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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.

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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 ...

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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

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 ...