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

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:

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

Managing the New Normal

The new normal includes not only periodic recurrences of Covid-19 outbreaks but also the periodic emergence of new global pandemics. This means putting in place at least three layers of digital business continuity practice:

■ Continuity for illness-free periods

■ Continuity for periods marked by known pandemics

■ Continuity for periods marked by new pandemics

Rules-based, historical data analysis, and predictive analysis based on history become useless in this scenario. Instead, what's needed is technology that can anticipate outages without reliance on stable historical patterns, as AIOps does.

Significant economic contraction and resulting pressure on both capital and operational expenditures will lead to chronic understaffing of IT operations and NOC functions. IT Ops can leverage AIOps to achieve heightened levels of automation and to support radically deep cuts in the number of tools required to both monitor the digital infrastructure and respond to incidents that occur.

As remote work becomes default, it will become impossible to replicate the "monitoring cockpit" experience or the "service desk cockpit" experience. IT operations team members and first responders will need to get by with standard IT management software. That requires a significant increase in the number of signals that require observation on the one hand and the number of tickets which require response on the other hand. AIOps can help to manage this by reducing signals and tickets.

Optimizing the New Normal

The move to an almost entirely virtualized infrastructure and service portfolio will allow for maximum agility and the ability to reconfigure people, processes and technologies to meet emerging business needs (which will themselves likely be novel in the new normal.) To provide continuous assurance of service levels (even as the services themselves evolve), IT Ops teams can leverage AIOps and its ability to anticipate outages and brown-outs on the basis of data as it arrives, as opposed to pre-existing static models of topology and user behaviour.

The shift from an IT budget that, beyond labor commitments, is dominated by capital expenditures and maintenance, to one that is almost entirely dominated by renewable operational expenditures, will increase business resilience in the face of the three types of continuity issues outlined above. AIOps can help in this area as well by helping to anticipate short-term fluctuations in resource requirements based on the possibility of looming outages and brown-outs.

The economic contraction will accelerate digitalization and, in fact, lead to what may be called "maximum digitalization" with the consequence that, for the most part, business process events will be IT system state changes. One will not be able to manage business processes unless one simultaneously manages IT system events. AIOps can be invaluable here by effectively discovering and managing the higher-level IT system event patterns that are, in fact, business process patterns.

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 2

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:

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

Managing the New Normal

The new normal includes not only periodic recurrences of Covid-19 outbreaks but also the periodic emergence of new global pandemics. This means putting in place at least three layers of digital business continuity practice:

■ Continuity for illness-free periods

■ Continuity for periods marked by known pandemics

■ Continuity for periods marked by new pandemics

Rules-based, historical data analysis, and predictive analysis based on history become useless in this scenario. Instead, what's needed is technology that can anticipate outages without reliance on stable historical patterns, as AIOps does.

Significant economic contraction and resulting pressure on both capital and operational expenditures will lead to chronic understaffing of IT operations and NOC functions. IT Ops can leverage AIOps to achieve heightened levels of automation and to support radically deep cuts in the number of tools required to both monitor the digital infrastructure and respond to incidents that occur.

As remote work becomes default, it will become impossible to replicate the "monitoring cockpit" experience or the "service desk cockpit" experience. IT operations team members and first responders will need to get by with standard IT management software. That requires a significant increase in the number of signals that require observation on the one hand and the number of tickets which require response on the other hand. AIOps can help to manage this by reducing signals and tickets.

Optimizing the New Normal

The move to an almost entirely virtualized infrastructure and service portfolio will allow for maximum agility and the ability to reconfigure people, processes and technologies to meet emerging business needs (which will themselves likely be novel in the new normal.) To provide continuous assurance of service levels (even as the services themselves evolve), IT Ops teams can leverage AIOps and its ability to anticipate outages and brown-outs on the basis of data as it arrives, as opposed to pre-existing static models of topology and user behaviour.

The shift from an IT budget that, beyond labor commitments, is dominated by capital expenditures and maintenance, to one that is almost entirely dominated by renewable operational expenditures, will increase business resilience in the face of the three types of continuity issues outlined above. AIOps can help in this area as well by helping to anticipate short-term fluctuations in resource requirements based on the possibility of looming outages and brown-outs.

The economic contraction will accelerate digitalization and, in fact, lead to what may be called "maximum digitalization" with the consequence that, for the most part, business process events will be IT system state changes. One will not be able to manage business processes unless one simultaneously manages IT system events. AIOps can be invaluable here by effectively discovering and managing the higher-level IT system event patterns that are, in fact, business process patterns.

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 ...