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How Much Does Your IT Operations Really Cost?

Mohan Kompella

With the complex, dynamic nature of today's IT stack and the operational processes that support it, IT operations teams are finding they need to constantly grow their resources to manage all the moving pieces. This can get expensive … but how much are they spending?


The answer is often surprising. Complexity has made it hard to quantify how much excess resources are being wasted on simply dealing with new processes and challenges that relate to growth. Sorting through noise, filtering the signals that matter, recognizing and troubleshooting, sharing with distributed teams — all of these processes become more complex as organizations grow and environments modernize. AIOps solutions can help recoup some of these wasted resources — but how much? To understand the true cost of IT operations and the value AIOps can provide them, it helps to deep-dive into how key roles and processes in IT organizations have transformed, and how these changes are impacting the way IT operations teams need to operate.

Business Value Assessment

The key to understanding the actual cost of your IT operations lies in assessing the impact of several core metrics on your performance and processes. Along the way, you also identify where AIOps improvements can make the biggest difference and determine the actual financial value of an AIOps adoption project.

These are detailed in the following image:


■ Major incidents — their volume and MTTR help quantify your average service downtime — which basically means your Operational Efficiency.

■ Minor incidents — their volume, MTTR, and time spent on handling them — all amount to your Operational Performance in man-hours.

■ Incident management processes — determining the amount of time you spend on each of your incident management life cycle phases allows you to understand where the most improvement is needed.

■ The maturity of your tools and processes — allows you to identify how much you will need to invest in improvement through AIOps adoption, and how much value can be achieved.

■ Your headcount — identifying exactly how many people are involved in your IT operations, directly and indirectly, helps close the loop on Opex.

Closing the Gap: AIOps to the Rescue

AIOps de-risks digital transformation initiatives by allowing IT operations teams to handle the data and complexity that these transformations bring to the table. It does so by providing IT Ops with several capabilities detailed in the following illustration:


What are the quantitative values of AIOps?

■ AIOps gets rid of the noise. Whether it's multiple alerts stemming from the same problem, or a change that caused an alert storm, AIOps identifies and eliminates that noise before IT Ops spends time on it. Correlation, maintenance-based alert squelching both equate to fewer incidents. Typically, 50% or more of incidents are non-actionable noise.

■ AIOps helps quickly diagnose and identify the root cause of an incident. That means teams can start remediating sooner and with more certainty.

■ AIOps provides automation. That means everything from a unified ops console to automated incident workflow to auto-triggering of remediation actions. Overall, it means speed and accuracy for every incident dealt with or lower MTTR.

■ These benefits enable organizations to reclaim engineering time and put it to use on transformation initiatives. These also mean improvements to Service Availability.

Once you assess the actual costs of your IT operations and calculate the quantitative values AIOps can bring you — you can make an educated decision on where and how to improve.

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 ...

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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.

How Much Does Your IT Operations Really Cost?

Mohan Kompella

With the complex, dynamic nature of today's IT stack and the operational processes that support it, IT operations teams are finding they need to constantly grow their resources to manage all the moving pieces. This can get expensive … but how much are they spending?


The answer is often surprising. Complexity has made it hard to quantify how much excess resources are being wasted on simply dealing with new processes and challenges that relate to growth. Sorting through noise, filtering the signals that matter, recognizing and troubleshooting, sharing with distributed teams — all of these processes become more complex as organizations grow and environments modernize. AIOps solutions can help recoup some of these wasted resources — but how much? To understand the true cost of IT operations and the value AIOps can provide them, it helps to deep-dive into how key roles and processes in IT organizations have transformed, and how these changes are impacting the way IT operations teams need to operate.

Business Value Assessment

The key to understanding the actual cost of your IT operations lies in assessing the impact of several core metrics on your performance and processes. Along the way, you also identify where AIOps improvements can make the biggest difference and determine the actual financial value of an AIOps adoption project.

These are detailed in the following image:


■ Major incidents — their volume and MTTR help quantify your average service downtime — which basically means your Operational Efficiency.

■ Minor incidents — their volume, MTTR, and time spent on handling them — all amount to your Operational Performance in man-hours.

■ Incident management processes — determining the amount of time you spend on each of your incident management life cycle phases allows you to understand where the most improvement is needed.

■ The maturity of your tools and processes — allows you to identify how much you will need to invest in improvement through AIOps adoption, and how much value can be achieved.

■ Your headcount — identifying exactly how many people are involved in your IT operations, directly and indirectly, helps close the loop on Opex.

Closing the Gap: AIOps to the Rescue

AIOps de-risks digital transformation initiatives by allowing IT operations teams to handle the data and complexity that these transformations bring to the table. It does so by providing IT Ops with several capabilities detailed in the following illustration:


What are the quantitative values of AIOps?

■ AIOps gets rid of the noise. Whether it's multiple alerts stemming from the same problem, or a change that caused an alert storm, AIOps identifies and eliminates that noise before IT Ops spends time on it. Correlation, maintenance-based alert squelching both equate to fewer incidents. Typically, 50% or more of incidents are non-actionable noise.

■ AIOps helps quickly diagnose and identify the root cause of an incident. That means teams can start remediating sooner and with more certainty.

■ AIOps provides automation. That means everything from a unified ops console to automated incident workflow to auto-triggering of remediation actions. Overall, it means speed and accuracy for every incident dealt with or lower MTTR.

■ These benefits enable organizations to reclaim engineering time and put it to use on transformation initiatives. These also mean improvements to Service Availability.

Once you assess the actual costs of your IT operations and calculate the quantitative values AIOps can bring you — you can make an educated decision on where and how to improve.

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.