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2019 IT Predictions: Applications Remain Top of Mind, AIOps Meets Reality

Len Rosenthal

This time last year, we predicted that IT managers were going to move away from the "hybrid data center" and finally realize the reality of the "hybrid application" – the concept that there are multiple components to a single application, living in different data centers and on different infrastructure types. And this year, we saw that prediction of an increased focus on applications come to pass, as organizations increasingly made buying and deployment decisions based on the needs of their applications. This also resulted in many organizations pulling workloads from the public cloud and redeploying them on-premises, due to an increased understanding the workload requirements and performance-focused SLAs.

It's become clear that not only do an organization's applications drive the business, but they actually are the business. As we move into 2019, the application will continue to be the focus of the conversation, but it will also evolve to be the central driver of IT, both from a workload placement perspective and from an operations management angle. IT departments are continuously trying to contextualize the information and insights provided by these applications, but this is much easier said than done. The problem is that many organizations lack real-time application-aware monitoring capabilities, leading to a limited understanding of how applications are interacting with the various infrastructure components. As a result, IT departments continue to "fly blind" when it comes to allocating their on-prem and cloud-based infrastructure resources to support the number one priority: customer-facing applications.

One technology hitting the headlines lately is AIOps, Gartner's category name for Artificial Intelligence and Machine Learning-assisted operations. If 2018 was the year of aggressively marketing these technologies, 2019 will be the year of cutting through the hype and revealing their true value when actually applied in a meaningful manner. This is crucial, as organizations are slowly but surely understanding that AIOps may not be the "easy button" they initially thought it was.

While some AIOps solutions have promised to relieve tool fatigue and make sense of the onslaught of data and alerts constantly berating IT practitioners, AIOps unfortunately isn't a "set it and forget it" solution – quite the opposite, in fact. Context and efficient integrations with existing systems are paramount to successful AIOps, and more and more organizations will soon discover that an algorithm combined with corollary alerts does not fix everything.

Much like the hype cycle we experienced with the cloud in the past decade, we're now starting to move past the buzzword phase and into the reality of meaningful AIOps initiatives. According to Gartner, we should expect to see more I&O leaders initiating AIOps deployments over the next two to five years, with most organizations looking to augment their IT service management and overall automation strategies.

Adoption of AIOps technologies didn't pan out the way IT vendors may have anticipated in 2018, and that tends to happen when the "solution" really isn't a solution at all, but rather an incremental feature or capability that's dressed up by buzzwords and marketing-speak. However, with organizations becoming more savvy about combining real-time monitoring with AIOps in 2019 and beyond, buying decisions will shift and the adoption of true application-aware AIOps will emerge in 2019 – resulting in more successful deployments of the technology.

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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 ...

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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 ...

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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.

2019 IT Predictions: Applications Remain Top of Mind, AIOps Meets Reality

Len Rosenthal

This time last year, we predicted that IT managers were going to move away from the "hybrid data center" and finally realize the reality of the "hybrid application" – the concept that there are multiple components to a single application, living in different data centers and on different infrastructure types. And this year, we saw that prediction of an increased focus on applications come to pass, as organizations increasingly made buying and deployment decisions based on the needs of their applications. This also resulted in many organizations pulling workloads from the public cloud and redeploying them on-premises, due to an increased understanding the workload requirements and performance-focused SLAs.

It's become clear that not only do an organization's applications drive the business, but they actually are the business. As we move into 2019, the application will continue to be the focus of the conversation, but it will also evolve to be the central driver of IT, both from a workload placement perspective and from an operations management angle. IT departments are continuously trying to contextualize the information and insights provided by these applications, but this is much easier said than done. The problem is that many organizations lack real-time application-aware monitoring capabilities, leading to a limited understanding of how applications are interacting with the various infrastructure components. As a result, IT departments continue to "fly blind" when it comes to allocating their on-prem and cloud-based infrastructure resources to support the number one priority: customer-facing applications.

One technology hitting the headlines lately is AIOps, Gartner's category name for Artificial Intelligence and Machine Learning-assisted operations. If 2018 was the year of aggressively marketing these technologies, 2019 will be the year of cutting through the hype and revealing their true value when actually applied in a meaningful manner. This is crucial, as organizations are slowly but surely understanding that AIOps may not be the "easy button" they initially thought it was.

While some AIOps solutions have promised to relieve tool fatigue and make sense of the onslaught of data and alerts constantly berating IT practitioners, AIOps unfortunately isn't a "set it and forget it" solution – quite the opposite, in fact. Context and efficient integrations with existing systems are paramount to successful AIOps, and more and more organizations will soon discover that an algorithm combined with corollary alerts does not fix everything.

Much like the hype cycle we experienced with the cloud in the past decade, we're now starting to move past the buzzword phase and into the reality of meaningful AIOps initiatives. According to Gartner, we should expect to see more I&O leaders initiating AIOps deployments over the next two to five years, with most organizations looking to augment their IT service management and overall automation strategies.

Adoption of AIOps technologies didn't pan out the way IT vendors may have anticipated in 2018, and that tends to happen when the "solution" really isn't a solution at all, but rather an incremental feature or capability that's dressed up by buzzwords and marketing-speak. However, with organizations becoming more savvy about combining real-time monitoring with AIOps in 2019 and beyond, buying decisions will shift and the adoption of true application-aware AIOps will emerge in 2019 – resulting in more successful deployments of the technology.

Hot Topics

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.