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AIOps: Yet Another Acronym or a Real Transformational Technology for IT Operations?

Roy Illsley
Omdia

The introduction of the latest technology — such as AI and machine learning — can be seen as a way for organizations to accelerate growth, increase efficiency, and improve customer service. However, the truth is that the technology alone will do little to deliver on these business outcomes. AI for IT operations (AIOps) is one area where the application of technology, if not matched with organizational maturity readiness, will fail to deliver all the promised benefits.

Market Definition

The AIOps market has evolved from many different domain expert systems being developed to provide more holistic capabilities. The new report, Omdia Universe: Selecting an AIOps Solution, 2021–22, brings Omdia's vision of what an AIOps solution should currently deliver as well as areas we expect AIOps to evolve into.

AIOps is a term that has been adopted by the market to define the way IT operations needs to perform in digital enterprises. Omdia defines AIOps as the overarching technology that can bring all the management practices (observability, rapid mitigation, augmented decision making, self-healing, auto-scaling, etc.) in IT together. This concept does not translate to a single person or team that can now perform all these activities; rather, a single view can be obtained, and a single control point established. Omdia clarifies the sector by identifying the key characteristics of an AIOps solution.

The current reality of the market is that many different AIOps solutions exist, but they do not all deliver on Omdia's ten key characteristics. 

Omdia View

The IT department is going through a significant, and many would argue long overdue, transformation. At the heart of this transformation are the new emerging technologies such as AI, quantum computing, blockchain, etc.

The degree to which these technologies when deployed will deliver the desired business outcomes is less clear, and Omdia argues the outcomes are more closely linked to the maturity and culture of the organization, and matching that to the use of technology, than to the technology itself. IT operational activities (defined as those activities IT undertakes to ensure business users can perform their activities) span multiple different disciplines, yet most organizations still have a very team-centric, or domain-centric, approach to managing and orchestrating these disciplines.

The rise of DevOps was seen as the vehicle to bring two of these different disciplines together for the greater good in order to improve business outcomes faster. While it is true DevOps has gone some way toward changing the culture and mind-set of IT operational activities, it remains focused on a too-narrow definition of the role IT has to play in the digital enterprise.

Omdia considers that AIOps represents a natural evolution of DevOps and can become more inclusive of all the activities that impact the customer/employee experience, or business outcome. It is only when IT can ensure its focus is customer outcome-centric that its activities will be aligned to the business's objectives and the tools used will be used in a way designed to ensure it meets those objectives.

AIOps adds the missing link that can bring the disparate processes and tools together for the single purpose of delivering improved business outcomes, not just improving IT efficiency.

Recommendations for Enterprises

The adoption of new concepts that claim to be a silver bullet has traditionally failed to deliver fully on its promises. AIOps is no exception; it is not a shrink-wrapped solution that can simply be deployed in order to automatically generate an improvement in the performance of IT operations. Instead, it is the application of AI to the different activities IT performs.

By linking all these activities, sharing knowledge, and automating actions, AIOps can deliver. But this requires the IT department to be honest in terms of the current level of organizational maturity and what it can realistically expect to reach in the next 12 months by using AIOps. 

Omdia's AIOps Universe

Omdia is a proud advocate of the business benefits derived through technology, and AIOps is at the forefront of realizing benefits for IT operational teams. The Omdia Universe report is not intended to advocate an individual vendor, but rather to guide and inform the selection process to ensure all relevant options are considered and evaluated in an efficient manner. The report findings gravitate toward the customer's perspective and likely requirements, characteristically those of a medium-large multi-national enterprise (5,000+ employees).

Download the Omdia Universe Report

Roy Illsley is a Chief Analyst at Omdia

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Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

AIOps: Yet Another Acronym or a Real Transformational Technology for IT Operations?

Roy Illsley
Omdia

The introduction of the latest technology — such as AI and machine learning — can be seen as a way for organizations to accelerate growth, increase efficiency, and improve customer service. However, the truth is that the technology alone will do little to deliver on these business outcomes. AI for IT operations (AIOps) is one area where the application of technology, if not matched with organizational maturity readiness, will fail to deliver all the promised benefits.

Market Definition

The AIOps market has evolved from many different domain expert systems being developed to provide more holistic capabilities. The new report, Omdia Universe: Selecting an AIOps Solution, 2021–22, brings Omdia's vision of what an AIOps solution should currently deliver as well as areas we expect AIOps to evolve into.

AIOps is a term that has been adopted by the market to define the way IT operations needs to perform in digital enterprises. Omdia defines AIOps as the overarching technology that can bring all the management practices (observability, rapid mitigation, augmented decision making, self-healing, auto-scaling, etc.) in IT together. This concept does not translate to a single person or team that can now perform all these activities; rather, a single view can be obtained, and a single control point established. Omdia clarifies the sector by identifying the key characteristics of an AIOps solution.

The current reality of the market is that many different AIOps solutions exist, but they do not all deliver on Omdia's ten key characteristics. 

Omdia View

The IT department is going through a significant, and many would argue long overdue, transformation. At the heart of this transformation are the new emerging technologies such as AI, quantum computing, blockchain, etc.

The degree to which these technologies when deployed will deliver the desired business outcomes is less clear, and Omdia argues the outcomes are more closely linked to the maturity and culture of the organization, and matching that to the use of technology, than to the technology itself. IT operational activities (defined as those activities IT undertakes to ensure business users can perform their activities) span multiple different disciplines, yet most organizations still have a very team-centric, or domain-centric, approach to managing and orchestrating these disciplines.

The rise of DevOps was seen as the vehicle to bring two of these different disciplines together for the greater good in order to improve business outcomes faster. While it is true DevOps has gone some way toward changing the culture and mind-set of IT operational activities, it remains focused on a too-narrow definition of the role IT has to play in the digital enterprise.

Omdia considers that AIOps represents a natural evolution of DevOps and can become more inclusive of all the activities that impact the customer/employee experience, or business outcome. It is only when IT can ensure its focus is customer outcome-centric that its activities will be aligned to the business's objectives and the tools used will be used in a way designed to ensure it meets those objectives.

AIOps adds the missing link that can bring the disparate processes and tools together for the single purpose of delivering improved business outcomes, not just improving IT efficiency.

Recommendations for Enterprises

The adoption of new concepts that claim to be a silver bullet has traditionally failed to deliver fully on its promises. AIOps is no exception; it is not a shrink-wrapped solution that can simply be deployed in order to automatically generate an improvement in the performance of IT operations. Instead, it is the application of AI to the different activities IT performs.

By linking all these activities, sharing knowledge, and automating actions, AIOps can deliver. But this requires the IT department to be honest in terms of the current level of organizational maturity and what it can realistically expect to reach in the next 12 months by using AIOps. 

Omdia's AIOps Universe

Omdia is a proud advocate of the business benefits derived through technology, and AIOps is at the forefront of realizing benefits for IT operational teams. The Omdia Universe report is not intended to advocate an individual vendor, but rather to guide and inform the selection process to ensure all relevant options are considered and evaluated in an efficient manner. The report findings gravitate toward the customer's perspective and likely requirements, characteristically those of a medium-large multi-national enterprise (5,000+ employees).

Download the Omdia Universe Report

Roy Illsley is a Chief Analyst at Omdia

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...