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Businesses Are Double-Invested in Monitoring – and Leaders Don't See It

Phil Tee

Our digital economy is intolerant of downtime. But consumers haven't just come to expect always-on digital apps and services. They also expect continuous innovation, new functionality and lightening fast response times.


Organizations have taken note, investing heavily in teams and tools that supposedly increase uptime and free resources for innovation. But leaders have not realized this "throw money at the problem" approach to monitoring is burning through resources without much improvement in availability outcomes.

The Moogsoft State of Availability Report — which helps engineering teams and leaders uncover insights about availability KPIs, teams and tools — found that businesses are double-investing in monitoring. Organizations spend too much money on too many tools, and teams spend the majority of their days monitoring their monitoring tools.

This over-investment in incident management goes largely unnoticed by management. So does the fact that monitoring cycles siphon resources from the future-driven work that delights customers and keeps engineers engaged.

We identify a few common causes of the spend for less approach here:

1. Sprawling single-domain monitoring tools

In a noble attempt to keep digital apps and services available to end users at all times, business leaders buy tools that monitor their increasingly large and complex IT infrastructures. In theory, these tools should speed fixes to performance-affecting issues by continuously scanning systems and notifying engineers about anomalies.

The problem is: Teams have far too many tools. On average, engineers manage 16 monitoring tools. And that number can creep up to 40 as SLAs increase. Sprawling tools like this are unwieldy and license, management and maintenance overheads are expensive. But the over-investment in monitoring doesn't stop there.

2. Days spend in monitoring cycles

IT monitoring tools should bear the brunt of monitoring itself. In principle, these tools relieve engineers from spending too much time on a fairly tedious task and enable them to deliver what customers want: bigger and better technology.

Unfortunately, teams spend by far the most time monitoring over any other task. Why? Engineers spin their wheels managing single-domain tools that are not integrated cross stack. and produce huge volumes of largely useless data. Teams facing a critical outage or incident waste valuable time investigating data from disparate tools and connecting the dots themselves.

3. Leadership-team misalignment

Business leaders do not see just how much time their teams spend on monitoring, and likely believe they're making sound monitoring investments. Leaders believe their teams spend about the same amount of their time on monitoring as they do on other daily (and often future-driven) responsibilities like automation, cloud transformation and development.

4. Stalling innovation and experimentation

With engineering teams stuck in monitoring cycles, something has to give. And unfortunately, that thing is innovation and experimentation — the very activities that delight customers and engage engineering teams. In other words, not only do organizations over-invest in monitoring, they do so to the detriment of customer experience improvements.

The solution: steps to tech stability

If you are part of an engineering team or team leader, chances are you're facing modern-day monitoring problems. Consider these best practices for breaking wasteful monitoring cycles and building your tech stability:

1. Baseline your tools. Audit your existing tools, understand their utilization and what they cost. Then, you can determine which of these assets advance availability goals and which just create more noise.

2. Consolidate your tools. Hold on to only those monitoring tools that provide value. Otherwise, try to shrink your monitoring tools' footprint to decrease total cost of ownership (TCO) and reduce noise.

3. Implement an artificial intelligence for IT Operations (AIOps) solution. Make your next monitoring investment one that makes engineer's jobs less toilsome, not more. AIOps connects cloud and on-prem monitoring tools, giving engineers a central system of engagement for all monitoring activities. The platform alerts engineers to data anomalies and their root cause and automates the entire incident lifecycle.

4. Pay down your technical debt. With time back on your side, tackle the most relevant tech debt and increase system stability. Free even more time by automating away toil and continue to increase availability with chaos engineering.

5. Invest in the future. With time and money saved, refocus your investments on company-differentiating initiatives.

Monitoring tools are essential to uptime. But monitoring cannot be the only thing teams do — especially when it hinders innovation and experimentation. Leaders must make more informed investments to monitor more effectively. Only then can organizations move from maintaining the customer experience to innovating the customer experience.

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Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

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Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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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 ...

Businesses Are Double-Invested in Monitoring – and Leaders Don't See It

Phil Tee

Our digital economy is intolerant of downtime. But consumers haven't just come to expect always-on digital apps and services. They also expect continuous innovation, new functionality and lightening fast response times.


Organizations have taken note, investing heavily in teams and tools that supposedly increase uptime and free resources for innovation. But leaders have not realized this "throw money at the problem" approach to monitoring is burning through resources without much improvement in availability outcomes.

The Moogsoft State of Availability Report — which helps engineering teams and leaders uncover insights about availability KPIs, teams and tools — found that businesses are double-investing in monitoring. Organizations spend too much money on too many tools, and teams spend the majority of their days monitoring their monitoring tools.

This over-investment in incident management goes largely unnoticed by management. So does the fact that monitoring cycles siphon resources from the future-driven work that delights customers and keeps engineers engaged.

We identify a few common causes of the spend for less approach here:

1. Sprawling single-domain monitoring tools

In a noble attempt to keep digital apps and services available to end users at all times, business leaders buy tools that monitor their increasingly large and complex IT infrastructures. In theory, these tools should speed fixes to performance-affecting issues by continuously scanning systems and notifying engineers about anomalies.

The problem is: Teams have far too many tools. On average, engineers manage 16 monitoring tools. And that number can creep up to 40 as SLAs increase. Sprawling tools like this are unwieldy and license, management and maintenance overheads are expensive. But the over-investment in monitoring doesn't stop there.

2. Days spend in monitoring cycles

IT monitoring tools should bear the brunt of monitoring itself. In principle, these tools relieve engineers from spending too much time on a fairly tedious task and enable them to deliver what customers want: bigger and better technology.

Unfortunately, teams spend by far the most time monitoring over any other task. Why? Engineers spin their wheels managing single-domain tools that are not integrated cross stack. and produce huge volumes of largely useless data. Teams facing a critical outage or incident waste valuable time investigating data from disparate tools and connecting the dots themselves.

3. Leadership-team misalignment

Business leaders do not see just how much time their teams spend on monitoring, and likely believe they're making sound monitoring investments. Leaders believe their teams spend about the same amount of their time on monitoring as they do on other daily (and often future-driven) responsibilities like automation, cloud transformation and development.

4. Stalling innovation and experimentation

With engineering teams stuck in monitoring cycles, something has to give. And unfortunately, that thing is innovation and experimentation — the very activities that delight customers and engage engineering teams. In other words, not only do organizations over-invest in monitoring, they do so to the detriment of customer experience improvements.

The solution: steps to tech stability

If you are part of an engineering team or team leader, chances are you're facing modern-day monitoring problems. Consider these best practices for breaking wasteful monitoring cycles and building your tech stability:

1. Baseline your tools. Audit your existing tools, understand their utilization and what they cost. Then, you can determine which of these assets advance availability goals and which just create more noise.

2. Consolidate your tools. Hold on to only those monitoring tools that provide value. Otherwise, try to shrink your monitoring tools' footprint to decrease total cost of ownership (TCO) and reduce noise.

3. Implement an artificial intelligence for IT Operations (AIOps) solution. Make your next monitoring investment one that makes engineer's jobs less toilsome, not more. AIOps connects cloud and on-prem monitoring tools, giving engineers a central system of engagement for all monitoring activities. The platform alerts engineers to data anomalies and their root cause and automates the entire incident lifecycle.

4. Pay down your technical debt. With time back on your side, tackle the most relevant tech debt and increase system stability. Free even more time by automating away toil and continue to increase availability with chaos engineering.

5. Invest in the future. With time and money saved, refocus your investments on company-differentiating initiatives.

Monitoring tools are essential to uptime. But monitoring cannot be the only thing teams do — especially when it hinders innovation and experimentation. Leaders must make more informed investments to monitor more effectively. Only then can organizations move from maintaining the customer experience to innovating the customer experience.

Hot Topics

The Latest

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...