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Built-in Monitoring Is Most Important AIOps Feature

Built-in monitoring/native instrumentation ranked as the most important feature of an AIOps solution, cited by nearly 55% of respondents in a new study from OpsRamp, The State of AIOps 2023.

The study concludes that AIOps is delivering real benefits for enterprises and MSPs, even as two-thirds of respondents have concerns about how accurate the data going into their AIOps systems is.

With the global economy facing headwinds on multiple fronts including inflation, the cost-of-living crisis, higher interest rates and war in Ukraine, both enterprises and MSPs are focused on improving IT efficiency and automation in 2023. MSPs cited improving operational efficiencies as their No. 1 challenge to achieving steady growth and profitability, while enterprises pointed to automating as many operations as possible as the biggest need or challenge they were trying to overcome in 2023.

While more than 60% of respondents were adopting AIOps to improve service and application availability and performance, the second and third top choices were for automation of operations (58%) and processes (54%).

Meanwhile, the greatest IT operations challenge for enterprises in 2023 was automating as many operations as possible, cited by 66% of respondents. Yet barely half of respondents (52%) cited automation of tedious tasks as their primary operational benefit of AIOps, trailing reduction in open incident tickets (65%) and reduction in MTTD and MTTR (56%). Improvements in automation are clearly top of mind for enterprises and MSPs in 2023.

Other key findings include:

■ Application to infrastructure dependency mapping is the top incident management challenge for enterprises and MSPs, cited by 64% of total respondents.

■ Intelligent alerting is the No. 1 use case for AIOps today for both enterprises (70%) and MSPs (66%).

■ The vast majority of AIOps implementations—more than 80%—take six months or less.

■ Data accuracy was respondents' biggest concern about AIOps, cited by 70% of MSPs and 62% of enterprises.

■ AIOps is creating jobs, not killing them, though engineers with the right skillsets for AIOps remain hard to find. Just 36% of respondents were concerned about AIOps deployment causing job loss while 68% said it takes more than six months to hire engineers with the right skillsets for AIOps

"The study shows that AIOps is real and is delivering tangible benefits for enterprises and MSPs," said Suresh Vobbilesetty, EVP, Engineering at OpsRamp. "But it also shows that organizations' AIOps initiatives remain a work in progress and have a ways to go before they can realize the full potential of the technology."

Methodology: The study was conducted in December by a third party research firm, and includes input from 265 respondents who work at the general manager, director or vice president level at enterprises and MSPs in North America, Europe or Asia Pacific. All respondents have budget decision-making responsibilities for IT monitoring tools, and work at firms with at least $25 million in annual revenue and more than 500 employees.

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Built-in Monitoring Is Most Important AIOps Feature

Built-in monitoring/native instrumentation ranked as the most important feature of an AIOps solution, cited by nearly 55% of respondents in a new study from OpsRamp, The State of AIOps 2023.

The study concludes that AIOps is delivering real benefits for enterprises and MSPs, even as two-thirds of respondents have concerns about how accurate the data going into their AIOps systems is.

With the global economy facing headwinds on multiple fronts including inflation, the cost-of-living crisis, higher interest rates and war in Ukraine, both enterprises and MSPs are focused on improving IT efficiency and automation in 2023. MSPs cited improving operational efficiencies as their No. 1 challenge to achieving steady growth and profitability, while enterprises pointed to automating as many operations as possible as the biggest need or challenge they were trying to overcome in 2023.

While more than 60% of respondents were adopting AIOps to improve service and application availability and performance, the second and third top choices were for automation of operations (58%) and processes (54%).

Meanwhile, the greatest IT operations challenge for enterprises in 2023 was automating as many operations as possible, cited by 66% of respondents. Yet barely half of respondents (52%) cited automation of tedious tasks as their primary operational benefit of AIOps, trailing reduction in open incident tickets (65%) and reduction in MTTD and MTTR (56%). Improvements in automation are clearly top of mind for enterprises and MSPs in 2023.

Other key findings include:

■ Application to infrastructure dependency mapping is the top incident management challenge for enterprises and MSPs, cited by 64% of total respondents.

■ Intelligent alerting is the No. 1 use case for AIOps today for both enterprises (70%) and MSPs (66%).

■ The vast majority of AIOps implementations—more than 80%—take six months or less.

■ Data accuracy was respondents' biggest concern about AIOps, cited by 70% of MSPs and 62% of enterprises.

■ AIOps is creating jobs, not killing them, though engineers with the right skillsets for AIOps remain hard to find. Just 36% of respondents were concerned about AIOps deployment causing job loss while 68% said it takes more than six months to hire engineers with the right skillsets for AIOps

"The study shows that AIOps is real and is delivering tangible benefits for enterprises and MSPs," said Suresh Vobbilesetty, EVP, Engineering at OpsRamp. "But it also shows that organizations' AIOps initiatives remain a work in progress and have a ways to go before they can realize the full potential of the technology."

Methodology: The study was conducted in December by a third party research firm, and includes input from 265 respondents who work at the general manager, director or vice president level at enterprises and MSPs in North America, Europe or Asia Pacific. All respondents have budget decision-making responsibilities for IT monitoring tools, and work at firms with at least $25 million in annual revenue and more than 500 employees.

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The Latest

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 ...

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.