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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.

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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.

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...