8 Takeaways on the State of Observability for Energy and Utilities

Peter Pezaris
New Relic
Learn more about 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

Related Links

Hot Topics

Related Vendors

The Latest

Why Cloud Reliability Comes Down to People and How to Improve It

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

Why IT Keeps Getting Handed an AI Training Problem It Can't Solve Alone

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

Bringing IBM i Into the Modern Observability Stack: The Missing Piece of Enterprise AIOps

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...

89% of Frontend Teams Use AI in Their Workflows, but Few Use It for Observability

We just surveyed 300 frontend and mobile engineers across 16 countries, and the finding that keeps sticking with me isn't the one about AI. It's this: 74% of engineering teams rate themselves in the "middle" of the observability maturity scale. Not reactive, not strategic. Stuck in the middle. They have dashboards, they have tracing, they have alerts. And yet when something goes wrong, they still can't tell you why ...

MEAN TIME TO INSIGHT Podcast - Episode 25: AI's Impact on WAN

In MEAN TIME TO INSIGHT Episode 25, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses  AI's impact on the Wide Area Network (WAN) ... 

6 Signs Your APM Dashboard Looks Healthy but Your App Is Not

Application performance monitoring (APM) dashboards are only as useful as what they are configured to measure. The default setup covers obvious failure modes such as downtime, error spikes, and latency breaches, but it does not cover everything. Some failures produce no alerts or anomalies. The dashboard stays green while users experience a broken product. Here are six signs that is happening ...

AI Innovation Was the First Phase. Governance and Accountability Are the Next ...

The race to deploy AI is largely over. Most enterprises have entered it. The question now is not whether artificial intelligence is running inside the organization. The question is whether anyone is genuinely responsible for what it does. That is not a technical question. It is a leadership one. And most organizations are not yet structured to answer it honestly ...

Your AI Agent's Biggest Problem Isn't the Model - It's the Data

A new analysis of 250 real-world queries across common retail tasks, such as product pricing, availability, ratings, shipping and specifications, reveals systemic inefficiency at the heart of web-based AI agents. On average, 97.9% of the data retrieved by agents from live web pages is irrelevant to the query being answered. Specifically, the average page ingested ran nearly 9,000 characters, while the average answer was just 32 characters, resulting in a noise-to-signal ratio of 278:1. Price queries were the most extreme outlier, with noise rates approaching 99.5%. That's not a rounding error. That's a structural problem ...

Observability Is the New Control Plane for Enterprise Transformation

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

When AI Becomes the Corporate OS

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

8 Takeaways on the State of Observability for Energy and Utilities

Peter Pezaris
New Relic
Learn more about 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

Related Links

Hot Topics

Related Vendors

The Latest

Why Cloud Reliability Comes Down to People and How to Improve It

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

Why IT Keeps Getting Handed an AI Training Problem It Can't Solve Alone

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

Bringing IBM i Into the Modern Observability Stack: The Missing Piece of Enterprise AIOps

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...

89% of Frontend Teams Use AI in Their Workflows, but Few Use It for Observability

We just surveyed 300 frontend and mobile engineers across 16 countries, and the finding that keeps sticking with me isn't the one about AI. It's this: 74% of engineering teams rate themselves in the "middle" of the observability maturity scale. Not reactive, not strategic. Stuck in the middle. They have dashboards, they have tracing, they have alerts. And yet when something goes wrong, they still can't tell you why ...

MEAN TIME TO INSIGHT Podcast - Episode 25: AI's Impact on WAN

In MEAN TIME TO INSIGHT Episode 25, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses  AI's impact on the Wide Area Network (WAN) ... 

6 Signs Your APM Dashboard Looks Healthy but Your App Is Not

Application performance monitoring (APM) dashboards are only as useful as what they are configured to measure. The default setup covers obvious failure modes such as downtime, error spikes, and latency breaches, but it does not cover everything. Some failures produce no alerts or anomalies. The dashboard stays green while users experience a broken product. Here are six signs that is happening ...

AI Innovation Was the First Phase. Governance and Accountability Are the Next ...

The race to deploy AI is largely over. Most enterprises have entered it. The question now is not whether artificial intelligence is running inside the organization. The question is whether anyone is genuinely responsible for what it does. That is not a technical question. It is a leadership one. And most organizations are not yet structured to answer it honestly ...

Your AI Agent's Biggest Problem Isn't the Model - It's the Data

A new analysis of 250 real-world queries across common retail tasks, such as product pricing, availability, ratings, shipping and specifications, reveals systemic inefficiency at the heart of web-based AI agents. On average, 97.9% of the data retrieved by agents from live web pages is irrelevant to the query being answered. Specifically, the average page ingested ran nearly 9,000 characters, while the average answer was just 32 characters, resulting in a noise-to-signal ratio of 278:1. Price queries were the most extreme outlier, with noise rates approaching 99.5%. That's not a rounding error. That's a structural problem ...

Observability Is the New Control Plane for Enterprise Transformation

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

When AI Becomes the Corporate OS

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...