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IT Burnout is Real - Here's How Full-Stack Observability Can Help

Angie Mistretta
AppDynamics

You might have heard of the phrase "canary in the coal mine," which refers to an indicator of potential danger or failure ahead. In the tech industry, these potential failures could be catastrophic, as anomalies in applications negatively impact the performance, customer experience and business outcomes. As we have seen during this digital transformation boom during the pandemic, technologists are managing more applications and data than ever before, which has led three quarters of technologists to be concerned with increased IT complexity.

Even more significant, 89% admitted to feeling under immense pressure to keep up with the churn, according to the recent AppDynamics Agents of Transformation report. It's clear that the pandemic has pushed many technologists to their breaking point. To help tackle IT burnout, tech professionals need a "canary" to help them streamline and catch the anomalies before they cause any major performance issues.

The Canary is Full-Stack Observability

Businesses have fast-tracked their digital strategies, moving toward technology like cloud computing to meet storage and speed demands. As a result, technologists have not only had to continue to manage their initial applications running the business' digital properties, but now also the new applications from the cloud. For technologists to manage the deluge of data, relieve them from the pressure of having to manually monitor each domain of the IT stack, and catch and fix issues before they disrupt business, they need to consider implementing a full-stack observability strategy.

Full-stack observability enables technologists to have visibility into the full IT estate — current and new, on-prem, hybrid and/or cloud applications — and the ability to connect performance issues or updates directly to their effect on business outcomes. Full-stack observability allows for visibility, understanding, and optimization of what happens inside and beyond architecture.

The advantage of deep business context is that it speeds digital transformation by aligning teams around shared priorities and enables technologists to act with confidence on what matters most. Instead of trying to keep up with the complex and increasing amount of data running through the business' IT stack, technologists can observe everything and understand how what they do directly relates to the bigger picture.

Save Time, Money and Energy

In addition to saving the time that would have been spent on manually monitoring everything, the overview of the IT estate that full-stack observability provides also brings opportunities to save money and energy. When you're looking at your IT stack's infrastructure, network and security domains; you can identify which applications are performing well and the criticality of having access to business context, versus those that are potentially being underutilized or not producing valuable results. This allows you to redirect your finances accordingly, so you are getting your money's worth and optimizing all your applications.

Similarly, your IT team can relocate its energy to supporting the applications that are functioning properly and identify areas where there might be room for the business' digital strategy to innovate or expand.

IT burnout is real and on the heels of the past year's events, it is clear that digital transformation will only continue to evolve going forward. Now that most organizations have had to shift to a digital strategy, they must do more in order to stay competitive, as the bar for digital experiences has increased dramatically.

Give your team the support they deserve and help your business' digital strategy expand by starting the journey with full-stack observability and letting it serve as the "canary in the coal mine," giving teams visibility, correlation back to the app, as well as user and business experience which leads them on their path for success.

Angie Mistretta is CMO of AppDynamics, a part of Cisco

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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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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 ...

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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.

IT Burnout is Real - Here's How Full-Stack Observability Can Help

Angie Mistretta
AppDynamics

You might have heard of the phrase "canary in the coal mine," which refers to an indicator of potential danger or failure ahead. In the tech industry, these potential failures could be catastrophic, as anomalies in applications negatively impact the performance, customer experience and business outcomes. As we have seen during this digital transformation boom during the pandemic, technologists are managing more applications and data than ever before, which has led three quarters of technologists to be concerned with increased IT complexity.

Even more significant, 89% admitted to feeling under immense pressure to keep up with the churn, according to the recent AppDynamics Agents of Transformation report. It's clear that the pandemic has pushed many technologists to their breaking point. To help tackle IT burnout, tech professionals need a "canary" to help them streamline and catch the anomalies before they cause any major performance issues.

The Canary is Full-Stack Observability

Businesses have fast-tracked their digital strategies, moving toward technology like cloud computing to meet storage and speed demands. As a result, technologists have not only had to continue to manage their initial applications running the business' digital properties, but now also the new applications from the cloud. For technologists to manage the deluge of data, relieve them from the pressure of having to manually monitor each domain of the IT stack, and catch and fix issues before they disrupt business, they need to consider implementing a full-stack observability strategy.

Full-stack observability enables technologists to have visibility into the full IT estate — current and new, on-prem, hybrid and/or cloud applications — and the ability to connect performance issues or updates directly to their effect on business outcomes. Full-stack observability allows for visibility, understanding, and optimization of what happens inside and beyond architecture.

The advantage of deep business context is that it speeds digital transformation by aligning teams around shared priorities and enables technologists to act with confidence on what matters most. Instead of trying to keep up with the complex and increasing amount of data running through the business' IT stack, technologists can observe everything and understand how what they do directly relates to the bigger picture.

Save Time, Money and Energy

In addition to saving the time that would have been spent on manually monitoring everything, the overview of the IT estate that full-stack observability provides also brings opportunities to save money and energy. When you're looking at your IT stack's infrastructure, network and security domains; you can identify which applications are performing well and the criticality of having access to business context, versus those that are potentially being underutilized or not producing valuable results. This allows you to redirect your finances accordingly, so you are getting your money's worth and optimizing all your applications.

Similarly, your IT team can relocate its energy to supporting the applications that are functioning properly and identify areas where there might be room for the business' digital strategy to innovate or expand.

IT burnout is real and on the heels of the past year's events, it is clear that digital transformation will only continue to evolve going forward. Now that most organizations have had to shift to a digital strategy, they must do more in order to stay competitive, as the bar for digital experiences has increased dramatically.

Give your team the support they deserve and help your business' digital strategy expand by starting the journey with full-stack observability and letting it serve as the "canary in the coal mine," giving teams visibility, correlation back to the app, as well as user and business experience which leads them on their path for success.

Angie Mistretta is CMO of AppDynamics, a part of Cisco

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.