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How to Speed Up Incidents with a Lot of Cooks in the Kitchen

Anirban Chatterjee

In today's complex, dynamic IT environments, the proliferation of disparate IT Ops, NOC, DevOps, and SRE teams and tools is a given — and usually considered a necessity. This leads to the inevitable truth that when an incident happens, often the biggest challenge is collaborating between these teams to understand what happened and resolve the issue. Inefficiencies suffered during this critical stage can have huge impacts on how much each incident costs the business.

I recently sat down (virtually) with Sid Roy, VP of Client Services at Scicom, to get a deeper understanding of how IT leaders can more effectively size up these inefficiencies and eliminate them.

The Cost of IT Incidents

When asked what a minute of downtime costs, analysts and vendors may provide different answers — but they are more or less aligned around the same order of magnitude — several thousands of dollars per minute. And with an average of 5 major incidents a month, at an average time of 6 hours for resolution — this easily amounts to millions of dollars a year.


The three key drivers of these costs are:

Staffing and team member costs: It includes FTEs, consultants, and overhead — when other teams are pulled in to deal with the incident. For many organizations, this can include offshore incident response teams.

The direct and indirect costs of an IT incident: This includes your infrastructure or capital expenditures like software licenses for monitoring, log and event management, notification, ticketing, collaboration, etc.

The business impact of an IT incident: This is one of the most challenging and unpredictable variable costs to calculate or manage, and is often the highest of all three drivers. It includes revenue loss/delay or reduction due to a major incident and the profit or loss due to brand or goodwill impact. It also includes inefficiencies suffered by other parts of the business when critical services they depend on are degraded or unavailable.

Fragmented Teams Magnify the Challenge

The incident volume, complexity, and throughput obviously affect the number of people and time needed to deal with them and often drive more indirect costs as needed resources pile up. To save on these millions of dollars of costs, you need to be able to collaborate and lower MTTR. As mentioned above, this becomes a challenge in agile IT environments.

To help streamline operations, teams need to start asking and answering several key questions:

■ Do you have an up-to-date map of your critical services?

■ Are they prioritized by business criticality (revenue, number of customers, other supported services in the supply chain)?

■ What are the upstream and downstream dependencies of these applications?

■ Have you identified the major infrastructure and application elements in your environment?

■ Are you aligned with the owners of these systems?

■ Do you have real-time knowledge of changes being done to the infrastructure and applications?

■ Do you have monitoring gaps?

■ Which monitoring tools provide you with the best value?

Answering these questions involves overcoming fragmentation across teams of people, processes, and tools — essentially integrating ITSM and ITOM to enjoy the benefits of contextual full-stack visibility and streamlined processes within the organization.


The Right Combination

What is the right combination of people, processes, and tools we just discussed? Here are the two main guidelines:

Set up a major incident management team- to optimally benefit from your existing staff.

This team includes three vital roles:

- The incident manager/incident response commander. A designated role in charge of declaring a major incident and taking ownership of it. Their job is to essentially stop the bleeding of revenue and costs.

- The NOC/monitoring team. This is your front line of defense. When things go bump in the night or boom in the day, they're the ones picking it up with their “eyes on the glass” — 24/7. And they're in charge of reporting and creating full situational awareness for the incident command through bidirectional communications.

- The production support. The team that actually effects the required changes and executes the remediating action.


Deploy event correlation and automation tools to enable the incident management team.

These tools are key, allowing your team to do all the above.

First, correlate the alerts your monitoring and observability tools create into a drastically reduced number of high-level, insight-rich incidents by using Machine Learning and AI. Add context to these incidents by ingesting and understanding topology sources as well. This creates the needed full-stack visibility and situational awareness.

Then use ML and AI to determine the root cause of these incidents, including correlating them with data streams from your change tools: CI/CD, orchestration, change management, and auditing — to identify whether any changes were done in your environment are causing these incidents.

Finally — automate as many manual processes as you can to free your IT Ops team from time-consuming tasks. By integrating with collaboration tools — you can also enable the above-mentioned bi-directional communications.

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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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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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

How to Speed Up Incidents with a Lot of Cooks in the Kitchen

Anirban Chatterjee

In today's complex, dynamic IT environments, the proliferation of disparate IT Ops, NOC, DevOps, and SRE teams and tools is a given — and usually considered a necessity. This leads to the inevitable truth that when an incident happens, often the biggest challenge is collaborating between these teams to understand what happened and resolve the issue. Inefficiencies suffered during this critical stage can have huge impacts on how much each incident costs the business.

I recently sat down (virtually) with Sid Roy, VP of Client Services at Scicom, to get a deeper understanding of how IT leaders can more effectively size up these inefficiencies and eliminate them.

The Cost of IT Incidents

When asked what a minute of downtime costs, analysts and vendors may provide different answers — but they are more or less aligned around the same order of magnitude — several thousands of dollars per minute. And with an average of 5 major incidents a month, at an average time of 6 hours for resolution — this easily amounts to millions of dollars a year.


The three key drivers of these costs are:

Staffing and team member costs: It includes FTEs, consultants, and overhead — when other teams are pulled in to deal with the incident. For many organizations, this can include offshore incident response teams.

The direct and indirect costs of an IT incident: This includes your infrastructure or capital expenditures like software licenses for monitoring, log and event management, notification, ticketing, collaboration, etc.

The business impact of an IT incident: This is one of the most challenging and unpredictable variable costs to calculate or manage, and is often the highest of all three drivers. It includes revenue loss/delay or reduction due to a major incident and the profit or loss due to brand or goodwill impact. It also includes inefficiencies suffered by other parts of the business when critical services they depend on are degraded or unavailable.

Fragmented Teams Magnify the Challenge

The incident volume, complexity, and throughput obviously affect the number of people and time needed to deal with them and often drive more indirect costs as needed resources pile up. To save on these millions of dollars of costs, you need to be able to collaborate and lower MTTR. As mentioned above, this becomes a challenge in agile IT environments.

To help streamline operations, teams need to start asking and answering several key questions:

■ Do you have an up-to-date map of your critical services?

■ Are they prioritized by business criticality (revenue, number of customers, other supported services in the supply chain)?

■ What are the upstream and downstream dependencies of these applications?

■ Have you identified the major infrastructure and application elements in your environment?

■ Are you aligned with the owners of these systems?

■ Do you have real-time knowledge of changes being done to the infrastructure and applications?

■ Do you have monitoring gaps?

■ Which monitoring tools provide you with the best value?

Answering these questions involves overcoming fragmentation across teams of people, processes, and tools — essentially integrating ITSM and ITOM to enjoy the benefits of contextual full-stack visibility and streamlined processes within the organization.


The Right Combination

What is the right combination of people, processes, and tools we just discussed? Here are the two main guidelines:

Set up a major incident management team- to optimally benefit from your existing staff.

This team includes three vital roles:

- The incident manager/incident response commander. A designated role in charge of declaring a major incident and taking ownership of it. Their job is to essentially stop the bleeding of revenue and costs.

- The NOC/monitoring team. This is your front line of defense. When things go bump in the night or boom in the day, they're the ones picking it up with their “eyes on the glass” — 24/7. And they're in charge of reporting and creating full situational awareness for the incident command through bidirectional communications.

- The production support. The team that actually effects the required changes and executes the remediating action.


Deploy event correlation and automation tools to enable the incident management team.

These tools are key, allowing your team to do all the above.

First, correlate the alerts your monitoring and observability tools create into a drastically reduced number of high-level, insight-rich incidents by using Machine Learning and AI. Add context to these incidents by ingesting and understanding topology sources as well. This creates the needed full-stack visibility and situational awareness.

Then use ML and AI to determine the root cause of these incidents, including correlating them with data streams from your change tools: CI/CD, orchestration, change management, and auditing — to identify whether any changes were done in your environment are causing these incidents.

Finally — automate as many manual processes as you can to free your IT Ops team from time-consuming tasks. By integrating with collaboration tools — you can also enable the above-mentioned bi-directional communications.

Hot Topics

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...