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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

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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

Hot Topics

The Latest

According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

Image
Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...