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What it Takes for Today's Organizations to Achieve Operational Resilience

Sean Sebring
SolarWinds

Over the past year, I spent a good amount of time thinking about operational resilience. I asked myself what does it mean? Why is it so important, especially now?

My colleagues and I define operational resilience as the ability to identify, anticipate, and mitigate risks to help prevent future issues while accelerating responsiveness to ongoing disruptions when they do occur. It is achieved by understanding the different parts of the business and how they interact across teams, workflows, and tools, while driving a culture of intentional learning and adaptation.

Adequately preventing and responding to disruptions has never been more important — or more possible. The growing ubiquity of AI has introduced more automated workstreams and increased productivity, while simultaneously creating a greater need for better data management. As customer expectations increasingly align with always-on services, the ability to prevent and recover from disruptions has direct ties to a business's bottom line.

Recent data from the SolarWinds IT Trends Report 2025, which surveyed more than 600 IT leaders and professionals, suggests nine in 10 IT teams believe they're resilient. However, a closer inspection of the data indicates a more complex reality. Many organizations still have room to improve their operational reliance and prepare for an AI-driven, data-intensive future.

The Complex Reality of Today's IT Teams

While these organizations consider themselves resilient, survey respondents pointed to a lack of confidence in their ability to handle certain core IT functions. For example, only 26% of IT leaders were confident they could sufficiently handle bring-your-own-device practices. Less than half of IT leaders felt confident they could manage increasing user expectations (36%), support artificial intelligence (38%), and manage remote and distributed workforces (45%). A little more than half, 52%, felt they could sufficiently deal with cyberthreats.

An operationally resilient organization must be able to handle these functions. For example, if employees or third-party contractors are bringing their own devices onto your network, your IT systems will require proper security policies to help ensure those parties aren't introducing malicious content or data into your network. If today's organizations aren't able to adequately implement and support the use of AI, they run the risks of shadow AI use or experiencing competitive disadvantages in their respective markets.  

Speaking of competitive disadvantages, the report also highlighted how sub-par operational resilience can lead to reputational harm. More than one quarter (28%) of IT leaders said service outages can cause brand damage. A hit to public image can have cascading effects, causing consumers to take their business elsewhere and leading to both short-term and long-term revenue loss.

Why Organizations Are Facing Gaps in Their IT Operations

When facing issues with an IT environment, the most natural — and even logical — step is to expand IT capabilities. However, IT leaders in the report said their issues weren't solely technology-based. In fact, for some teams, tools are the least important issue. More IT leaders cited workflows (51%) and the size of their teams (36%) as the biggest hindrances to exercising operational resilience during disruption. This is a great reminder that, although a system disruption may begin as a technology issue, the resilience necessary to respond is neither technology-only nor technology-first.

Organizations face an inability to measure operational resilience as well as the additional challenge of practicing operational resilience. According to the survey, 3 in 10 IT teams spend half their time resolving critical issues. The only way to reduce these numbers is to know how long it takes to reach resolution and recover after an incident occurs.

Many teams view incident management and response times as a great way to measure IT performance. This often translates to use of the MTTx metric, also known as mean time to detect, mean time to acknowledge and or mean time to resolve.

Almost half of the respondents (45%) said they didn't use MTTx for multiple reasons, such as a lack of awareness, difficulty measuring accurately, or a preference for alternative metrics. Regardless, sufficient and prompt MTTx is a strong measure for operational resilience.

Improving Operational Resilience

To take operational resilience from insufficient to excellent, organizations must build their IT frameworks on solid relationships, streamlined processes, and comprehensive tooling.

A focus on relationships should extend to both technology and teams. IT leaders can look to comprehensive observability software to view how each IT asset, piece of data, and login credentials relate to each other. This help leaders create a map to describe the causes and effects within a system if a disruption occurs. Similar to tooling, it's also important to map relationships between team members. When you understand the relationships between team and technology, you can discern which assets and workflows are most important and which require the highest priority.

Once you outline relationships, you can begin delving into processes. A good way to figure out what's working and what isn't, is by surveying IT team members. They can best describe areas with communication problems, antithetical working styles, or a lack of necessary expertise. You may find you need to move team members around or decide that teamwork is great but could benefit from better tooling.

If tooling is part of the solution, it's important to meet with leadership to implement technology that is helpful, addresses team needs, and aligns with business goals. For example, if you have an IT team that has historically suffered from alert fatigue and disjointed incident management, the team may benefit from tooling that centralizes incident response and helps isolate and identify the most critical issues. This creates focus and streamlined processes that can enhance teamwide operational resilience.

When organizations can improve their tools, teams and processes, they can create a culture of operational resilience that breaks down silos and efficiently responds in the face of disruption. 

Sean Sebring is Solutions Engineering Manager at SolarWinds

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Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

What it Takes for Today's Organizations to Achieve Operational Resilience

Sean Sebring
SolarWinds

Over the past year, I spent a good amount of time thinking about operational resilience. I asked myself what does it mean? Why is it so important, especially now?

My colleagues and I define operational resilience as the ability to identify, anticipate, and mitigate risks to help prevent future issues while accelerating responsiveness to ongoing disruptions when they do occur. It is achieved by understanding the different parts of the business and how they interact across teams, workflows, and tools, while driving a culture of intentional learning and adaptation.

Adequately preventing and responding to disruptions has never been more important — or more possible. The growing ubiquity of AI has introduced more automated workstreams and increased productivity, while simultaneously creating a greater need for better data management. As customer expectations increasingly align with always-on services, the ability to prevent and recover from disruptions has direct ties to a business's bottom line.

Recent data from the SolarWinds IT Trends Report 2025, which surveyed more than 600 IT leaders and professionals, suggests nine in 10 IT teams believe they're resilient. However, a closer inspection of the data indicates a more complex reality. Many organizations still have room to improve their operational reliance and prepare for an AI-driven, data-intensive future.

The Complex Reality of Today's IT Teams

While these organizations consider themselves resilient, survey respondents pointed to a lack of confidence in their ability to handle certain core IT functions. For example, only 26% of IT leaders were confident they could sufficiently handle bring-your-own-device practices. Less than half of IT leaders felt confident they could manage increasing user expectations (36%), support artificial intelligence (38%), and manage remote and distributed workforces (45%). A little more than half, 52%, felt they could sufficiently deal with cyberthreats.

An operationally resilient organization must be able to handle these functions. For example, if employees or third-party contractors are bringing their own devices onto your network, your IT systems will require proper security policies to help ensure those parties aren't introducing malicious content or data into your network. If today's organizations aren't able to adequately implement and support the use of AI, they run the risks of shadow AI use or experiencing competitive disadvantages in their respective markets.  

Speaking of competitive disadvantages, the report also highlighted how sub-par operational resilience can lead to reputational harm. More than one quarter (28%) of IT leaders said service outages can cause brand damage. A hit to public image can have cascading effects, causing consumers to take their business elsewhere and leading to both short-term and long-term revenue loss.

Why Organizations Are Facing Gaps in Their IT Operations

When facing issues with an IT environment, the most natural — and even logical — step is to expand IT capabilities. However, IT leaders in the report said their issues weren't solely technology-based. In fact, for some teams, tools are the least important issue. More IT leaders cited workflows (51%) and the size of their teams (36%) as the biggest hindrances to exercising operational resilience during disruption. This is a great reminder that, although a system disruption may begin as a technology issue, the resilience necessary to respond is neither technology-only nor technology-first.

Organizations face an inability to measure operational resilience as well as the additional challenge of practicing operational resilience. According to the survey, 3 in 10 IT teams spend half their time resolving critical issues. The only way to reduce these numbers is to know how long it takes to reach resolution and recover after an incident occurs.

Many teams view incident management and response times as a great way to measure IT performance. This often translates to use of the MTTx metric, also known as mean time to detect, mean time to acknowledge and or mean time to resolve.

Almost half of the respondents (45%) said they didn't use MTTx for multiple reasons, such as a lack of awareness, difficulty measuring accurately, or a preference for alternative metrics. Regardless, sufficient and prompt MTTx is a strong measure for operational resilience.

Improving Operational Resilience

To take operational resilience from insufficient to excellent, organizations must build their IT frameworks on solid relationships, streamlined processes, and comprehensive tooling.

A focus on relationships should extend to both technology and teams. IT leaders can look to comprehensive observability software to view how each IT asset, piece of data, and login credentials relate to each other. This help leaders create a map to describe the causes and effects within a system if a disruption occurs. Similar to tooling, it's also important to map relationships between team members. When you understand the relationships between team and technology, you can discern which assets and workflows are most important and which require the highest priority.

Once you outline relationships, you can begin delving into processes. A good way to figure out what's working and what isn't, is by surveying IT team members. They can best describe areas with communication problems, antithetical working styles, or a lack of necessary expertise. You may find you need to move team members around or decide that teamwork is great but could benefit from better tooling.

If tooling is part of the solution, it's important to meet with leadership to implement technology that is helpful, addresses team needs, and aligns with business goals. For example, if you have an IT team that has historically suffered from alert fatigue and disjointed incident management, the team may benefit from tooling that centralizes incident response and helps isolate and identify the most critical issues. This creates focus and streamlined processes that can enhance teamwide operational resilience.

When organizations can improve their tools, teams and processes, they can create a culture of operational resilience that breaks down silos and efficiently responds in the face of disruption. 

Sean Sebring is Solutions Engineering Manager at SolarWinds

Hot Topics

The Latest

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...