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Taking Action Against the Data You Have

Adam Frank
Moogsoft

Move fast and break things: A phrase that has been a rallying cry for many SREs and DevOps practitioners. After all, these teams are charged with delivering rapid and unceasing innovation to wow customers and keep pace with competitors.

But today's society doesn't tolerate broken things (aka downtime). So, what if you can move fast and not break things? Or at least, move fast and rapidly identify or even predict broken things?

It's high time to rethink the old rallying cry, and with AI and observability working in tandem, it's possible.

Applying AI to observability data turns mountains of telemetry data, regardless of the relative size of the mountain to your business, into actionable information, playing a critical role in how quickly an organization can innovate. Let's explore why these solutions are so essential.

How AI and Observability Converge to Help

DevOps practitioners strive to provide superior digital experiences by continuously delivering and integrating features, fixes and functionalities for immersive experiences. This constant behind-the-scenes innovation is at odds with customers' expectations for 100% availability. Today's consumer expects to purchase, transact, interact and access on-demand digital services with zero downtime.

SREs and DevOps teams need AI-driven observability to monitor system performance or innovation and productivity plummets. Teams spend entire days managing alerts and fighting fires. And even with an infinite amount of time to shift through data, today's distributed IT systems, virtual computing and ephemeral machines are simply too complex and interdependent for the human mind to monitor manually.

Only automated intelligence can constantly verify and restore digital products and services in modern IT architectures. And only AI and ML can create a continual learning cycle, understanding more from the data gathered across infrastructures, applications and services. These insights build more system reliability, but because nothing can fully protect against outages happening, they also allow IT teams to resolve incidents rapidly when they do occur.

SREs, DevOps Practitioners ... and Astronauts?

When incidents arise and systems fail, the stakes are high for SREs and DevOps practitioners to right the ship — and fast. For every minute of downtime, businesses suffer exponential losses, like tanking stocks, tarnished reputations and disillusioned customers. But teams also need to remain cool under mounting pressure to work efficiently.

How? In one word: knowledge.

I recently read Chirs Hadfield's book An Astronaut's Guide to Life on Earth. Although I only wish people thought of IT teams as superheroes, the author's advice resonated:

"People tend to think astronauts have the courage of a superhero — or maybe the emotional range of a robot. But in order to stay calm in a high-stress, high-stakes situation, all you really need is knowledge."

Under pressure to tackle a challenging system failure, knowledge also allows SREs and DevOps teams to overcome emotions and find solutions. And that's precisely where intelligent observability comes in: it gathers data produced from apps and services, adds context and turns volumes of information into actionable knowledge.

Automate the Cognitive Load

The benefits of automation don't stop at speedy fixes. Automating the toil out of observability provides economic value by freeing teams to accelerate innovation and provide measurable value. Teams can focus more on development and less on ops with little mundane work to accomplish.

Intelligent observability also reduces stress and burnout prevalent among IT teams. AI-driven observability platforms reduce alert noise and focus teams on the incidents that matter for triage and remediation.

And, for businesses, intelligent observability assures the quality of the customer experience, which is ultimately what matters most.

Welcome to the era of move fast and break things infrequently. Although less catchy than the original, it's more reflective of the automated intelligence today's software-defined world needs to deliver superior customer experiences. Every business's revenue, reputation and growth depend on it.

Adam Frank is VP, Product & Design, at Moogsoft

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.

Taking Action Against the Data You Have

Adam Frank
Moogsoft

Move fast and break things: A phrase that has been a rallying cry for many SREs and DevOps practitioners. After all, these teams are charged with delivering rapid and unceasing innovation to wow customers and keep pace with competitors.

But today's society doesn't tolerate broken things (aka downtime). So, what if you can move fast and not break things? Or at least, move fast and rapidly identify or even predict broken things?

It's high time to rethink the old rallying cry, and with AI and observability working in tandem, it's possible.

Applying AI to observability data turns mountains of telemetry data, regardless of the relative size of the mountain to your business, into actionable information, playing a critical role in how quickly an organization can innovate. Let's explore why these solutions are so essential.

How AI and Observability Converge to Help

DevOps practitioners strive to provide superior digital experiences by continuously delivering and integrating features, fixes and functionalities for immersive experiences. This constant behind-the-scenes innovation is at odds with customers' expectations for 100% availability. Today's consumer expects to purchase, transact, interact and access on-demand digital services with zero downtime.

SREs and DevOps teams need AI-driven observability to monitor system performance or innovation and productivity plummets. Teams spend entire days managing alerts and fighting fires. And even with an infinite amount of time to shift through data, today's distributed IT systems, virtual computing and ephemeral machines are simply too complex and interdependent for the human mind to monitor manually.

Only automated intelligence can constantly verify and restore digital products and services in modern IT architectures. And only AI and ML can create a continual learning cycle, understanding more from the data gathered across infrastructures, applications and services. These insights build more system reliability, but because nothing can fully protect against outages happening, they also allow IT teams to resolve incidents rapidly when they do occur.

SREs, DevOps Practitioners ... and Astronauts?

When incidents arise and systems fail, the stakes are high for SREs and DevOps practitioners to right the ship — and fast. For every minute of downtime, businesses suffer exponential losses, like tanking stocks, tarnished reputations and disillusioned customers. But teams also need to remain cool under mounting pressure to work efficiently.

How? In one word: knowledge.

I recently read Chirs Hadfield's book An Astronaut's Guide to Life on Earth. Although I only wish people thought of IT teams as superheroes, the author's advice resonated:

"People tend to think astronauts have the courage of a superhero — or maybe the emotional range of a robot. But in order to stay calm in a high-stress, high-stakes situation, all you really need is knowledge."

Under pressure to tackle a challenging system failure, knowledge also allows SREs and DevOps teams to overcome emotions and find solutions. And that's precisely where intelligent observability comes in: it gathers data produced from apps and services, adds context and turns volumes of information into actionable knowledge.

Automate the Cognitive Load

The benefits of automation don't stop at speedy fixes. Automating the toil out of observability provides economic value by freeing teams to accelerate innovation and provide measurable value. Teams can focus more on development and less on ops with little mundane work to accomplish.

Intelligent observability also reduces stress and burnout prevalent among IT teams. AI-driven observability platforms reduce alert noise and focus teams on the incidents that matter for triage and remediation.

And, for businesses, intelligent observability assures the quality of the customer experience, which is ultimately what matters most.

Welcome to the era of move fast and break things infrequently. Although less catchy than the original, it's more reflective of the automated intelligence today's software-defined world needs to deliver superior customer experiences. Every business's revenue, reputation and growth depend on it.

Adam Frank is VP, Product & Design, at Moogsoft

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.