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Missing Opportunities for Early Issue Identification

Pete Goldin
APMdigest

Enterprises are not prioritizing holistic monitoring technology and are missing the opportunity for early issue identification that could prevent customer-impacting problems and, ultimately, damage to the company’s reputation, according to a study conducted by Forrester Consulting on behalf of Virtual Instruments.

The study titled Shift to Workload-Centric Infrastructure Performance Management yielded five key findings:

■ Resolving critical performance issues takes hours. Further, 25 percent of respondents noted that resolution could exceed a day in many cases.

■ Poor communication and a lack of consensus hamper problem resolution. A lack of ownership of issues and distrust among teams can cause poor communication and extend the time to resolution.

■ Most respondents incorrectly believed their environments are dynamic. The disparity between the number of respondents who classified their environments as dynamic (72 percent) and those able to quickly resolve issues (18 percent) is quite expansive.

■ Virtualization is making problem resolution more difficult. 43 percent of respondents agreed that virtualization is increasing the complexity of both the systems and issues resolution.

■ Real-time data collection, advanced analytics and data correlation across domains are required to resolve problems in dynamic environments. Real-time monitoring helps optimize resources and drive performance, agility and availability to peak levels.

Virtual Instruments commissioned Forrester Consulting to survey 152 IT infrastructure and operations decision-makers. Per Virtual Instruments, the research illustrates the need for unified IT infrastructure performance management (IPM) tools with correlated data across server, storage and network domains. The findings revealed a gap in IPM that stems from various causes, including poor communication and inadequate monitoring tools.

“We commissioned the Forrester Consulting study to gain an understanding of the performance pain points in enterprise IT departments. On any given day, IT professionals may be struggling with hours of downtime as a result of critical performance issues,” said John Gentry, Virtual Instruments’ VP of Marketing and Alliances. “The survey results tell us that strong IPM solutions can alleviate many of the primary issues identified, improving overall system performance and relieving much of the stress that IT infrastructure decision-makers experience today.”

Pete Goldin is Editor and Publisher of APMdigest

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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 ...

Missing Opportunities for Early Issue Identification

Pete Goldin
APMdigest

Enterprises are not prioritizing holistic monitoring technology and are missing the opportunity for early issue identification that could prevent customer-impacting problems and, ultimately, damage to the company’s reputation, according to a study conducted by Forrester Consulting on behalf of Virtual Instruments.

The study titled Shift to Workload-Centric Infrastructure Performance Management yielded five key findings:

■ Resolving critical performance issues takes hours. Further, 25 percent of respondents noted that resolution could exceed a day in many cases.

■ Poor communication and a lack of consensus hamper problem resolution. A lack of ownership of issues and distrust among teams can cause poor communication and extend the time to resolution.

■ Most respondents incorrectly believed their environments are dynamic. The disparity between the number of respondents who classified their environments as dynamic (72 percent) and those able to quickly resolve issues (18 percent) is quite expansive.

■ Virtualization is making problem resolution more difficult. 43 percent of respondents agreed that virtualization is increasing the complexity of both the systems and issues resolution.

■ Real-time data collection, advanced analytics and data correlation across domains are required to resolve problems in dynamic environments. Real-time monitoring helps optimize resources and drive performance, agility and availability to peak levels.

Virtual Instruments commissioned Forrester Consulting to survey 152 IT infrastructure and operations decision-makers. Per Virtual Instruments, the research illustrates the need for unified IT infrastructure performance management (IPM) tools with correlated data across server, storage and network domains. The findings revealed a gap in IPM that stems from various causes, including poor communication and inadequate monitoring tools.

“We commissioned the Forrester Consulting study to gain an understanding of the performance pain points in enterprise IT departments. On any given day, IT professionals may be struggling with hours of downtime as a result of critical performance issues,” said John Gentry, Virtual Instruments’ VP of Marketing and Alliances. “The survey results tell us that strong IPM solutions can alleviate many of the primary issues identified, improving overall system performance and relieving much of the stress that IT infrastructure decision-makers experience today.”

Pete Goldin is Editor and Publisher of APMdigest

Hot Topics

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 ...