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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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.

Related Links

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Manufacturing Organizations Doubled AI Investment Yet Only 37% Fully Prepared to Operationalize AI

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Optimizing Decisions with Edge-First Cognitive Intelligence

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

The New Perimeter Is the User: Why Identity Is the Real Network Edge

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

When AI Infrastructure Stops Behaving Like Infrastructure

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Enterprise Resilience: Understanding the Shift from Static to Dynamic

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile Users Expect Perfection in 2026

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

AI Confidence Is High. Readiness Is Not. What the Data Tells Us - and Why It Matters

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

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Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

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AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

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