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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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...