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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

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

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

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...