Q&A: Gartner Talks About AIOps - Part 1

In APMdigest's exclusive interview, Colin Fletcher, Research Director at Gartner, talks about Algorithmic IT Operations (AIOps) and how it will impact ITOA (IT Operations Analytics) and APM (Application Performance Management).

APM: For the readers who are unfamiliar, what is AIOps?

CF: Algorithmic IT operations (AIOps) platforms utilize big data, modern machine learning and other advanced analytics technologies to directly and indirectly enhance all primary IT operations functions with proactive, personal and dynamic insight. AIOps platforms enable the concurrent use of multiple data sources, data collection methods, analytical technologies (real-time and deep) and presentation technologies. AIOps platforms represent the evolving and expanded use of technologies previously categorized as IT operations analytics (ITOA).

APM: What advantages can IT Ops gain from AIOps?

CF: I find it is really useful when looking at how you or your operations team can take advantage of predictive, machine learning-enhanced tools to think in terms of how they assist and/or augment your current capabilities. The ideal state or ultimate goal of an AIOps investment is a platform that is capable of continuously, proactively generating insights that are used in support any number of internal and external customers. While AIOps has tremendous potential to deliver on use cases that stretch well beyond core IT operations functions, to date we've seen enterprises get real, tangible value using AIOps platforms to:

■ Make the holy grail vision of a "single pane of glass" a reality across multiple technology stacks and generations most often in support of root cause analysis

■ Rapidly support new digital business initiatives and their accompanying use of the latest disruptive technologies (containers, microservices, IoT, etc.) at scale

■ Achieve the long sought after goal of automated, sustainable, scalable, and most importantly, useful event correlation that works to reduce alert noise/fatigue and speed diagnosis

APM: How does AIOps enable you to get more from your existing data?

CF: To put it as simply as possible, AIOps provides for many a more practical way to get multiple data sources into one platform and apply multiple analytical technologies to that data in an automated fashion to discover the relationships and patterns that lie undiscovered in previously isolated data. This is particularly true in the case of utilizing IT operational data in combination with data generated by applications or infrastructure normally outside of IT's operational visibility. To be clear, this is not to say that this has somehow not been possible previously, of course it has been, but in most cases prior to AIOps, it was cost prohibitive or technically challenging to do so.

APM: Does AIOPs augment or support APM?

CF: Currently AIOps is typically used to supplement APM use cases and/or tooling by providing a much more practical and in some cases cost effective means of filling in the gap between what data is being collected directly by the APM tool and the rest of the supporting applications, infrastructure, security, service, customer/business operational, and configuration data that is rarely directly integrated or utilized in APM tools. This is due in large part to AIOps' emphasis on providing the ability to continuously deliver insights from multiple data sources regardless of the mechanism used to collect the data.

APM: Does AIOps support DevOps?

CF: DevOps teams and particularly application developers gravitate to AIOps tools naturally in their search for data-driven (as opposed to instrumentation-driven) insight to their particular application's behavior.

Many DevOps teams also cite the "democratic" or "agnostic" or "open" nature of AIOps tools that from day one are assumed to be integrated with "something or multiple somethings" to deliver combinative value as well as their genuine support of experimentation and creative use of data for purposes beyond problem solving as reasons for using AIOps tools.

Most frequently we see DevOps teams using AIOps to monitor application and infrastructure performance, troubleshoot issues, and provide dashboards and reporting across entire toolchains that consist of multiple tools used in both development (CI, Test, ARA) and operations (monitoring, CD/Release/Configuration).

Read Gartner Talks About AIOps - Part 2

Related Links

Hot Topics

The Latest

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

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 ...

AI Deepfakes: Rethinking Trust in the Workplace

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 ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

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 ...

Why ITOps Need Right-Sized AI, Not Bigger Models

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 ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

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 ...

5 Takeaways from the Observability Forecast for Media and Entertainment

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 ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

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

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

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 ...

5 Security Principles Every Entrepreneur Should Apply to Leadership

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 ...

Q&A: Gartner Talks About AIOps - Part 1

In APMdigest's exclusive interview, Colin Fletcher, Research Director at Gartner, talks about Algorithmic IT Operations (AIOps) and how it will impact ITOA (IT Operations Analytics) and APM (Application Performance Management).

APM: For the readers who are unfamiliar, what is AIOps?

CF: Algorithmic IT operations (AIOps) platforms utilize big data, modern machine learning and other advanced analytics technologies to directly and indirectly enhance all primary IT operations functions with proactive, personal and dynamic insight. AIOps platforms enable the concurrent use of multiple data sources, data collection methods, analytical technologies (real-time and deep) and presentation technologies. AIOps platforms represent the evolving and expanded use of technologies previously categorized as IT operations analytics (ITOA).

APM: What advantages can IT Ops gain from AIOps?

CF: I find it is really useful when looking at how you or your operations team can take advantage of predictive, machine learning-enhanced tools to think in terms of how they assist and/or augment your current capabilities. The ideal state or ultimate goal of an AIOps investment is a platform that is capable of continuously, proactively generating insights that are used in support any number of internal and external customers. While AIOps has tremendous potential to deliver on use cases that stretch well beyond core IT operations functions, to date we've seen enterprises get real, tangible value using AIOps platforms to:

■ Make the holy grail vision of a "single pane of glass" a reality across multiple technology stacks and generations most often in support of root cause analysis

■ Rapidly support new digital business initiatives and their accompanying use of the latest disruptive technologies (containers, microservices, IoT, etc.) at scale

■ Achieve the long sought after goal of automated, sustainable, scalable, and most importantly, useful event correlation that works to reduce alert noise/fatigue and speed diagnosis

APM: How does AIOps enable you to get more from your existing data?

CF: To put it as simply as possible, AIOps provides for many a more practical way to get multiple data sources into one platform and apply multiple analytical technologies to that data in an automated fashion to discover the relationships and patterns that lie undiscovered in previously isolated data. This is particularly true in the case of utilizing IT operational data in combination with data generated by applications or infrastructure normally outside of IT's operational visibility. To be clear, this is not to say that this has somehow not been possible previously, of course it has been, but in most cases prior to AIOps, it was cost prohibitive or technically challenging to do so.

APM: Does AIOPs augment or support APM?

CF: Currently AIOps is typically used to supplement APM use cases and/or tooling by providing a much more practical and in some cases cost effective means of filling in the gap between what data is being collected directly by the APM tool and the rest of the supporting applications, infrastructure, security, service, customer/business operational, and configuration data that is rarely directly integrated or utilized in APM tools. This is due in large part to AIOps' emphasis on providing the ability to continuously deliver insights from multiple data sources regardless of the mechanism used to collect the data.

APM: Does AIOps support DevOps?

CF: DevOps teams and particularly application developers gravitate to AIOps tools naturally in their search for data-driven (as opposed to instrumentation-driven) insight to their particular application's behavior.

Many DevOps teams also cite the "democratic" or "agnostic" or "open" nature of AIOps tools that from day one are assumed to be integrated with "something or multiple somethings" to deliver combinative value as well as their genuine support of experimentation and creative use of data for purposes beyond problem solving as reasons for using AIOps tools.

Most frequently we see DevOps teams using AIOps to monitor application and infrastructure performance, troubleshoot issues, and provide dashboards and reporting across entire toolchains that consist of multiple tools used in both development (CI, Test, ARA) and operations (monitoring, CD/Release/Configuration).

Read Gartner Talks About AIOps - Part 2

Related Links

Hot Topics

The Latest

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

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 ...

AI Deepfakes: Rethinking Trust in the Workplace

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 ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

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 ...

Why ITOps Need Right-Sized AI, Not Bigger Models

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 ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

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 ...

5 Takeaways from the Observability Forecast for Media and Entertainment

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 ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

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

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

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 ...

5 Security Principles Every Entrepreneur Should Apply to Leadership

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 ...