Skip to main content

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

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

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

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...