challenging to resolve, and when you consider that 79 percent of application outages and other issues directly impact customers, there just isn't room for guessing.

Deeper Insights Are the Key

The lack of visibility and proactive infrastructure and application management contributes to a lack of confidence from IT teams and their executives. In fact, 62 percent doubt that their current infrastructure would be able to meet their projected performance needs in the next two years, and two-thirds of respondents feel that they're often held personally responsible for application outages and slowdowns.

In addition, with an increasing number of applications being deployed in public clouds, nearly 65 percent are concerned about the perceived value of the internal IT infrastructure team to the business.

As discouraging as these findings may seem, the numbers indicate a strong opportunity for engineering, operations and application teams to come together and gain a deeper understanding of the impact of their applications on the underlying infrastructure, and visa versa. Since applications and infrastructure are intertwined to the point where they can no longer be viewed as distinct entities, an infrastructure monitoring approach that understands application workload behavior is essential to performance assurance.

The bottom line is that in today's highly competitive business environment, enterprises cannot afford to test their customers' limited patience by having an unacceptable number of application outages or slowdowns.

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 ...

Lack of Infrastructure Visibility Puts Businesses at Risk

Len Rosenthal

Most enterprises lack the complete visibility required to avoid business-impacting application outages and slowdowns – resulting in nearly 90 percent of enterprises being unable to consistently meet service level agreements (SLAs) for their business-critical applications, according to a recent survey conducted by Dimensional Research and Virtual Instruments. This research indicates a serious gap in IT operations teams' ability to monitor their enterprises' highly virtualized, multi-vendor hybrid data center environments, and the results show that this lack of visibility is significantly impacting business.

Blind Spots, Slowdowns and Outages Abound

59 percent of application outages and performance problems are related to infrastructure

The reality is that large enterprises endure a substantial number of application outages and performance issues every year, and an overwhelming number of those surveyed indicated that a slowdown impacts businesses just as much as a full outage.

86 percent of users experience two or more significant outages a year, with 61 percent suffering from four or more in the same period.

59 percent of application outages and performance problems are related to infrastructure, which begs the question: why can't IT teams see these problems coming, and what's getting in the way of timely resolution?

Too Many Cooks in the Kitchen

There are many dozens of infrastructure and application monitoring tools available to enterprises, so why does this visibility gap still exist?

This research showed that it's not necessarily a lack of tools that may be causing the problem, but rather the combination of too many silo-specific tools. In fact, more than 70 percent of respondents use more than five IT infrastructure monitoring tools, and 15 percent use more than 20!

But despite this plethora of tools, 54 percent of companies lack full visibility into their infrastructure and application workload behavior, and 42 percent of companies operate primarily in "reactive mode" when managing their infrastructure.

Teamwork Makes the Dream Work

When it comes to the modern enterprise, there's no single internal team that can accurately manage and assess application performance requirements. However, less than half of enterprises take a collaborative approach to establishing performance requirements for new data center infrastructure. With no collective understanding of how applications relate to the underlying infrastructure, the resulting blind spots cause chain reactions that leave enterprises highly exposed.

79 percent of application outages and other issues directly impact customers

Nearly 40 percent of enterprises say that performance issues related to infrastructure are the most challenging to resolve, and when you consider that 79 percent of application outages and other issues directly impact customers, there just isn't room for guessing.

Deeper Insights Are the Key

The lack of visibility and proactive infrastructure and application management contributes to a lack of confidence from IT teams and their executives. In fact, 62 percent doubt that their current infrastructure would be able to meet their projected performance needs in the next two years, and two-thirds of respondents feel that they're often held personally responsible for application outages and slowdowns.

In addition, with an increasing number of applications being deployed in public clouds, nearly 65 percent are concerned about the perceived value of the internal IT infrastructure team to the business.

As discouraging as these findings may seem, the numbers indicate a strong opportunity for engineering, operations and application teams to come together and gain a deeper understanding of the impact of their applications on the underlying infrastructure, and visa versa. Since applications and infrastructure are intertwined to the point where they can no longer be viewed as distinct entities, an infrastructure monitoring approach that understands application workload behavior is essential to performance assurance.

The bottom line is that in today's highly competitive business environment, enterprises cannot afford to test their customers' limited patience by having an unacceptable number of application outages or slowdowns.

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 ...