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The Challenge of Managing Converged Infrastructure

Kent Erickson

While the use of converged infrastructure (CI) is becoming mainstream, the accompanying management tools are still a challenge, according to the third annual global State of Converged Infrastructure survey from Zenoss.

CI systems integrate servers, storage, network and virtualization components into one system that can be managed as a single unit rather than separate systems. With 84% of all respondents using or planning to deploy CI, the technology has reached mainstream status. Companies large and small are building data centers around these integrated systems because they reduce investment risk and offer a faster way to scale out IT infrastructure capacity.

"While converged infrastructure architectures are delivering real value as the foundational element behind the transformation already underway in enterprise data centers, these packaged offerings are lacking unified monitoring and analytics software," said Megan Lueders, VP of Marketing at Zenoss. "With more effective management tools available, there is a huge opportunity for IT organizations shops to dramatically increase their agility and IT effectiveness."

The 2015 CI survey provides many additional insights into data center modernization, including:

■ In companies with more than 5,000 employees, only 8% are not using or considering CI.

■ Despite being pervasively adopted, the survey results indicate management software is lagging behind, with 63% of respondents indicating they manage their CI deployments by repurposing existing management tools designed for traditional infrastructure.

■ Astonishingly, 25% of companies who have deployed CI are dealing with seven or more tools to manage it.

■ Key drivers for CI deployments include projects for big data, infrastructure as a service, unified communications, and custom application development.

Zenoss polled 410 IT professionals from across the world to determine how technology leaders are leveraging CI to respond to business needs, including 151 who have already adopted CI within their IT environments and another 194 who were currently considering or planning for it. 32% of these respondents come from organizations with more than 5,000 employees.

Kent Erickson is Alliance Strategist at Zenoss.

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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 ...

The Challenge of Managing Converged Infrastructure

Kent Erickson

While the use of converged infrastructure (CI) is becoming mainstream, the accompanying management tools are still a challenge, according to the third annual global State of Converged Infrastructure survey from Zenoss.

CI systems integrate servers, storage, network and virtualization components into one system that can be managed as a single unit rather than separate systems. With 84% of all respondents using or planning to deploy CI, the technology has reached mainstream status. Companies large and small are building data centers around these integrated systems because they reduce investment risk and offer a faster way to scale out IT infrastructure capacity.

"While converged infrastructure architectures are delivering real value as the foundational element behind the transformation already underway in enterprise data centers, these packaged offerings are lacking unified monitoring and analytics software," said Megan Lueders, VP of Marketing at Zenoss. "With more effective management tools available, there is a huge opportunity for IT organizations shops to dramatically increase their agility and IT effectiveness."

The 2015 CI survey provides many additional insights into data center modernization, including:

■ In companies with more than 5,000 employees, only 8% are not using or considering CI.

■ Despite being pervasively adopted, the survey results indicate management software is lagging behind, with 63% of respondents indicating they manage their CI deployments by repurposing existing management tools designed for traditional infrastructure.

■ Astonishingly, 25% of companies who have deployed CI are dealing with seven or more tools to manage it.

■ Key drivers for CI deployments include projects for big data, infrastructure as a service, unified communications, and custom application development.

Zenoss polled 410 IT professionals from across the world to determine how technology leaders are leveraging CI to respond to business needs, including 151 who have already adopted CI within their IT environments and another 194 who were currently considering or planning for it. 32% of these respondents come from organizations with more than 5,000 employees.

Kent Erickson is Alliance Strategist at Zenoss.

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 ...