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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

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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

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

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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...