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The New Normal for IT Ops Deepens Need for AI - Part 1

Will Cappelli
Moogsoft

The global pandemic has radically changed how enterprise IT services are consumed, both in the short and long term. Here's how AIOps can help IT Ops teams.

The current crisis has upended all aspects of our personal and work lives, and IT Ops pros aren't the exception. The abrupt shift to remote work has created unprecedented challenges for IT Ops teams, while increasing pressure on them to prevent outages and provide service assurance.

Specifically, new consumption patterns of enterprise IT services have put stress on systems, architectures and topologies at all stack layers. In response, IT Ops teams must rapidly implement structural and management changes to address both temporary and permanent shifts.

In this turmoil, AIOps has emerged as a lifeline. By streamlining and automating IT operations, AIOps helps IT leaders collaborate remotely and act quickly and precisely to maintain business-critical digital services — during the pandemic and beyond.

Let's look in more detail at these challenges and at how AIOps can help IT Ops teams cope and succeed.

AIOps: A Definition

An AIOps solution must have these five types of algorithms that fully automate and streamline five key dimensions of IT operations monitoring:

■ Data selection: Identifying and surfacing the most relevant information.

■ Pattern discovery: Correlating and finding relationships between events across your tool stack.

■ Inference: Identifying root causes and recurring issues.

■ Collaboration: Notifying appropriate operators, and facilitating collaboration.

■ Automation: Automating remediation

In a real world setting, an AIOps solution ingests heterogeneous data from many different sources. Using entropy algorithms, it removes noise and duplication, and selects only the truly relevant data. It then groups and correlates this relevant information using various criteria, like text, time and topology.

Next, it discovers patterns in the data, and infers which data items signify causes, and which signify events. It then communicates the result of that analysis to a collaborative environment, which will support automated responses to what has been discovered.

As such, an AIOps solution plays the role of organizing and integrating what an organization's domain-specific IT monitoring and management tools do, intelligently integrating the stack's functionalities. AIOps should act as the brain that brings together these tools, and becomes a coordinating, central layer.

Transitioning to the New Normal

As the workforce shifts to remote work, user behaviors will change and different elements of the IT infrastructure, both in-house and publicly sourced, will be stressed. This will result in new, quickly-evolving types of incidents and outages. With AIOps, IT Ops teams can detect and analyze genuinely novel anomalies which can cause incidents and outages rapidly and stealthily.

Cross-regional and intra-regional team collaboration among IT operations and NOC organizations will need to be reinforced virtually as the implicit supports derived from physical co-presence are removed. AIOps can enable and guide virtual collaborative observation, analysis and response efforts, helping IT Ops teams collaborate and communicate despite being physically dispersed.

Sharp and unpredictable levels of staff reduction due to illness and self-isolation will force IT operations and NOC organizations to "do more with less" on both the side of signal observation and the side of signal response. Here again AIOps can help IT Ops teams to respond by both dynamically filtering noisy alert streams, and integrating and automating platforms that support various aspects of incident and problem management.

Go to The New Normal for IT Ops Deepens Need for AI - Part 2

Will Cappelli is Field CTO at Moogsoft

The Latest

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 ... 

Virtual Private Networks became a cornerstone of enterprise security at a time when corporate infrastructure looked very different from today ... For years, this model worked well. But the architecture behind VPNs assumed a centralized corporate environment—one where the network itself was the hub of activity. In a cloud — first world, that assumption no longer holds ...

Website outages get resolved just as fast in August as they do in November. I went looking for the opposite: the summer slowdown everyone assumes is there once the people who fix things are away. It isn't in the data we collected, covering 1.8 million confirmed outages across tens of thousands of websites ...

This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...

The New Normal for IT Ops Deepens Need for AI - Part 1

Will Cappelli
Moogsoft

The global pandemic has radically changed how enterprise IT services are consumed, both in the short and long term. Here's how AIOps can help IT Ops teams.

The current crisis has upended all aspects of our personal and work lives, and IT Ops pros aren't the exception. The abrupt shift to remote work has created unprecedented challenges for IT Ops teams, while increasing pressure on them to prevent outages and provide service assurance.

Specifically, new consumption patterns of enterprise IT services have put stress on systems, architectures and topologies at all stack layers. In response, IT Ops teams must rapidly implement structural and management changes to address both temporary and permanent shifts.

In this turmoil, AIOps has emerged as a lifeline. By streamlining and automating IT operations, AIOps helps IT leaders collaborate remotely and act quickly and precisely to maintain business-critical digital services — during the pandemic and beyond.

Let's look in more detail at these challenges and at how AIOps can help IT Ops teams cope and succeed.

AIOps: A Definition

An AIOps solution must have these five types of algorithms that fully automate and streamline five key dimensions of IT operations monitoring:

■ Data selection: Identifying and surfacing the most relevant information.

■ Pattern discovery: Correlating and finding relationships between events across your tool stack.

■ Inference: Identifying root causes and recurring issues.

■ Collaboration: Notifying appropriate operators, and facilitating collaboration.

■ Automation: Automating remediation

In a real world setting, an AIOps solution ingests heterogeneous data from many different sources. Using entropy algorithms, it removes noise and duplication, and selects only the truly relevant data. It then groups and correlates this relevant information using various criteria, like text, time and topology.

Next, it discovers patterns in the data, and infers which data items signify causes, and which signify events. It then communicates the result of that analysis to a collaborative environment, which will support automated responses to what has been discovered.

As such, an AIOps solution plays the role of organizing and integrating what an organization's domain-specific IT monitoring and management tools do, intelligently integrating the stack's functionalities. AIOps should act as the brain that brings together these tools, and becomes a coordinating, central layer.

Transitioning to the New Normal

As the workforce shifts to remote work, user behaviors will change and different elements of the IT infrastructure, both in-house and publicly sourced, will be stressed. This will result in new, quickly-evolving types of incidents and outages. With AIOps, IT Ops teams can detect and analyze genuinely novel anomalies which can cause incidents and outages rapidly and stealthily.

Cross-regional and intra-regional team collaboration among IT operations and NOC organizations will need to be reinforced virtually as the implicit supports derived from physical co-presence are removed. AIOps can enable and guide virtual collaborative observation, analysis and response efforts, helping IT Ops teams collaborate and communicate despite being physically dispersed.

Sharp and unpredictable levels of staff reduction due to illness and self-isolation will force IT operations and NOC organizations to "do more with less" on both the side of signal observation and the side of signal response. Here again AIOps can help IT Ops teams to respond by both dynamically filtering noisy alert streams, and integrating and automating platforms that support various aspects of incident and problem management.

Go to The New Normal for IT Ops Deepens Need for AI - Part 2

Will Cappelli is Field CTO at Moogsoft

The Latest

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 ... 

Virtual Private Networks became a cornerstone of enterprise security at a time when corporate infrastructure looked very different from today ... For years, this model worked well. But the architecture behind VPNs assumed a centralized corporate environment—one where the network itself was the hub of activity. In a cloud — first world, that assumption no longer holds ...

Website outages get resolved just as fast in August as they do in November. I went looking for the opposite: the summer slowdown everyone assumes is there once the people who fix things are away. It isn't in the data we collected, covering 1.8 million confirmed outages across tens of thousands of websites ...

This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...