Skip to main content

8 Takeaways on the State of Observability for Energy and Utilities

Peter Pezaris
New Relic

In June, New Relic published the State of Observability for Energy and Utilities Report to share insights, analysis, and data on the impact of full-stack observability software in energy and utilities organizations' service capabilities.

  

Source: National Grid

Here are eight key takeaways from the report:

1. Outages Cost Energy and Utilities Companies More than Any Other Industry

The report found that high-impact-outages affect energy and utilities more than any other industry, with 40% experiencing outages at least once per week compared to 32% across all other industries surveyed. Consequently, the median annual downtime for energy and utility organizations was 37 hours, with 61% of respondents reporting that their mean time to resolve (MTTR) is at least 30 minutes to resolve outages. Each second during an outage comes with a price tag. More than half of energy and utilities organizations (52%) shared that critical business app outages cost at least $500,000 per hour, and 34% indicated that outages cost at least $1 million per hour.

2. Observability Increases Productivity

Since adopting observability solutions, energy and utilities companies have experienced substantial productivity improvements. Of those surveyed, 78% said their MTTR has somewhat improved. Further, organizations with full-stack observability noted even more significant MTTR progress, with 87% reporting improvements.

3. Increased Focus on Security, Governance, Risk, and Compliance is Driving Observability Adoption

For energy and utility organizations, the top technology trend driving the need for observability was an increased focus on security, governance, risk, and compliance (44%), followed by the adoption of Internet of Things (IoT) technologies (36%) and customer experience management (36%).

4. Observability Tooling Deployment is on the Rise

Organizations are prioritizing investment in observability tooling, which includes security monitoring (68%), network monitoring (66%), and infrastructure monitoring (60%). Notably, energy and utility organizations reported high levels of deployment for AIOps (AI for IT operations) capabilities, including anomaly detection, indecent intelligence, and root cause analysis (55%). In fact, by mid-2026, 89% of respondents plan to have deployed AIOps.

5. Energy and Utilities Companies are More Likely to Use Multiple Monitoring Tools

Energy and utilities organizations showed a higher tendency than average to utilize multiple monitoring tools across the 17 observability capabilities included in the study. In fact, three-fourths (75%) of respondents used four or more tools for observability, and 24% used eight or more tools. However, over the next year, 36% indicated that their organization is likely to consolidate tools.

6. Organizations are Maximizing the Value of Observability Spend

Out of all industries surveyed, energy and utilities organizations indicated the highest annual observability spend, with more than two-thirds (68%) spending at least $500,000 and 46% spending at least $1 million per year on observability tooling. In turn, organizations are planning to maximize the return on investment (ROI) on observability spending in the next year by training staff on how best to use their observability tools (48%), optimizing their engineering team size (42%), and consolidating tools (36%). Energy and utility companies stated that their organizations receive a significantly higher total annual value from observability than average, with 76% reporting receiving more than $500,000 from its observability investment per year, 66% stating $1 million or more, and 41% attaining $5 million or more per year in total value. The numbers reported around annual spending and annual value received reflect nearly a 3x median ROI, or 192%.

7. Observability Increases Business Value

Energy and utilities companies reported that observability improves their lives in several ways. Half of IT decision-makers (ITDMs) expressed that observability helps establish a technology strategy, and 46% said it enables data visualization from a single dashboard. Practitioners indicated that observability increases productivity so they can detect and resolve issues faster (43%) and allows less guesswork when managing complicated and distributed tech stacks (35%). Respondents also noted benefits enabled by observability, including increased operational efficiency (39%), improved system uptime and reliability (35%), security vulnerability management (35%), and improved real-user experience (29%). Ultimately, organizations concluded that observability provides numerous positive business outcomes, including improving collaboration across teams to make decisions related to the software stack (42%), creating revenue-generating use cases (35%), and quantifying the business impact of events and incidents with telemetry data (33%).

8. The Future is Bright for Observability Tooling Deployment

Energy and utilities companies are enthusiastic about their observability deployment plans over the next one to three years. By mid-2026, 99% of respondents expect to have deployed several monitoring tools, including security monitoring, database monitoring, and network monitoring, followed by 96% of organizations anticipating alerts and application performance monitoring. Methodology: New Relic's annual observability forecast offers insights into how observability influences organizations and their decision-makers. To gauge the current observability landscape, professionals from various industries and regions were surveyed. Among the 1,700 technology practitioners and decision-makers surveyed, 132 were associated with the energy and utilities sectors.

Peter Pezaris is Chief Design and Strategy Officer at New Relic

The Latest

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 ... 

Virtual Private Networks became a cornerstone of enterprise security at a time when corporate infrastructure looked very different from today ... For years, this model worked well. But the architecture behind VPNs assumed a centralized corporate environment—one where the network itself was the hub of activity. In a cloud — first world, that assumption no longer holds ...

Website outages get resolved just as fast in August as they do in November. I went looking for the opposite: the summer slowdown everyone assumes is there once the people who fix things are away. It isn't in the data we collected, covering 1.8 million confirmed outages across tens of thousands of websites ...

This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...

8 Takeaways on the State of Observability for Energy and Utilities

Peter Pezaris
New Relic

In June, New Relic published the State of Observability for Energy and Utilities Report to share insights, analysis, and data on the impact of full-stack observability software in energy and utilities organizations' service capabilities.

  

Source: National Grid

Here are eight key takeaways from the report:

1. Outages Cost Energy and Utilities Companies More than Any Other Industry

The report found that high-impact-outages affect energy and utilities more than any other industry, with 40% experiencing outages at least once per week compared to 32% across all other industries surveyed. Consequently, the median annual downtime for energy and utility organizations was 37 hours, with 61% of respondents reporting that their mean time to resolve (MTTR) is at least 30 minutes to resolve outages. Each second during an outage comes with a price tag. More than half of energy and utilities organizations (52%) shared that critical business app outages cost at least $500,000 per hour, and 34% indicated that outages cost at least $1 million per hour.

2. Observability Increases Productivity

Since adopting observability solutions, energy and utilities companies have experienced substantial productivity improvements. Of those surveyed, 78% said their MTTR has somewhat improved. Further, organizations with full-stack observability noted even more significant MTTR progress, with 87% reporting improvements.

3. Increased Focus on Security, Governance, Risk, and Compliance is Driving Observability Adoption

For energy and utility organizations, the top technology trend driving the need for observability was an increased focus on security, governance, risk, and compliance (44%), followed by the adoption of Internet of Things (IoT) technologies (36%) and customer experience management (36%).

4. Observability Tooling Deployment is on the Rise

Organizations are prioritizing investment in observability tooling, which includes security monitoring (68%), network monitoring (66%), and infrastructure monitoring (60%). Notably, energy and utility organizations reported high levels of deployment for AIOps (AI for IT operations) capabilities, including anomaly detection, indecent intelligence, and root cause analysis (55%). In fact, by mid-2026, 89% of respondents plan to have deployed AIOps.

5. Energy and Utilities Companies are More Likely to Use Multiple Monitoring Tools

Energy and utilities organizations showed a higher tendency than average to utilize multiple monitoring tools across the 17 observability capabilities included in the study. In fact, three-fourths (75%) of respondents used four or more tools for observability, and 24% used eight or more tools. However, over the next year, 36% indicated that their organization is likely to consolidate tools.

6. Organizations are Maximizing the Value of Observability Spend

Out of all industries surveyed, energy and utilities organizations indicated the highest annual observability spend, with more than two-thirds (68%) spending at least $500,000 and 46% spending at least $1 million per year on observability tooling. In turn, organizations are planning to maximize the return on investment (ROI) on observability spending in the next year by training staff on how best to use their observability tools (48%), optimizing their engineering team size (42%), and consolidating tools (36%). Energy and utility companies stated that their organizations receive a significantly higher total annual value from observability than average, with 76% reporting receiving more than $500,000 from its observability investment per year, 66% stating $1 million or more, and 41% attaining $5 million or more per year in total value. The numbers reported around annual spending and annual value received reflect nearly a 3x median ROI, or 192%.

7. Observability Increases Business Value

Energy and utilities companies reported that observability improves their lives in several ways. Half of IT decision-makers (ITDMs) expressed that observability helps establish a technology strategy, and 46% said it enables data visualization from a single dashboard. Practitioners indicated that observability increases productivity so they can detect and resolve issues faster (43%) and allows less guesswork when managing complicated and distributed tech stacks (35%). Respondents also noted benefits enabled by observability, including increased operational efficiency (39%), improved system uptime and reliability (35%), security vulnerability management (35%), and improved real-user experience (29%). Ultimately, organizations concluded that observability provides numerous positive business outcomes, including improving collaboration across teams to make decisions related to the software stack (42%), creating revenue-generating use cases (35%), and quantifying the business impact of events and incidents with telemetry data (33%).

8. The Future is Bright for Observability Tooling Deployment

Energy and utilities companies are enthusiastic about their observability deployment plans over the next one to three years. By mid-2026, 99% of respondents expect to have deployed several monitoring tools, including security monitoring, database monitoring, and network monitoring, followed by 96% of organizations anticipating alerts and application performance monitoring. Methodology: New Relic's annual observability forecast offers insights into how observability influences organizations and their decision-makers. To gauge the current observability landscape, professionals from various industries and regions were surveyed. Among the 1,700 technology practitioners and decision-makers surveyed, 132 were associated with the energy and utilities sectors.

Peter Pezaris is Chief Design and Strategy Officer at New Relic

The Latest

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 ... 

Virtual Private Networks became a cornerstone of enterprise security at a time when corporate infrastructure looked very different from today ... For years, this model worked well. But the architecture behind VPNs assumed a centralized corporate environment—one where the network itself was the hub of activity. In a cloud — first world, that assumption no longer holds ...

Website outages get resolved just as fast in August as they do in November. I went looking for the opposite: the summer slowdown everyone assumes is there once the people who fix things are away. It isn't in the data we collected, covering 1.8 million confirmed outages across tens of thousands of websites ...

This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...