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Organization and Process (Or Lack Thereof) in the Digital War Room

Dennis Drogseth

In my prior blog, I tried to paint a picture of some of the surprising (and not so surprising) highlights from our research on Unifying IT for Digital War Room Performance, which is also a webinar.

Start with Opening the Gates to the Digital War Room - What is it Now, and What is it Likely to Become?

One point to reinforce is that the digital war room — physical, virtual or hybrid — is not in retreat but in fact is growing in scope to include greater participation from development and security. It's also becoming more proactive, with on average more than 30% of "major incidents" before they impacted business service performance.

The reasons for this added (not diminished) level of relevance will be examined more in depth in my webinar on April 11th (and yes, there will be replays), but generally the answer lies in the fact that improved levels of team efficiency are critical to the future of IT, and the digital war room shines a spotlight on this evolving reality.

In this blog I'm providing a few additional highlights from the insights we got on digital war room organization and processes.

A Few Organizational Insights

One of the questions we asked was directed at finding out whether war rooms, as they evolve, were becoming more organizationally defined, or more sporadic and ad-hoc. The answer was solidly in the "more formalized" category (47%) versus the group with "more ad-hoc teams and processes" (28%). Another 22% indicated that their teams were already solidly formalized and established.

Then, when we evaluated success rates to this mix, we saw that those digital war rooms becoming "more formalized and established" were far more likely to align with digital war room effectiveness than the other groups.

Well defined teams that can be brought together across all domains provide a unique advantage over fragmented, technically isolated teams

If you think about this, it does suggest a contradiction to some of the trendier thinking endorsing multiple teams and more completely decentralized ways of working. But the logic for core consistency is clear. Well defined teams that can be brought together across all domains provide a unique advantage over fragmented, more technically isolated teams when confronting the full gamut of "major incident" possibilities.

And BTW, the average head count for these teams across small, medium and large was about 15. The implication being not that all 15 stakeholders are being activated for every single incident, but there are 15 individuals assigned and available for digital war room decision making on an on-going, as-needed basis. The trend, BTW, is toward growing not shrinking levels of involvement — in large part because of the accelerating need to include development and security professionals. The overall data also showed a significant role in digital war room decision making for non-IT, or business stakeholders.

Having a single organizational owner, also helps to drive war-room efficiencies. Interestingly, "Security/compliance" was in third place for war-room ownership after "ITSM" and the "executive suite." Having senior executive involvement helped, as well. The most prevalent was ongoing "director-level" involvement, but the most effective turned out to be "CIO-level" involvement.

Processes (or Lack Thereof)

In last week's blog, we enumerated the following critical processes that help to define war-room performance:

Initial awareness, which is usually driven by events or some other type of automated intelligence, or complaints to the service desk.

Response team engagement and coordination, bringing relevant stakeholders together and providing a context for them to work together.

Triage and diagnostics, where problems are understood in context and then detailed requirements for remediation can be defined.

Remediation, where active fixes to major incidents are made, often through change and configuration management procedures.

Validation, in which testing is done to ensure that actions for remediation were successful, ideally from a business impact as well as a purely technical perspective.

In the non-progressive category, we discovered that, based on our data, the average response indicated only a little more than half (2.57) of these processes were defined — a surprising revelation in a rather negative way. When we mapped "success rates" to the number of processes mapped out, however, we did get a reasonable correlation:

■ 3 for the extremely successful

■ 2.5 for the successful

■ 2 for the only marginally successful

The most prevalently defined process was response team coordination — which also turned out to be the most problematic or delay-causing process. In fact, identifying process with delay or problems mapped well to the processes that were most likely to be identified, suggesting that clarifying the reality of what's going on opens the door to realizing what's wrong and how improvements can be made.

Timing is Everything

Time to assemble an effective team, on average, was about 1.5 hours, which could be damaging when a serious outage occurred

We also asked about times associated with these processes. When we asked about the time to assemble an effective team, the average was about 1.5 hours, which could, of course, be meaningfully damaging when a serious outage occurred. When asked about total time to resolution, the average was about six hours, but 20 percent took more than eleven hours. Once again, as an average, this can be concerning for incidents with major business impacts.

These are again, just a few of many highlights from our research.

Don't forget to watch the webinar for a great many more insights.

Read my third and final blog on the digital war room: The Digital War Room in Changing Times: The Impacts of DevOps, Cloud and SecOps

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 ...

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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.

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 ...

Organization and Process (Or Lack Thereof) in the Digital War Room

Dennis Drogseth

In my prior blog, I tried to paint a picture of some of the surprising (and not so surprising) highlights from our research on Unifying IT for Digital War Room Performance, which is also a webinar.

Start with Opening the Gates to the Digital War Room - What is it Now, and What is it Likely to Become?

One point to reinforce is that the digital war room — physical, virtual or hybrid — is not in retreat but in fact is growing in scope to include greater participation from development and security. It's also becoming more proactive, with on average more than 30% of "major incidents" before they impacted business service performance.

The reasons for this added (not diminished) level of relevance will be examined more in depth in my webinar on April 11th (and yes, there will be replays), but generally the answer lies in the fact that improved levels of team efficiency are critical to the future of IT, and the digital war room shines a spotlight on this evolving reality.

In this blog I'm providing a few additional highlights from the insights we got on digital war room organization and processes.

A Few Organizational Insights

One of the questions we asked was directed at finding out whether war rooms, as they evolve, were becoming more organizationally defined, or more sporadic and ad-hoc. The answer was solidly in the "more formalized" category (47%) versus the group with "more ad-hoc teams and processes" (28%). Another 22% indicated that their teams were already solidly formalized and established.

Then, when we evaluated success rates to this mix, we saw that those digital war rooms becoming "more formalized and established" were far more likely to align with digital war room effectiveness than the other groups.

Well defined teams that can be brought together across all domains provide a unique advantage over fragmented, technically isolated teams

If you think about this, it does suggest a contradiction to some of the trendier thinking endorsing multiple teams and more completely decentralized ways of working. But the logic for core consistency is clear. Well defined teams that can be brought together across all domains provide a unique advantage over fragmented, more technically isolated teams when confronting the full gamut of "major incident" possibilities.

And BTW, the average head count for these teams across small, medium and large was about 15. The implication being not that all 15 stakeholders are being activated for every single incident, but there are 15 individuals assigned and available for digital war room decision making on an on-going, as-needed basis. The trend, BTW, is toward growing not shrinking levels of involvement — in large part because of the accelerating need to include development and security professionals. The overall data also showed a significant role in digital war room decision making for non-IT, or business stakeholders.

Having a single organizational owner, also helps to drive war-room efficiencies. Interestingly, "Security/compliance" was in third place for war-room ownership after "ITSM" and the "executive suite." Having senior executive involvement helped, as well. The most prevalent was ongoing "director-level" involvement, but the most effective turned out to be "CIO-level" involvement.

Processes (or Lack Thereof)

In last week's blog, we enumerated the following critical processes that help to define war-room performance:

Initial awareness, which is usually driven by events or some other type of automated intelligence, or complaints to the service desk.

Response team engagement and coordination, bringing relevant stakeholders together and providing a context for them to work together.

Triage and diagnostics, where problems are understood in context and then detailed requirements for remediation can be defined.

Remediation, where active fixes to major incidents are made, often through change and configuration management procedures.

Validation, in which testing is done to ensure that actions for remediation were successful, ideally from a business impact as well as a purely technical perspective.

In the non-progressive category, we discovered that, based on our data, the average response indicated only a little more than half (2.57) of these processes were defined — a surprising revelation in a rather negative way. When we mapped "success rates" to the number of processes mapped out, however, we did get a reasonable correlation:

■ 3 for the extremely successful

■ 2.5 for the successful

■ 2 for the only marginally successful

The most prevalently defined process was response team coordination — which also turned out to be the most problematic or delay-causing process. In fact, identifying process with delay or problems mapped well to the processes that were most likely to be identified, suggesting that clarifying the reality of what's going on opens the door to realizing what's wrong and how improvements can be made.

Timing is Everything

Time to assemble an effective team, on average, was about 1.5 hours, which could be damaging when a serious outage occurred

We also asked about times associated with these processes. When we asked about the time to assemble an effective team, the average was about 1.5 hours, which could, of course, be meaningfully damaging when a serious outage occurred. When asked about total time to resolution, the average was about six hours, but 20 percent took more than eleven hours. Once again, as an average, this can be concerning for incidents with major business impacts.

These are again, just a few of many highlights from our research.

Don't forget to watch the webinar for a great many more insights.

Read my third and final blog on the digital war room: The Digital War Room in Changing Times: The Impacts of DevOps, Cloud and SecOps

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 ...