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75% of Companies Consider AI Essential to Operations

Executive trust in AI agents and reliance on AI across business operations is growing, according to the PagerDuty AI Resilience Survey — 81% of executives trust AI agents to take action on the company's behalf during a crisis, such as a service outage or security event.

AI is moving from experimental to essential. Nearly three-quarters of executives (74%) say their company would struggle to function without it, showing how quickly reliance has grown. Projects that began as pilots and trials are now viewed as mission-critical infrastructure.

Additionally, companies are increasingly using AI in software development, where more than four out of five respondents (84%) report using it to write, review, or suggest code.

Key Findings:

Agentic AI deployment is racing ahead

Three out of four (75%) companies have already deployed more than one AI agent, with a quarter (25%) deploying five or more.

Maturing models drive confidence gains

Executives credit better outputs (49%), more frequent usage with positive results (48%), improved understanding of AI (47%), and stronger oversight measures (45%) as the top reasons for growing confidence.

AI is now seen as mission-critical infrastructure

Nearly three in four executives (74%) view AI as essential to operations, rising to 77% for smaller companies under 10,000 employees. C-suites and owners are especially convinced, with 83% saying their business would struggle without AI compared to 73% of directors and VPs.

Engineers are coding with AI at scale

More than four out of five (84%) companies now use AI to write, review or suggest code. Companies with multiple AI agents are even more likely to rely on AI for coding (91%) compared to those with one agent (68%) or none (44%). While 85% test AI-generated code, only 39% do so consistently through formal processes. The US leads on formal testing (59%) while Japan trails at 19%.

Guardrails lag behind increased adoption

An overwhelming 85% of executives say their organizations need better procedures to detect errors or failures in AI tools, with sentiment being highest in France (90%).

Companies are bracing for AI outages

84% of companies report experiencing at least one AI-related outage. More than half (57%) of those that haven't yet had an outage already have protocols in place for handling one, showing that resilience planning is becoming part of AI strategy.

Experience reveals the hidden complexity of AI

Among respondents whose companies have deployed one AI agent, 76% believe AI-driven complexity will outpace the number of people their company has to manage it. This concern is even higher among those with multiple AI agents at 79%.

In contrast, only 57% of respondents from companies without AI agents anticipate this challenge, suggesting that hands-on experience with AI deployment reveals the true scope of management complexity involved.

Methodology: The report is based on responses from 1,500 IT and business executives across Australia, France, Germany, Japan, UK and US regions.

The Latest

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 ...

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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 ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

75% of Companies Consider AI Essential to Operations

Executive trust in AI agents and reliance on AI across business operations is growing, according to the PagerDuty AI Resilience Survey — 81% of executives trust AI agents to take action on the company's behalf during a crisis, such as a service outage or security event.

AI is moving from experimental to essential. Nearly three-quarters of executives (74%) say their company would struggle to function without it, showing how quickly reliance has grown. Projects that began as pilots and trials are now viewed as mission-critical infrastructure.

Additionally, companies are increasingly using AI in software development, where more than four out of five respondents (84%) report using it to write, review, or suggest code.

Key Findings:

Agentic AI deployment is racing ahead

Three out of four (75%) companies have already deployed more than one AI agent, with a quarter (25%) deploying five or more.

Maturing models drive confidence gains

Executives credit better outputs (49%), more frequent usage with positive results (48%), improved understanding of AI (47%), and stronger oversight measures (45%) as the top reasons for growing confidence.

AI is now seen as mission-critical infrastructure

Nearly three in four executives (74%) view AI as essential to operations, rising to 77% for smaller companies under 10,000 employees. C-suites and owners are especially convinced, with 83% saying their business would struggle without AI compared to 73% of directors and VPs.

Engineers are coding with AI at scale

More than four out of five (84%) companies now use AI to write, review or suggest code. Companies with multiple AI agents are even more likely to rely on AI for coding (91%) compared to those with one agent (68%) or none (44%). While 85% test AI-generated code, only 39% do so consistently through formal processes. The US leads on formal testing (59%) while Japan trails at 19%.

Guardrails lag behind increased adoption

An overwhelming 85% of executives say their organizations need better procedures to detect errors or failures in AI tools, with sentiment being highest in France (90%).

Companies are bracing for AI outages

84% of companies report experiencing at least one AI-related outage. More than half (57%) of those that haven't yet had an outage already have protocols in place for handling one, showing that resilience planning is becoming part of AI strategy.

Experience reveals the hidden complexity of AI

Among respondents whose companies have deployed one AI agent, 76% believe AI-driven complexity will outpace the number of people their company has to manage it. This concern is even higher among those with multiple AI agents at 79%.

In contrast, only 57% of respondents from companies without AI agents anticipate this challenge, suggesting that hands-on experience with AI deployment reveals the true scope of management complexity involved.

Methodology: The report is based on responses from 1,500 IT and business executives across Australia, France, Germany, Japan, UK and US regions.

The Latest

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 ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...