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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

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

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

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ...