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

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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

Related Links

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Gartner: Regular AI System Assessments Triple the Likelihood of High GenAI Value

Organizations that perform regular audits and assessments of AI system performance and compliance are over three times more likely to achieve high GenAI value than organizations that do not, according to a survey by Gartner ...

Beyond Autoscaling: What True Kubernetes Optimization Actually Requires

Kubernetes has become the backbone of cloud infrastructure, but it's also one of its biggest cost drivers. Recent research shows that 98% of senior IT leaders say Kubernetes now drives cloud spend, yet 91% still can't optimize it effectively. After years of adoption, most organizations have moved past discovery. They know container sprawl, idle resources and reactive scaling inflate costs. What they don't know is how to fix it ...

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Artificial intelligence is no longer a future investment. It's already embedded in how we work — whether through copilots in productivity apps, real-time transcription tools in meetings, or machine learning models fueling analytics and personalization. But while enterprise adoption accelerates, there's one critical area many leaders have yet to examine: Can your network actually support AI at the speed your users expect? ...

Keeping Your Business Stable When Going Through an IT Disaster

The more technology businesses invest in, the more potential attack surfaces they have that can be exploited. Without the right continuity plans in place, the disruptions caused by these attacks can bring operations to a standstill and cause irreparable damage to an organization. It's essential to take the time now to ensure your business has the right tools, processes, and recovery initiatives in place to weather any type of IT disaster that comes up. Here are some effective strategies you can follow to achieve this ...

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In today's fast-paced AI landscape, CIOs, IT leaders, and engineers are constantly challenged to manage increasingly complex and interconnected systems. The sheer scale and velocity of data generated by modern infrastructure can be overwhelming, making it difficult to maintain uptime, prevent outages, and create a seamless customer experience. This complexity is magnified by the industry's shift towards agentic AI ...

MEAN TIME TO INSIGHT Podcast - Episode 19: The AWS Outage

In MEAN TIME TO INSIGHT Episode 19, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA explains the cause of the AWS outage in October ... 

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The explosion of generative AI and machine learning capabilities has fundamentally changed the conversation around cloud migration. It's no longer just about modernization or cost savings — it's about being able to compete in a market where AI is rapidly becoming table stakes. Companies that can't quickly spin up AI workloads, feed models with data at scale, or experiment with new capabilities are falling behind faster than ever before. But here's what I'm seeing: many organizations want to capitalize on AI, but they're stuck ...

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On September 16, the world celebrated the 10th annual IT Pro Day, giving companies a chance to laud the professionals who serve as the backbone to almost every successful business across the globe. Despite the growing importance of their roles, many IT pros still work in the background and often go underappreciated ...

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Artificial Intelligence (AI) is reshaping observability, and observability is becoming essential for AI. This is a two-way relationship that is increasingly relevant as enterprises scale generative AI ... This dual role makes AI and observability inseparable. In this blog, I cover more details of each side ...

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Poor DEX directly costs global businesses an average of 470,000 hours per year, equivalent to around 226 full-time employees, according to a new report from Nexthink, Cracking the DEX Equation: The Annual Workplace Productivity Report. This indicates that digital friction is a vital and underreported element of the global productivity crisis ...