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Diving Into the True Costs of IT Outages

Adam Blau
BigPanda

There are two words that strike fear in every IT professional: "unplanned outage." These come with a steep price tag: A recent report, The Modern IT Outage: Costs, Causes and Cures, found that downtime due to unplanned outages costs businesses $12,900 per minute. Breaking that statistic down further, the report revealed significant differences among companies of different sizes relating to downtime. For example, the outage cost per minute in an organization of 1,000 to 2,500 employees is $1,850, while the outage cost per minute in a larger company of 20,000 employees is $25,402 on average.

These statistics blow away the outdated yet often-quoted statistic that an average minute of downtime costs $5,600 because, as it turns out, this information from 2014 hasn't been adjusted to reflect the real and nuanced costs of a modern IT outage. Here, we dive deeper into this recent research so ITOps organizations can gain a better understanding of downtime's impact, causes and remedies.

Cost Factors and Causes

While we tend to think lost revenue is the biggest cost casualty of an outage, "The Modern IT Outage: Costs, Causes and ‘Cures'" found that that simply isn't the case. In fact, "business disruption" and "impact on employee activity" tie for the top spot, while "lost revenue" was tied for third, along with "data breach" and "governance regulatory exposure." Also on the list are "reputation" and "hit to DevOps/SRE productivity." These came in fourth and fifth, respectively.


A full 41% of organizations suffer an outage at least monthly, a significant number. And these outages take an hour to repair on average.


As for the outage causes, the report found important differences among organizations in two categories: those that have enterprise-wide, mature artificial intelligence for IT operations (AIOps) and those that are implementing AIOps on a departmental basis. The organizations in the first category had tamed unplanned outages for the most part and only struggled with external factors such as power outages or internet provider failure that are outside the organization's control. Those in the second category primarily suffered change and configuration issues and human error — factors that are very much in the organization's control — and therefore ready for being mitigated with the power of AI and automation.


AIOps and the Road to Better Outcomes

IT leaders' thoughts on how the future looks relative to outage costs aren't optimistic. In fact, for some, the mood is downright fatalistic, with 36% believing that increased outage costs are guaranteed.

Still, some are hopeful, and AIOps plays a part here: The survey found that 22% of respondents say that rising costs are avoidable and they plan to use AIOps and automation to stem them. Another 13% of respondents reported that proactive systems have allowed them to actually decrease outage costs. With this proof of AIOps' success, the more pessimistic IT leaders can keep their chins up.

There is no way to apply a surefire "cure" for IT outages in any organization, but AIOps and automation come close. Not only do they help minimize the costs and impact of outages, but they also are proven to reduce the number of outages, improve business process efficiencies, decrease war-room frequency, and much, much more. With AIOps and automation in their arsenal, IT professionals can rest a little easier knowing they have powerful weapons to use in their battle against the dreaded unplanned outage downtime.

Adam Blau is Senior Director of Product Marketing at BigPanda

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

Diving Into the True Costs of IT Outages

Adam Blau
BigPanda

There are two words that strike fear in every IT professional: "unplanned outage." These come with a steep price tag: A recent report, The Modern IT Outage: Costs, Causes and Cures, found that downtime due to unplanned outages costs businesses $12,900 per minute. Breaking that statistic down further, the report revealed significant differences among companies of different sizes relating to downtime. For example, the outage cost per minute in an organization of 1,000 to 2,500 employees is $1,850, while the outage cost per minute in a larger company of 20,000 employees is $25,402 on average.

These statistics blow away the outdated yet often-quoted statistic that an average minute of downtime costs $5,600 because, as it turns out, this information from 2014 hasn't been adjusted to reflect the real and nuanced costs of a modern IT outage. Here, we dive deeper into this recent research so ITOps organizations can gain a better understanding of downtime's impact, causes and remedies.

Cost Factors and Causes

While we tend to think lost revenue is the biggest cost casualty of an outage, "The Modern IT Outage: Costs, Causes and ‘Cures'" found that that simply isn't the case. In fact, "business disruption" and "impact on employee activity" tie for the top spot, while "lost revenue" was tied for third, along with "data breach" and "governance regulatory exposure." Also on the list are "reputation" and "hit to DevOps/SRE productivity." These came in fourth and fifth, respectively.


A full 41% of organizations suffer an outage at least monthly, a significant number. And these outages take an hour to repair on average.


As for the outage causes, the report found important differences among organizations in two categories: those that have enterprise-wide, mature artificial intelligence for IT operations (AIOps) and those that are implementing AIOps on a departmental basis. The organizations in the first category had tamed unplanned outages for the most part and only struggled with external factors such as power outages or internet provider failure that are outside the organization's control. Those in the second category primarily suffered change and configuration issues and human error — factors that are very much in the organization's control — and therefore ready for being mitigated with the power of AI and automation.


AIOps and the Road to Better Outcomes

IT leaders' thoughts on how the future looks relative to outage costs aren't optimistic. In fact, for some, the mood is downright fatalistic, with 36% believing that increased outage costs are guaranteed.

Still, some are hopeful, and AIOps plays a part here: The survey found that 22% of respondents say that rising costs are avoidable and they plan to use AIOps and automation to stem them. Another 13% of respondents reported that proactive systems have allowed them to actually decrease outage costs. With this proof of AIOps' success, the more pessimistic IT leaders can keep their chins up.

There is no way to apply a surefire "cure" for IT outages in any organization, but AIOps and automation come close. Not only do they help minimize the costs and impact of outages, but they also are proven to reduce the number of outages, improve business process efficiencies, decrease war-room frequency, and much, much more. With AIOps and automation in their arsenal, IT professionals can rest a little easier knowing they have powerful weapons to use in their battle against the dreaded unplanned outage downtime.

Adam Blau is Senior Director of Product Marketing at BigPanda

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...