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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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Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

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Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...

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

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...