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Site Reliability Engineering (SRE) is the Force Multiplier of Digital Experiences

Colin Fallwell
Sumo Logic

The pandemic spurred a wave of digital services because they allowed companies to stay competitive in the digital transformation. This trend, in turn, caused companies to adopt site reliability engineering (SRE) to keep up with the customer demand for digital experiences.

DevOps Institute recently published the Global SRE Pulse 2022 highlighting the growing adoption of SRE as a central operating model to deliver digital services and applications.


Even with over 62% of respondents saying their organizations are leveraging SRE within their company today, the survey shows that many organizations are at different stages within SRE adoption. Only 1% of respondents report that they tried SRE but that it did not work for their company.

SRE is now an essential engineering practice for enterprises seeking to accelerate digital transformations to digital-first brands. So how can companies empower SREs and adopt the model across their entire IT organizations to improve digital experiences and ultimately the business? It first starts with addressing the workforce gap and then breaking down team silos.

Closing the Skills Gap

The biggest challenge when adopting SRE is finding those with the right skills to make SRE to work properly — with 85% of respondents citing the lack of staff with necessary skills as their biggest challenge.

Leaders can address skill gaps by training talent and promoting within the organization. It's important to not only look at the technical skills but also at a candidate's ability to see and advocate for the relationship between engineering and business.

It's also essential to implement automation solutions to reduce the manual work of solving priority alerts. It's not just a matter of implementing technology though. Teams must also update processes to ensure the technology is used by everyone, including those who resist AIOps and automation.

The survey found that some teams are implementing intelligent automation everywhere to ensure the reliability and continuous operation of systems. Specifically, 29% of respondents said they are currently leveraging observability tools and techniques.

One method of advancing automation is through chaos engineering and intentionally destroying and rebuilding environments to improve both hygiene and confidence. However, 43% of survey respondents said they're not applying chaos engineering at all, so there is significant opportunity for those willing to learn the skills.

SRE Best Practices Can Unify Teams

Siloed teams is another common challenge for organizations. Communication and dependencies delay responses and innovation. SREs can bridge the gap between IT and developers if leaders first implement these SRE best practices across teams.

Track and manage toil. Toil is work that is manual, repetitive, automatable, tactical, or devoid of enduring value, and it scales linearly as a service grows. In the survey, 66% of respondents said they measure toil in some or several teams, and 11% indicated they track toil everywhere. By measuring toil, SREs can proactively reduce its effects across teams to improve reliability.

Provide ongoing support. Organizations also report implementing SRE best practices, including these across all teams:

- Adopting observability and monitoring tools (29%)
- Supporting essential job certifications (27%)
- Practicing a no blame philosophy (36%)

The two most widely adopted practices to at least some extent were practicing no blame (92%) and retrospectives or post-mortems (95%). The philosophy of learning from failure is what drives SRE success in many organizations.

Looking into the Future of SRE

Overall, the level of maturity revealed by the Global SRE Pulse survey indicates that many organizations are invested in improving SRE and making it part of their processes and cultures.

With 37% of organizations reporting that they have centralized SRE teams, it appears the practices and topologies are evolving. But the foundation for SRE is on solid ground and business leaders can expect SRE to remain a fixture in the industry. Beyond that, SRE also has the opportunity to be a unifying force between IT and business departments. By partnering with business and development teams, SRE will have the ability to influence and improve business outcomes.

Colin Fallwell is Field CTO of Sumo Logic

Hot Topics

The Latest

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...

Site Reliability Engineering (SRE) is the Force Multiplier of Digital Experiences

Colin Fallwell
Sumo Logic

The pandemic spurred a wave of digital services because they allowed companies to stay competitive in the digital transformation. This trend, in turn, caused companies to adopt site reliability engineering (SRE) to keep up with the customer demand for digital experiences.

DevOps Institute recently published the Global SRE Pulse 2022 highlighting the growing adoption of SRE as a central operating model to deliver digital services and applications.


Even with over 62% of respondents saying their organizations are leveraging SRE within their company today, the survey shows that many organizations are at different stages within SRE adoption. Only 1% of respondents report that they tried SRE but that it did not work for their company.

SRE is now an essential engineering practice for enterprises seeking to accelerate digital transformations to digital-first brands. So how can companies empower SREs and adopt the model across their entire IT organizations to improve digital experiences and ultimately the business? It first starts with addressing the workforce gap and then breaking down team silos.

Closing the Skills Gap

The biggest challenge when adopting SRE is finding those with the right skills to make SRE to work properly — with 85% of respondents citing the lack of staff with necessary skills as their biggest challenge.

Leaders can address skill gaps by training talent and promoting within the organization. It's important to not only look at the technical skills but also at a candidate's ability to see and advocate for the relationship between engineering and business.

It's also essential to implement automation solutions to reduce the manual work of solving priority alerts. It's not just a matter of implementing technology though. Teams must also update processes to ensure the technology is used by everyone, including those who resist AIOps and automation.

The survey found that some teams are implementing intelligent automation everywhere to ensure the reliability and continuous operation of systems. Specifically, 29% of respondents said they are currently leveraging observability tools and techniques.

One method of advancing automation is through chaos engineering and intentionally destroying and rebuilding environments to improve both hygiene and confidence. However, 43% of survey respondents said they're not applying chaos engineering at all, so there is significant opportunity for those willing to learn the skills.

SRE Best Practices Can Unify Teams

Siloed teams is another common challenge for organizations. Communication and dependencies delay responses and innovation. SREs can bridge the gap between IT and developers if leaders first implement these SRE best practices across teams.

Track and manage toil. Toil is work that is manual, repetitive, automatable, tactical, or devoid of enduring value, and it scales linearly as a service grows. In the survey, 66% of respondents said they measure toil in some or several teams, and 11% indicated they track toil everywhere. By measuring toil, SREs can proactively reduce its effects across teams to improve reliability.

Provide ongoing support. Organizations also report implementing SRE best practices, including these across all teams:

- Adopting observability and monitoring tools (29%)
- Supporting essential job certifications (27%)
- Practicing a no blame philosophy (36%)

The two most widely adopted practices to at least some extent were practicing no blame (92%) and retrospectives or post-mortems (95%). The philosophy of learning from failure is what drives SRE success in many organizations.

Looking into the Future of SRE

Overall, the level of maturity revealed by the Global SRE Pulse survey indicates that many organizations are invested in improving SRE and making it part of their processes and cultures.

With 37% of organizations reporting that they have centralized SRE teams, it appears the practices and topologies are evolving. But the foundation for SRE is on solid ground and business leaders can expect SRE to remain a fixture in the industry. Beyond that, SRE also has the opportunity to be a unifying force between IT and business departments. By partnering with business and development teams, SRE will have the ability to influence and improve business outcomes.

Colin Fallwell is Field CTO of Sumo Logic

Hot Topics

The Latest

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...