Skip to main content

AIOps = Brains (AI) + Brawn (Automation)

Valerie O'Connell
EMA

Artificial intelligence (AI) may be the brains, but when the market hears the term "AIOps," it puts automation in the mix. After all, what is the use of knowing without doing?

Recent EMA research, AI(work)Ops 2021: The State of AIOps took a ground-level look at AIOps as it is used globally today and as planned in the coming months — a view more practical than aspirational. As it turns out, AI and automation form the one-two punch that makes digital service excellence possible in what is otherwise an impossible complexity of IT operations.

Both AIOps and automation are C-suite winners, strategic and enterprise-wide for organizations that self-rate their AIOps implementations as highly successful. However, automation holds the edge. When EMA explicitly separated AIOps and automation, more than half of all research participants identified automation as a C-suite initiative, strategic across the enterprise, while AIOps weighed in at 32%. An interesting side note is that, when it comes to automation, CFOs join CIOs in having a significant decision-making role, presumably because there is a fairly straight line between automation and savings.

The Double-Edged Sword

Yet, automation is a double-edged sword. Beloved by CXOs both in theory and in budget allocations, it is a harder sell on the ground, especially when it is paired with AI and machine learning (ML). In addition to the time-honored resistance to change that accompanies almost all major IT initiatives, AIOps automation faces deep-rooted fear and distrust.

EMA research shows that AI-driven automation is an acquired taste. As organizations gain experience, that experience is statistically highly likely to be positive, productive, and profitable. Just as success breeds success, automation breeds automation. It follows that the more mature AIOps implementations are also the most advanced in terms of number, types, and degree of automation in play.

What was a little bit more surprising was that all-autonomous automation is not the universal endgame. The preferred state is increased automation that retains a human touch.


The Pain/Gain Ratio

Asked, "How would you characterize the value AIOps brings to your organization relative to the cost?" respondents were almost unanimous in achieving value. The difference was in degree. Offered a range of answers, including negative ones that no one selected, 17% rated AIOps value as a break even proposition, 41% chose "high" value, and 21% cited "very high" value relative to cost. EMA went on to explore the detail and range of benefits that comprise this value, both qualitative and quantitative. For instance, asked about the impact of AIOps on the relationship between IT and the rest of the business, 99% of the responses were positive. Notably, 21% chose the superlative "transformational" to describe that impact.

Although the gains are plentiful, the path to AIOps benefits is neither fast nor easy. Asked to rate the rigor of AIOps implementation, only 18% characterized the process as either "smooth" or "relatively easy." The rest ranged between "straightforward, but not easy" (28%) and "very difficult" (11%), with 43% choosing the midpoint "challenging" as the best descriptor. Asked to enumerate those challenges, respondents ranked cost, data, and conflicts within IT as the top impediments to AIOps initiatives.

Addressing the Data Dilemma

EMA research and experience show that cost holds a leading position in the list of challenges for all major IT initiatives, as does conflict within IT. However, the challenge of data accuracy and accessibility holds an elevated role in AIOps because of the wide range of data sources involved in its implementation and its high reliance on data in execution. For that reason, smoothing data issues can accelerate AIOps initiatives and the benefits that will predictably attend success.

Valerie O'Connell is EMA Research Director of Digital Service Execution

Hot Topics

The Latest

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...

AIOps = Brains (AI) + Brawn (Automation)

Valerie O'Connell
EMA

Artificial intelligence (AI) may be the brains, but when the market hears the term "AIOps," it puts automation in the mix. After all, what is the use of knowing without doing?

Recent EMA research, AI(work)Ops 2021: The State of AIOps took a ground-level look at AIOps as it is used globally today and as planned in the coming months — a view more practical than aspirational. As it turns out, AI and automation form the one-two punch that makes digital service excellence possible in what is otherwise an impossible complexity of IT operations.

Both AIOps and automation are C-suite winners, strategic and enterprise-wide for organizations that self-rate their AIOps implementations as highly successful. However, automation holds the edge. When EMA explicitly separated AIOps and automation, more than half of all research participants identified automation as a C-suite initiative, strategic across the enterprise, while AIOps weighed in at 32%. An interesting side note is that, when it comes to automation, CFOs join CIOs in having a significant decision-making role, presumably because there is a fairly straight line between automation and savings.

The Double-Edged Sword

Yet, automation is a double-edged sword. Beloved by CXOs both in theory and in budget allocations, it is a harder sell on the ground, especially when it is paired with AI and machine learning (ML). In addition to the time-honored resistance to change that accompanies almost all major IT initiatives, AIOps automation faces deep-rooted fear and distrust.

EMA research shows that AI-driven automation is an acquired taste. As organizations gain experience, that experience is statistically highly likely to be positive, productive, and profitable. Just as success breeds success, automation breeds automation. It follows that the more mature AIOps implementations are also the most advanced in terms of number, types, and degree of automation in play.

What was a little bit more surprising was that all-autonomous automation is not the universal endgame. The preferred state is increased automation that retains a human touch.


The Pain/Gain Ratio

Asked, "How would you characterize the value AIOps brings to your organization relative to the cost?" respondents were almost unanimous in achieving value. The difference was in degree. Offered a range of answers, including negative ones that no one selected, 17% rated AIOps value as a break even proposition, 41% chose "high" value, and 21% cited "very high" value relative to cost. EMA went on to explore the detail and range of benefits that comprise this value, both qualitative and quantitative. For instance, asked about the impact of AIOps on the relationship between IT and the rest of the business, 99% of the responses were positive. Notably, 21% chose the superlative "transformational" to describe that impact.

Although the gains are plentiful, the path to AIOps benefits is neither fast nor easy. Asked to rate the rigor of AIOps implementation, only 18% characterized the process as either "smooth" or "relatively easy." The rest ranged between "straightforward, but not easy" (28%) and "very difficult" (11%), with 43% choosing the midpoint "challenging" as the best descriptor. Asked to enumerate those challenges, respondents ranked cost, data, and conflicts within IT as the top impediments to AIOps initiatives.

Addressing the Data Dilemma

EMA research and experience show that cost holds a leading position in the list of challenges for all major IT initiatives, as does conflict within IT. However, the challenge of data accuracy and accessibility holds an elevated role in AIOps because of the wide range of data sources involved in its implementation and its high reliance on data in execution. For that reason, smoothing data issues can accelerate AIOps initiatives and the benefits that will predictably attend success.

Valerie O'Connell is EMA Research Director of Digital Service Execution

Hot Topics

The Latest

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...