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Four Key Pillars to a Leading Observability Practice

Mimi Shalash
Splunk

Splunk's latest research reveals that companies embracing observability aren't just keeping up, they're pulling ahead. Whether it's unlocking advantages across their digital infrastructure, achieving deeper understanding of their IT environments or uncovering faster insights, organizations are slashing through resolution times like never before.

The companies achieving the most powerful business outcomes are recognized as "observability leaders," excelling across the four critical stages of observability: foundational visibility, guided insights, proactive response, and unified workflows. Achieving "leader" status in observability unlocks more than just visibility because it empowers organizations with a deep, actionable understanding of their digital ecosystems. This translates directly into business returns, delivering a 2.6x annual ROI. In practical terms, that means smoother operations, fewer disruptions, and more time spent innovating rather than firefighting.

Let's explore the four key pillars that form the foundation of a world-class observability practice:

1. AI: A critical observability tool to help remediate issues

Automation is a cornerstone of an effective observability practice and elevates operational efficiency. As IT environments expand and grow increasingly complex, maintaining visibility over the full ecosystem of tools and technologies presents a growing challenge. Combine this with the volume of alerts that ITOps and engineering teams manage daily and the manual intervention becomes unsustainable. AI and machine learning (ML) solutions reduce the cognitive load off teams by dynamically recalibrating baseline metrics and detecting anomalies that static thresholds might overlook.

The latest data from Splunk highlights just how impactful AI and ML solutions can be with 85% of respondents reporting resolving at least half of their alerts. Additionally, 65% rely on AIOps for automated root cause analysis, giving teams the intelligence they need to stay ahead of issues.

Becoming an observability leader requires more than just basic monitoring. It requires advanced solutions that understand the normal patterns of your IT environment to then automatically detect anomalies. Consider a straightforward scenario: an unexpected CPU spike occurs on a cluster node, triggering an alert that requires quick, actionable insights. With an advanced observability tool, the system might recommend redistributing workloads across nodes to prevent service disruptions. Or it could initiate an automated remediation workflow via integrations with orchestration tools, ensuring rapid resolution without manual intervention.

2. Build a dedicated platform engineering team

Integrating AI and ML solutions is just one piece of what sets observability leaders apart. The competitive edge lies in how these technologies are embedded into platform engineering. Platform engineering isn't just about tools and processes, it's about empowering software engineers to do what they do best: create. By adopting standardized toolchains, workflows, and self-service platforms, teams minimize time spent managing tools because they've codified making the right thing to do, the easy thing to do.

The research shows that 73% of organizations are already embracing these practices and 58% of observability leaders recognize that this isn't just a trend. Rather, it's becoming the hallmark for continuous innovation, enabling seamless automation, scalability, and enhanced developer experience.

3. Harness control of the telemetry pipeline

At the heart of platform engineering, beyond automation and scalability, it's about maintaining control. Knowing exactly where your data flows, where it originates, and staying firmly in control of it isn't just important, it's essential. Ownership over data is the lifeline of every organization. Today, over 75% of observability leaders have adopted OpenTelemetry, solidifying it as the industry standard for collecting and controlling critical data. This open framework seamlessly integrates with other tools, bringing together data from multiple sources for complete visibility.

The result? A flexible observability practice that fosters innovation, reduces dependencies, and empowers businesses to grow on their own terms. With OpenTelemetry, you're not just building observability, you're building the freedom to evolve and innovate without limits.

Tapping into OpenTelemetry's full potential isn't just about adopting the technology. It also requires having the right talent in place and that comes with its own set of challenges. The report indicates that many companies are struggling with a shortage of people possessing the expertise on the open source project. To bridge the gap, organizations should prioritize training existing team members on how to configure, manage, and optimize OpenTelemetry pipelines and build this strategy into bullet #2.

4. Remember that observability is a team sport

Lastly, organizations must learn that true observability isn't achieved in isolation. Nearly three-quarters (73%) of observability leaders said they saw an improvement in their mean time to resolve (MTTR) after combining their observability and security workflows and tools. This highlights the importance of keeping observability tools connected across teams. When teams have cross-functional visibility into each other's workflows, they're better equipped to solve issues faster and prevent them from happening again.

While every team — observability and security — share the ultimate goal of business success, they may not share the same immediate objectives. Finding common tools and testing shared data sources can help iron out workflow differences, paving the way for future convergence.

Scaling with observability

As cloud reliance grows and AI tools multiply, IT environments will become even more complex, leaving organizations without full control over every tool interacting with their infrastructure. In other words, organizations must build a world-class observability framework to ensure that no part of their infrastructure operates without the necessary oversight. After all, every security professional knows … what you can't see can haunt you.

Observability practices don't just happen overnight. They require thoughtful planning, the right tools, and a continuous commitment to refining processes and integrating insights across teams. By focusing on enhancing the way people work, businesses can turn complexity into opportunity and maintain control in an ever-evolving digital landscape. Staying ahead of chaos is just good business sense — and the best way to firewall your future.

Mimi Shalash is Observability Advisor at Splunk, a Cisco company

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.

Four Key Pillars to a Leading Observability Practice

Mimi Shalash
Splunk

Splunk's latest research reveals that companies embracing observability aren't just keeping up, they're pulling ahead. Whether it's unlocking advantages across their digital infrastructure, achieving deeper understanding of their IT environments or uncovering faster insights, organizations are slashing through resolution times like never before.

The companies achieving the most powerful business outcomes are recognized as "observability leaders," excelling across the four critical stages of observability: foundational visibility, guided insights, proactive response, and unified workflows. Achieving "leader" status in observability unlocks more than just visibility because it empowers organizations with a deep, actionable understanding of their digital ecosystems. This translates directly into business returns, delivering a 2.6x annual ROI. In practical terms, that means smoother operations, fewer disruptions, and more time spent innovating rather than firefighting.

Let's explore the four key pillars that form the foundation of a world-class observability practice:

1. AI: A critical observability tool to help remediate issues

Automation is a cornerstone of an effective observability practice and elevates operational efficiency. As IT environments expand and grow increasingly complex, maintaining visibility over the full ecosystem of tools and technologies presents a growing challenge. Combine this with the volume of alerts that ITOps and engineering teams manage daily and the manual intervention becomes unsustainable. AI and machine learning (ML) solutions reduce the cognitive load off teams by dynamically recalibrating baseline metrics and detecting anomalies that static thresholds might overlook.

The latest data from Splunk highlights just how impactful AI and ML solutions can be with 85% of respondents reporting resolving at least half of their alerts. Additionally, 65% rely on AIOps for automated root cause analysis, giving teams the intelligence they need to stay ahead of issues.

Becoming an observability leader requires more than just basic monitoring. It requires advanced solutions that understand the normal patterns of your IT environment to then automatically detect anomalies. Consider a straightforward scenario: an unexpected CPU spike occurs on a cluster node, triggering an alert that requires quick, actionable insights. With an advanced observability tool, the system might recommend redistributing workloads across nodes to prevent service disruptions. Or it could initiate an automated remediation workflow via integrations with orchestration tools, ensuring rapid resolution without manual intervention.

2. Build a dedicated platform engineering team

Integrating AI and ML solutions is just one piece of what sets observability leaders apart. The competitive edge lies in how these technologies are embedded into platform engineering. Platform engineering isn't just about tools and processes, it's about empowering software engineers to do what they do best: create. By adopting standardized toolchains, workflows, and self-service platforms, teams minimize time spent managing tools because they've codified making the right thing to do, the easy thing to do.

The research shows that 73% of organizations are already embracing these practices and 58% of observability leaders recognize that this isn't just a trend. Rather, it's becoming the hallmark for continuous innovation, enabling seamless automation, scalability, and enhanced developer experience.

3. Harness control of the telemetry pipeline

At the heart of platform engineering, beyond automation and scalability, it's about maintaining control. Knowing exactly where your data flows, where it originates, and staying firmly in control of it isn't just important, it's essential. Ownership over data is the lifeline of every organization. Today, over 75% of observability leaders have adopted OpenTelemetry, solidifying it as the industry standard for collecting and controlling critical data. This open framework seamlessly integrates with other tools, bringing together data from multiple sources for complete visibility.

The result? A flexible observability practice that fosters innovation, reduces dependencies, and empowers businesses to grow on their own terms. With OpenTelemetry, you're not just building observability, you're building the freedom to evolve and innovate without limits.

Tapping into OpenTelemetry's full potential isn't just about adopting the technology. It also requires having the right talent in place and that comes with its own set of challenges. The report indicates that many companies are struggling with a shortage of people possessing the expertise on the open source project. To bridge the gap, organizations should prioritize training existing team members on how to configure, manage, and optimize OpenTelemetry pipelines and build this strategy into bullet #2.

4. Remember that observability is a team sport

Lastly, organizations must learn that true observability isn't achieved in isolation. Nearly three-quarters (73%) of observability leaders said they saw an improvement in their mean time to resolve (MTTR) after combining their observability and security workflows and tools. This highlights the importance of keeping observability tools connected across teams. When teams have cross-functional visibility into each other's workflows, they're better equipped to solve issues faster and prevent them from happening again.

While every team — observability and security — share the ultimate goal of business success, they may not share the same immediate objectives. Finding common tools and testing shared data sources can help iron out workflow differences, paving the way for future convergence.

Scaling with observability

As cloud reliance grows and AI tools multiply, IT environments will become even more complex, leaving organizations without full control over every tool interacting with their infrastructure. In other words, organizations must build a world-class observability framework to ensure that no part of their infrastructure operates without the necessary oversight. After all, every security professional knows … what you can't see can haunt you.

Observability practices don't just happen overnight. They require thoughtful planning, the right tools, and a continuous commitment to refining processes and integrating insights across teams. By focusing on enhancing the way people work, businesses can turn complexity into opportunity and maintain control in an ever-evolving digital landscape. Staying ahead of chaos is just good business sense — and the best way to firewall your future.

Mimi Shalash is Observability Advisor at Splunk, a Cisco company

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.