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Maximizing Resilience: Insights from the 2025 SRE Report

Leo Vasiliou
Catchpoint

As the digital landscape expands, the stakes for delivering reliable and seamless online experiences have never been higher. In the past year, site reliability engineering (SRE) has continued to evolve into a critical driver of operational success, shaping how organizations approach resilience, collaboration, and customer satisfaction.

The 2025 Catchpoint SRE Report dives into the forces transforming the SRE landscape, exploring both the challenges and opportunities ahead. Let's break down the key findings and what they mean for SRE professionals and the businesses relying on them.

Slow Is the New Down

Performance is about more than just uptime; it's also about speed. This year's report reveals that 53% of organizations believe poor performance is as harmful as downtime, making user experience a critical reliability metric.

What This Means for You: Organizations must elevate their performance monitoring strategies to include experience level objectives (XLOs) for ensuring fast and seamless digital interactions. Proactive performance tuning and real-time observability can mitigate the impact of "slow" on end users.

Toil Levels Are Rising Despite AI

After years of decline, toil — the manual, repetitive tasks that consume engineering resources — has ticked upward. The median reported percentage of work spent on toil rose to 30% from 25% in 2024 causing us to hypothesize whether AI is filling our time with more — instead of less — operational workload.

Why It Matters: This hypothesis suggests that while AI is improving specific workflows, it hasn't eliminated the burden of toil. Teams should evaluate their AI implementations to ensure they target high-impact areas and actively reduce manual effort. As Laura de Vesine, one of this year's report contributors put it: AI is at best "a co-worker you can't trust." Even as AI tools become more integrated into workflows, human oversight and intervention remain critical to ensure these tools don't inadvertently add to the complexity of tasks.

Organizational Priorities Under Pressure

The tension between agility and stability persists. Over two-thirds of respondents reported feeling pressured to prioritize release schedules over reliability, highlighting the ongoing challenge of balancing speed with resilience.

Takeaway: Building a culture that values reliability alongside agility requires clear communication and alignment on priorities. Teams should integrate reliability metrics into performance evaluations and emphasize the long-term benefits of stable releases for both IT and the business.

Monitoring Tools: More Is More

The report found that most organizations use between 2-10 monitoring or observability tools, showing a "value over cost" mindset for effective oversight across complex technology stacks.

What This Means for You: While multiple tools can provide comprehensive coverage, they also introduce complexity. Organizations should focus on integrating these tools to provide unified visibility and actionable insights without overwhelming their teams.

AI Training Universally in High Demand, but Time-Constrained

As AI continues to shape the SRE landscape, 30% of respondents prioritized technical training on AI — a strong indicator of the desire to upskill. However, the top sentiment (37%) reflected caution, as teams balance enthusiasm for AI with practical implementation concerns.

Takeaway: Providing targeted, hands-on training programs can help bridge the knowledge gap and build confidence in AI's capabilities. Organizations should also set realistic expectations for AI adoption, ensuring a smooth transition into daily workflows.

Incidents Are a Certainty

Incident response remains a universal challenge, with 40% of respondents handling between 1 and 5 incidents in the last 30 days. Notably, incident management is a shared responsibility, with higher-level managers as involved as individual contributors.

Why This Matters: Teams should adopt a collaborative approach to incident response, leveraging diverse perspectives to address issues effectively. Implementing clear incident playbooks and blameless post-mortem practices can further enhance preparedness and learning.

Misalignment on Reliability Priorities

While the overall responses paint a positive picture of reliability practices, significant gaps emerge when analyzed by managerial responsibility. Misalignment on priorities and approaches remains a challenge.

Takeaway: Bridging this IT-to-business gap requires the acknowledgment of its existence. Ongoing dialogue, alignment across all levels of the organization, and regularly revisiting and communicating reliability goals can help ensure everyone is pulling in the same direction.

Ownership and Action in SRE

The report shows just how important it is to connect technical work with the bigger picture. It all comes down to teams knowing how their efforts make a real difference and taking thoughtful steps to grab the opportunities in front of them. This year's report sheds light on the ongoing challenges that need attention, like making reliability a part of release planning, giving teams the tools and training they need to tackle incidents smoothly, and getting everyone on the same page, from leadership to contributors.

When it comes to AI, the focus should be on using it in practical ways that actually make work easier rather than more complicated. Building resilience and reliability isn't just about technical know-how. It's about clear goals, teamwork, and always looking for ways to improve. Companies that see SRE as a way to drive real outcomes, rather than just a set of technical tasks, will be in a great spot to succeed as the digital world keeps getting more complex and fast-paced.

Leo Vasiliou is Director of Product Marketing at Catchpoint

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

Maximizing Resilience: Insights from the 2025 SRE Report

Leo Vasiliou
Catchpoint

As the digital landscape expands, the stakes for delivering reliable and seamless online experiences have never been higher. In the past year, site reliability engineering (SRE) has continued to evolve into a critical driver of operational success, shaping how organizations approach resilience, collaboration, and customer satisfaction.

The 2025 Catchpoint SRE Report dives into the forces transforming the SRE landscape, exploring both the challenges and opportunities ahead. Let's break down the key findings and what they mean for SRE professionals and the businesses relying on them.

Slow Is the New Down

Performance is about more than just uptime; it's also about speed. This year's report reveals that 53% of organizations believe poor performance is as harmful as downtime, making user experience a critical reliability metric.

What This Means for You: Organizations must elevate their performance monitoring strategies to include experience level objectives (XLOs) for ensuring fast and seamless digital interactions. Proactive performance tuning and real-time observability can mitigate the impact of "slow" on end users.

Toil Levels Are Rising Despite AI

After years of decline, toil — the manual, repetitive tasks that consume engineering resources — has ticked upward. The median reported percentage of work spent on toil rose to 30% from 25% in 2024 causing us to hypothesize whether AI is filling our time with more — instead of less — operational workload.

Why It Matters: This hypothesis suggests that while AI is improving specific workflows, it hasn't eliminated the burden of toil. Teams should evaluate their AI implementations to ensure they target high-impact areas and actively reduce manual effort. As Laura de Vesine, one of this year's report contributors put it: AI is at best "a co-worker you can't trust." Even as AI tools become more integrated into workflows, human oversight and intervention remain critical to ensure these tools don't inadvertently add to the complexity of tasks.

Organizational Priorities Under Pressure

The tension between agility and stability persists. Over two-thirds of respondents reported feeling pressured to prioritize release schedules over reliability, highlighting the ongoing challenge of balancing speed with resilience.

Takeaway: Building a culture that values reliability alongside agility requires clear communication and alignment on priorities. Teams should integrate reliability metrics into performance evaluations and emphasize the long-term benefits of stable releases for both IT and the business.

Monitoring Tools: More Is More

The report found that most organizations use between 2-10 monitoring or observability tools, showing a "value over cost" mindset for effective oversight across complex technology stacks.

What This Means for You: While multiple tools can provide comprehensive coverage, they also introduce complexity. Organizations should focus on integrating these tools to provide unified visibility and actionable insights without overwhelming their teams.

AI Training Universally in High Demand, but Time-Constrained

As AI continues to shape the SRE landscape, 30% of respondents prioritized technical training on AI — a strong indicator of the desire to upskill. However, the top sentiment (37%) reflected caution, as teams balance enthusiasm for AI with practical implementation concerns.

Takeaway: Providing targeted, hands-on training programs can help bridge the knowledge gap and build confidence in AI's capabilities. Organizations should also set realistic expectations for AI adoption, ensuring a smooth transition into daily workflows.

Incidents Are a Certainty

Incident response remains a universal challenge, with 40% of respondents handling between 1 and 5 incidents in the last 30 days. Notably, incident management is a shared responsibility, with higher-level managers as involved as individual contributors.

Why This Matters: Teams should adopt a collaborative approach to incident response, leveraging diverse perspectives to address issues effectively. Implementing clear incident playbooks and blameless post-mortem practices can further enhance preparedness and learning.

Misalignment on Reliability Priorities

While the overall responses paint a positive picture of reliability practices, significant gaps emerge when analyzed by managerial responsibility. Misalignment on priorities and approaches remains a challenge.

Takeaway: Bridging this IT-to-business gap requires the acknowledgment of its existence. Ongoing dialogue, alignment across all levels of the organization, and regularly revisiting and communicating reliability goals can help ensure everyone is pulling in the same direction.

Ownership and Action in SRE

The report shows just how important it is to connect technical work with the bigger picture. It all comes down to teams knowing how their efforts make a real difference and taking thoughtful steps to grab the opportunities in front of them. This year's report sheds light on the ongoing challenges that need attention, like making reliability a part of release planning, giving teams the tools and training they need to tackle incidents smoothly, and getting everyone on the same page, from leadership to contributors.

When it comes to AI, the focus should be on using it in practical ways that actually make work easier rather than more complicated. Building resilience and reliability isn't just about technical know-how. It's about clear goals, teamwork, and always looking for ways to improve. Companies that see SRE as a way to drive real outcomes, rather than just a set of technical tasks, will be in a great spot to succeed as the digital world keeps getting more complex and fast-paced.

Leo Vasiliou is Director of Product Marketing at Catchpoint

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...