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

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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

When AI Becomes the Corporate OS

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

The $600 Billion Wake Up Call

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Breaking Down Agentic AI Fragmentation and Complexity with Governance

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud Spend Is Rising and Cloud Optimization Is Key to Funding AI and Protecting Margins

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

Building AI Agents That Build Trust

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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 ...

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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 ...

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