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IT Trends 2018: The Intersection of Hype and Performance - Part 2

Leon Adato

If IT professionals want to be instrumental to their organization's successful digital transformation journey, they should continue prioritizing a hybrid IT environment while simultaneously developing new skillsets and leveraging emerging technologies.

To help IT professionals arm themselves with a new set of skills, technologies, and resources to bridge the leadership gap and manage the intersection of hype and performance, consider the following recommendations.

Start with: IT Trends 2018: The Intersection of Hype and Performance - Part 1

1. Concentrate on Containers

Because delivering organizational value is a constant goal, IT professionals should continue to prioritize container deployment, both from an investment and skills-development perspective.

For IT professionals seeking to concentrate on containers, they should first find out if the IT organization is already working with the technology. If it is, get to know the people involved and engage with them. If the IT organization is not working with containers, IT professionals can simply find resources or platforms online. There are also communities like GitHub that allow container experts to freely share their knowledge. Once IT professionals learn how containers work, they should start learning about container automation and orchestration to enable a bridge into scaling the integration and delivery of distributed apps and cloud deployments, all while opening a path to greater understanding of how those workloads are managed.

2. Cloud Power-Up

IT professionals are beginning to consume different service delivery models, like moving from Microsoft Exchange Servers to Office 365, and migrate more of their mission-critical applications to the cloud.

In parallel with these changes, there must be increased observability — leveraging combined metrics, logs, and application traces for controllability — built into an organization's cloud monitoring strategy. This degree of monitoring with discipline must carry forward the same level of granularity and source of truth that has existed in on-premises environments for decades. The key part of this process is establishing a baseline of observability within their hybrid IT environments across the entirety of their cloud-based applications.

3. Bridge the Leadership Gap

There will continue to be a great deal of excitement around ML and AI in the foreseeable future. As we saw with cloud, executives are eager to implement the technology, which promises the hyped benefits of disruptive innovation, and want to activate a new technology quickly without the experience to understand current capabilities, technical complexities, or deployment challenges. The best course of action for IT professionals is to become educators: identify ways to discuss the basics, the specific cost-benefit analysis of how the technology will benefit the business, and what it means for service integration and service delivery.

4. Embrace Resiliency and Reliability as Performance Metrics

To achieve digital transformation success, it's imperative that IT professionals begin to embrace resiliency and reliability of their environments as critical performance metrics.

Resiliency and reliability underscore the business value that IT professionals can bring to fruition for their organizations. They also represent measures of how well a distributed application was integrated and delivered; and because they also represent overall performance, these metrics translate into dollar values. With the stakes so high, the ability to ensure the end-user's digital experience is essential. IT should look to leverage tools that deliver full-stack observability into the logs, metrics, and tracing data that underpin reliability and resiliency metrics to ultimately optimize environments.

IT professionals must keep business leaders realistic about what technology implementations make the most sense for their organization. According to a recent report from Forrester, 55 percent of companies have not yet achieved tangible business outcomes from AI, and 43 percent say it's too soon to tell. AI and ML might not be a top priority today, but by focusing on optimizing cloud and hybrid IT environments right now, IT professionals can build the foundation for AI and ML while meeting the current needs of the business.

In 2018, IT professionals should balance optimizing the digital experience for end-users in hybrid IT environments with strategic decisions around which technologies their organization should invest in for business value beyond IT.

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

IT Trends 2018: The Intersection of Hype and Performance - Part 2

Leon Adato

If IT professionals want to be instrumental to their organization's successful digital transformation journey, they should continue prioritizing a hybrid IT environment while simultaneously developing new skillsets and leveraging emerging technologies.

To help IT professionals arm themselves with a new set of skills, technologies, and resources to bridge the leadership gap and manage the intersection of hype and performance, consider the following recommendations.

Start with: IT Trends 2018: The Intersection of Hype and Performance - Part 1

1. Concentrate on Containers

Because delivering organizational value is a constant goal, IT professionals should continue to prioritize container deployment, both from an investment and skills-development perspective.

For IT professionals seeking to concentrate on containers, they should first find out if the IT organization is already working with the technology. If it is, get to know the people involved and engage with them. If the IT organization is not working with containers, IT professionals can simply find resources or platforms online. There are also communities like GitHub that allow container experts to freely share their knowledge. Once IT professionals learn how containers work, they should start learning about container automation and orchestration to enable a bridge into scaling the integration and delivery of distributed apps and cloud deployments, all while opening a path to greater understanding of how those workloads are managed.

2. Cloud Power-Up

IT professionals are beginning to consume different service delivery models, like moving from Microsoft Exchange Servers to Office 365, and migrate more of their mission-critical applications to the cloud.

In parallel with these changes, there must be increased observability — leveraging combined metrics, logs, and application traces for controllability — built into an organization's cloud monitoring strategy. This degree of monitoring with discipline must carry forward the same level of granularity and source of truth that has existed in on-premises environments for decades. The key part of this process is establishing a baseline of observability within their hybrid IT environments across the entirety of their cloud-based applications.

3. Bridge the Leadership Gap

There will continue to be a great deal of excitement around ML and AI in the foreseeable future. As we saw with cloud, executives are eager to implement the technology, which promises the hyped benefits of disruptive innovation, and want to activate a new technology quickly without the experience to understand current capabilities, technical complexities, or deployment challenges. The best course of action for IT professionals is to become educators: identify ways to discuss the basics, the specific cost-benefit analysis of how the technology will benefit the business, and what it means for service integration and service delivery.

4. Embrace Resiliency and Reliability as Performance Metrics

To achieve digital transformation success, it's imperative that IT professionals begin to embrace resiliency and reliability of their environments as critical performance metrics.

Resiliency and reliability underscore the business value that IT professionals can bring to fruition for their organizations. They also represent measures of how well a distributed application was integrated and delivered; and because they also represent overall performance, these metrics translate into dollar values. With the stakes so high, the ability to ensure the end-user's digital experience is essential. IT should look to leverage tools that deliver full-stack observability into the logs, metrics, and tracing data that underpin reliability and resiliency metrics to ultimately optimize environments.

IT professionals must keep business leaders realistic about what technology implementations make the most sense for their organization. According to a recent report from Forrester, 55 percent of companies have not yet achieved tangible business outcomes from AI, and 43 percent say it's too soon to tell. AI and ML might not be a top priority today, but by focusing on optimizing cloud and hybrid IT environments right now, IT professionals can build the foundation for AI and ML while meeting the current needs of the business.

In 2018, IT professionals should balance optimizing the digital experience for end-users in hybrid IT environments with strategic decisions around which technologies their organization should invest in for business value beyond IT.

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...