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5 Ways AIOps Helps IT Teams

Phil Tee

In our digital world, it is impossible to reduce downtime and cut through alert noise without the proper tools. The pressure to avoid outages to maintain and improve customer experience has never been higher, and if you think old tools can handle the needs of today, think again.

AIOps leverages the power of artificial intelligence (AI) and machine learning (ML) to improve performance and availability.

Still not convinced on the value an AIOps platform offers? Consider this: one minute of downtime at Amazon costs the company roughly $220,000 in revenue. With that kind of money on the line, SRE and DevOps teams forced to manage availability by writing rules and querying logs manually are set up to fail — and failure is costly. AIOps is the necessary lift your monitoring tools need to improve performance and cut out the toil for DevOps and IT teams.

Here are five ways AIOps does exactly that:

1. Reduce noise

If your team has thousands of alerts coming in daily, there is no way to differentiate between which need immediate attention and those that can wait. Instead, when DevOps and IT teams are faced with an outage they find themselves bogged down in huges data sets as they attempt to find the incident. Legacy tools simply aren’t built for observability and the critical task of automating root cause and simply are not scalable enough for the high load of data they must process.

On the other hand , AIOps platforms thrive in this high data load environment.

AIOps (the key here: AI) solutions are built to look for anomalies and start remediating immediately, meaning DevOps and IT teams don’t have to hunt down the issue among thousands of alerts. AIOps is so powerful that it can even find the root cause before a customer even realizes the service is down!

2. Detect early

AIOps brings advanced capabilities to pinpoint which events or logs might be the issue to investigate early signs of a problem with anomaly detection.

Even better, AIOps platforms have no dependence upon rules. Instead, alerts and incidents evolve in real time, supported by deep metrication of your environment. This means that you do not have to wait for all the rules to be met, saving you costly (remember the price of downtime at Amazon) minutes as you tackle issues in the services you own.

3. Identify cause

These days, engineers regularly upgrade platforms, and systems are continuously changing. With an IT culture focused on constant change, it is difficult to know where to look first when things go wrong.

If the house is on fire, where do you point the firehose?

AIOps tells you exactly where to focus your efforts. AIOps platforms automatically add context to alerts and change records to show where issues are. These tools can easily identify patterns in data that a human would miss and help you diagnose and alert your team as it happens.

4. Automate responses

What is the quickest way to avoid alert fatigue and boost job satisfaction? AIOps.

If DevOps teams are spending all of their time manually sorting through alerts, there is little time for them to do what they enjoy: building and innovating. AIOps tools use AI and ML to automatically resolve an incident once detected or route the issue to the correct team to remedy it.

Not only do AIOps tools free up time and maintain job fulfillment for your team, but when a notification is sent to the IT team, you know that it’s mission-critical.

5. Trust one system

The number of different tools DevOps teams are expected to manage is overwhelming. But, choosing the right AIOps platform can replace other tools without losing capabilities. If you want quality incident management, invest in a quality AIOps platform. With flexible integrations, adaptable APIs and collaborative, automated incident management all within the same AIOps tool, you can manage an outage from start to finish without leaving the platform.

Of course, there are many more use cases for AIOps platforms. The impact AIOps has on every aspect of a business, from customer experience to employee satisfaction and revenue, is beyond what anyone could have predicted when Gartner introduced the term five years ago. It is why AIOps is the lift that will allow organizations to keep up as the digital transformation continues and changes.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

5 Ways AIOps Helps IT Teams

Phil Tee

In our digital world, it is impossible to reduce downtime and cut through alert noise without the proper tools. The pressure to avoid outages to maintain and improve customer experience has never been higher, and if you think old tools can handle the needs of today, think again.

AIOps leverages the power of artificial intelligence (AI) and machine learning (ML) to improve performance and availability.

Still not convinced on the value an AIOps platform offers? Consider this: one minute of downtime at Amazon costs the company roughly $220,000 in revenue. With that kind of money on the line, SRE and DevOps teams forced to manage availability by writing rules and querying logs manually are set up to fail — and failure is costly. AIOps is the necessary lift your monitoring tools need to improve performance and cut out the toil for DevOps and IT teams.

Here are five ways AIOps does exactly that:

1. Reduce noise

If your team has thousands of alerts coming in daily, there is no way to differentiate between which need immediate attention and those that can wait. Instead, when DevOps and IT teams are faced with an outage they find themselves bogged down in huges data sets as they attempt to find the incident. Legacy tools simply aren’t built for observability and the critical task of automating root cause and simply are not scalable enough for the high load of data they must process.

On the other hand , AIOps platforms thrive in this high data load environment.

AIOps (the key here: AI) solutions are built to look for anomalies and start remediating immediately, meaning DevOps and IT teams don’t have to hunt down the issue among thousands of alerts. AIOps is so powerful that it can even find the root cause before a customer even realizes the service is down!

2. Detect early

AIOps brings advanced capabilities to pinpoint which events or logs might be the issue to investigate early signs of a problem with anomaly detection.

Even better, AIOps platforms have no dependence upon rules. Instead, alerts and incidents evolve in real time, supported by deep metrication of your environment. This means that you do not have to wait for all the rules to be met, saving you costly (remember the price of downtime at Amazon) minutes as you tackle issues in the services you own.

3. Identify cause

These days, engineers regularly upgrade platforms, and systems are continuously changing. With an IT culture focused on constant change, it is difficult to know where to look first when things go wrong.

If the house is on fire, where do you point the firehose?

AIOps tells you exactly where to focus your efforts. AIOps platforms automatically add context to alerts and change records to show where issues are. These tools can easily identify patterns in data that a human would miss and help you diagnose and alert your team as it happens.

4. Automate responses

What is the quickest way to avoid alert fatigue and boost job satisfaction? AIOps.

If DevOps teams are spending all of their time manually sorting through alerts, there is little time for them to do what they enjoy: building and innovating. AIOps tools use AI and ML to automatically resolve an incident once detected or route the issue to the correct team to remedy it.

Not only do AIOps tools free up time and maintain job fulfillment for your team, but when a notification is sent to the IT team, you know that it’s mission-critical.

5. Trust one system

The number of different tools DevOps teams are expected to manage is overwhelming. But, choosing the right AIOps platform can replace other tools without losing capabilities. If you want quality incident management, invest in a quality AIOps platform. With flexible integrations, adaptable APIs and collaborative, automated incident management all within the same AIOps tool, you can manage an outage from start to finish without leaving the platform.

Of course, there are many more use cases for AIOps platforms. The impact AIOps has on every aspect of a business, from customer experience to employee satisfaction and revenue, is beyond what anyone could have predicted when Gartner introduced the term five years ago. It is why AIOps is the lift that will allow organizations to keep up as the digital transformation continues and changes.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...