Q&A: Gartner Talks About AIOps - Part 2

In APMdigest's exclusive interview, Colin Fletcher, Research Director at Gartner, talks about Algorithmic IT Operations (AIOps) and the challenges and recommendations for AIOps adoption.

Start with Gartner Talks About AIOps - Part 1

APM: What are current tools lacking that has given rise to AIOps?

CF: Many tools used across IT Operations Management historically and even today are implicitly or explicitly built around paradigms that limit their ability to cope with or keep up with rapidly changing demands and technological innovation.

Examples of these design paradigms include but are not limited to:

■ A rigid linkage between the data collection mechanism, the data retention mechanism, the analytical engine(s), and the means to visualize and interact with their results

■ Analytical capabilities that depend heavily on manual development and maintenance to deliver value

■ Data ingestion and storage mechanisms that do not easily scale

■ Limited support of bidirectional, "full" integration with other tools

■ Best practice-formed workflows and processes that are rarely adapted, updated or otherwise modified

These paradigms evolved for good reason out of decades of operational experience, however these same paradigms are exactly the calcified barriers to meeting growing and diversifying needs of business stakeholders.

APM: Is this tool used on top of a company's existing analytics and other IT Operations tools, or does it replace them?

CF: Currently most organizations are using AIOps platforms in tandem with other IT operations management tools and we expect that will continue to be the case for some time. That said, more and more enterprises and in particular DevOps teams find that over time their AIOps platform should be considered to be used in place of multiple tool types, particularly legacy tools.

APM: Is a certain type or size of enterprise a better candidate for AIOps?

CF: Keeping in mind that AIOps encompasses a wide span of capabilities, from basic log/machine data analysis tools to sophisticated, machine learning analytical engines, we see enterprises of all types finding good places to start on their AIOps journey.

APM: Does AIOps require a new skillset in IT?

CF: As mentioned earlier, AIOps covers a wide span of tooling, so it's possible to get started on an AIOps journey using very basic skills to start analyzing logs for example. That said, taking maximum advantage of all that AIOps has to offer will require an investment in developing or acquiring a level of analytical skill not always found in IT operations teams. Enterprises often find that DevOps teams/initiatives and business analysts are often good internal sources of talent.

APM: What do you see as the biggest barriers to AIOps adoption?

CF: Misplaced fear that the automated analytical capabilities AIOps offers will directly lead to job reductions, real and perceived cost issues, previous negative experiences with prior generations of statistical pattern discovery and recognition and/or event correlation and analysis tools that failed to deliver, fear that AIOps tools require an unobtainable level of skills to be useful, "tool gravity"/reluctance to change.

As you probably detected, most of these issues while reasonably formed, are rooted in perceptions built around a literal previous generation of thinking and technologies that does not directly apply to today's AIOps tools.

APM: For enterprises starting out with AIOps, do you have a recommendation of where to start?

CF: AIOps tools have progressed to the point that many of them are actually very easy to just look at and try with little or no cost – I think that's a great way to get a sense of what these tools are capable of, and I wouldn't wait long to do so! Of course Gartner clients are always welcome to also take advantage of our published research on the topic or get some time on the calendar to talk about how AIOps can help them.

ABOUT Colin Fletcher

Colin Fletcher focuses his research on how advances in application release automation (ARA), IT operations analytics (ITOA), continuous configuration automation (CCA) and DevOps can help IT operations teams continually drive greater business success, reduce costs and mitigate risk. Fletcher's research is informed by daily conversations with clients and thought leaders, as well as more than 16 years of IT practitioner experience (from service desk to admin to consultant), leadership experience (team, project and product management) and creative marketing experience (product and strategic) built at companies large and small, including Apple, HP, BMC Software, Motorola, IBM Global Services, Dell and several startups.

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Q&A: Gartner Talks About AIOps - Part 2

In APMdigest's exclusive interview, Colin Fletcher, Research Director at Gartner, talks about Algorithmic IT Operations (AIOps) and the challenges and recommendations for AIOps adoption.

Start with Gartner Talks About AIOps - Part 1

APM: What are current tools lacking that has given rise to AIOps?

CF: Many tools used across IT Operations Management historically and even today are implicitly or explicitly built around paradigms that limit their ability to cope with or keep up with rapidly changing demands and technological innovation.

Examples of these design paradigms include but are not limited to:

■ A rigid linkage between the data collection mechanism, the data retention mechanism, the analytical engine(s), and the means to visualize and interact with their results

■ Analytical capabilities that depend heavily on manual development and maintenance to deliver value

■ Data ingestion and storage mechanisms that do not easily scale

■ Limited support of bidirectional, "full" integration with other tools

■ Best practice-formed workflows and processes that are rarely adapted, updated or otherwise modified

These paradigms evolved for good reason out of decades of operational experience, however these same paradigms are exactly the calcified barriers to meeting growing and diversifying needs of business stakeholders.

APM: Is this tool used on top of a company's existing analytics and other IT Operations tools, or does it replace them?

CF: Currently most organizations are using AIOps platforms in tandem with other IT operations management tools and we expect that will continue to be the case for some time. That said, more and more enterprises and in particular DevOps teams find that over time their AIOps platform should be considered to be used in place of multiple tool types, particularly legacy tools.

APM: Is a certain type or size of enterprise a better candidate for AIOps?

CF: Keeping in mind that AIOps encompasses a wide span of capabilities, from basic log/machine data analysis tools to sophisticated, machine learning analytical engines, we see enterprises of all types finding good places to start on their AIOps journey.

APM: Does AIOps require a new skillset in IT?

CF: As mentioned earlier, AIOps covers a wide span of tooling, so it's possible to get started on an AIOps journey using very basic skills to start analyzing logs for example. That said, taking maximum advantage of all that AIOps has to offer will require an investment in developing or acquiring a level of analytical skill not always found in IT operations teams. Enterprises often find that DevOps teams/initiatives and business analysts are often good internal sources of talent.

APM: What do you see as the biggest barriers to AIOps adoption?

CF: Misplaced fear that the automated analytical capabilities AIOps offers will directly lead to job reductions, real and perceived cost issues, previous negative experiences with prior generations of statistical pattern discovery and recognition and/or event correlation and analysis tools that failed to deliver, fear that AIOps tools require an unobtainable level of skills to be useful, "tool gravity"/reluctance to change.

As you probably detected, most of these issues while reasonably formed, are rooted in perceptions built around a literal previous generation of thinking and technologies that does not directly apply to today's AIOps tools.

APM: For enterprises starting out with AIOps, do you have a recommendation of where to start?

CF: AIOps tools have progressed to the point that many of them are actually very easy to just look at and try with little or no cost – I think that's a great way to get a sense of what these tools are capable of, and I wouldn't wait long to do so! Of course Gartner clients are always welcome to also take advantage of our published research on the topic or get some time on the calendar to talk about how AIOps can help them.

ABOUT Colin Fletcher

Colin Fletcher focuses his research on how advances in application release automation (ARA), IT operations analytics (ITOA), continuous configuration automation (CCA) and DevOps can help IT operations teams continually drive greater business success, reduce costs and mitigate risk. Fletcher's research is informed by daily conversations with clients and thought leaders, as well as more than 16 years of IT practitioner experience (from service desk to admin to consultant), leadership experience (team, project and product management) and creative marketing experience (product and strategic) built at companies large and small, including Apple, HP, BMC Software, Motorola, IBM Global Services, Dell and several startups.

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? ...

Keys to Building a Partner Ecosystem That Scales

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 ...

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 ...