Skip to main content

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

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...

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

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...