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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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For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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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.

Hot Topics

The Latest

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...