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

Manufacturing Organizations Doubled AI Investment Yet Only 37% Fully Prepared to Operationalize AI

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Optimizing Decisions with Edge-First Cognitive Intelligence

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

The New Perimeter Is the User: Why Identity Is the Real Network Edge

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

When AI Infrastructure Stops Behaving Like Infrastructure

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Enterprise Resilience: Understanding the Shift from Static to Dynamic

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile Users Expect Perfection in 2026

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

AI Confidence Is High. Readiness Is Not. What the Data Tells Us - and Why It Matters

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI, Realistic Expectations and the Future of IT Operations in 2026

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

Trust, But Verify: Building Confidence in AI Through Human Oversight

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

Data Centers Plan to Reduce Reliance on Grid

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...