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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.

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For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

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Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

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Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

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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

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...