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IT Monitoring Paradox: Let's Step Outside the Bubble!

David Hayward

The world is full of paradoxes. To solve them, you have to look at the facts in a different, even nonconventional way. You have to step outside your bubble.

One of the earliest paradoxes is from the ancient Greek thinker Heraclitus. It goes like this: "You cannot step into the same river twice." As Roy Sorenson says in A Brief History of the Paradox, "[Heraclitus] means that you cannot step twice into the same water of a river. There is one river, but many distinct bodies of water flow through it. Heraclitus urges a balance between experience and reason."

Paradoxes are fun to solve, but real-life they can be serious. IT Operations faces paradoxes too, and one in particular day in and day out. Recently, an IT Manager in a FORTUNE 1000 company — let's call him "Joe" — told me that he was called into the distribution center VP's office. The center was at a standstill. Joe showed him that the IT systems supporting the center were meeting all their Service Level Agreements: servers, applications, databases, storage, routers, switches — the whole lot. And the VP's response? "So what. I can’t ship anything."

That's when the light bulb went off in Joe's head. All the IT technologies that underlie the distribution center were running fine, but not the center itself. IT Operations needed another way to look at things, so he could understand the IT environment's status in terms of its impact on the business, not just in terms of how this or that technology silo was behaving.

Like Heraclitus and the river, Joe needed to strike a balance between experience and reason. Joe had plenty of experience — reams of performance monitoring data and proof of SLA compliance for each technology domain — but no way to reason, or monitor, the distribution center business process itself.

Joe started thinking of ITIL — the framework for orienting IT with services, not technologies, in mind. The trouble is, IT operates in a bubble. In fact, lots of bubbles: silo’d teams, silo’d tools, each separately monitoring servers, applications, storage, databases, routers, switches, etc.. No one was monitoring the big picture outside the bubbles. IT Operations Level 1 (the “first line of defense”) was looking at a sea of monitoring screens, events and alerts about technology devices and circuits, and had little or no understanding about how those events and alerts impacted specific business processes.

So even when IT was meeting SLA objectives in each silo, little degradations (i.e., incidents) across silos were adding up and impacting different services (i.e., processes and user experience) in different ways. This was undetectable because there wasn't any way to way to associate all those incidents with specific business services: no operational view and real-time IT operational analytics of business processes across silos.

This is typical. As an analyst from a leading IT research firm recently told me: "The Industry has been trying to solve this problem for decades. It sounds old, but we keep coming back to the same paradox over and over again."

Joe and others like him have embarked on a mission to transform IT Operations from a purely technology monitoring team to a business service reliability monitoring team. They are transforming operations because either they’ll crack the paradox of managing services that they deliver to their business, or the business will outsource operations to someone who can.

Transformation doesn't happen overnight. As the ITIL mantra teaches us, it takes "people, processes and technology" to get IT properly focused on the business and its services. To start, you need to step outside your bubble.

David Hayward is Senior Principal Manager, Solutions Marketing at CA Technologies.

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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 ...

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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 ...

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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.

IT Monitoring Paradox: Let's Step Outside the Bubble!

David Hayward

The world is full of paradoxes. To solve them, you have to look at the facts in a different, even nonconventional way. You have to step outside your bubble.

One of the earliest paradoxes is from the ancient Greek thinker Heraclitus. It goes like this: "You cannot step into the same river twice." As Roy Sorenson says in A Brief History of the Paradox, "[Heraclitus] means that you cannot step twice into the same water of a river. There is one river, but many distinct bodies of water flow through it. Heraclitus urges a balance between experience and reason."

Paradoxes are fun to solve, but real-life they can be serious. IT Operations faces paradoxes too, and one in particular day in and day out. Recently, an IT Manager in a FORTUNE 1000 company — let's call him "Joe" — told me that he was called into the distribution center VP's office. The center was at a standstill. Joe showed him that the IT systems supporting the center were meeting all their Service Level Agreements: servers, applications, databases, storage, routers, switches — the whole lot. And the VP's response? "So what. I can’t ship anything."

That's when the light bulb went off in Joe's head. All the IT technologies that underlie the distribution center were running fine, but not the center itself. IT Operations needed another way to look at things, so he could understand the IT environment's status in terms of its impact on the business, not just in terms of how this or that technology silo was behaving.

Like Heraclitus and the river, Joe needed to strike a balance between experience and reason. Joe had plenty of experience — reams of performance monitoring data and proof of SLA compliance for each technology domain — but no way to reason, or monitor, the distribution center business process itself.

Joe started thinking of ITIL — the framework for orienting IT with services, not technologies, in mind. The trouble is, IT operates in a bubble. In fact, lots of bubbles: silo’d teams, silo’d tools, each separately monitoring servers, applications, storage, databases, routers, switches, etc.. No one was monitoring the big picture outside the bubbles. IT Operations Level 1 (the “first line of defense”) was looking at a sea of monitoring screens, events and alerts about technology devices and circuits, and had little or no understanding about how those events and alerts impacted specific business processes.

So even when IT was meeting SLA objectives in each silo, little degradations (i.e., incidents) across silos were adding up and impacting different services (i.e., processes and user experience) in different ways. This was undetectable because there wasn't any way to way to associate all those incidents with specific business services: no operational view and real-time IT operational analytics of business processes across silos.

This is typical. As an analyst from a leading IT research firm recently told me: "The Industry has been trying to solve this problem for decades. It sounds old, but we keep coming back to the same paradox over and over again."

Joe and others like him have embarked on a mission to transform IT Operations from a purely technology monitoring team to a business service reliability monitoring team. They are transforming operations because either they’ll crack the paradox of managing services that they deliver to their business, or the business will outsource operations to someone who can.

Transformation doesn't happen overnight. As the ITIL mantra teaches us, it takes "people, processes and technology" to get IT properly focused on the business and its services. To start, you need to step outside your bubble.

David Hayward is Senior Principal Manager, Solutions Marketing at CA Technologies.

Hot Topics

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.