Why AI Is the Differentiator for Operationally Resilient Organizations

Eric Johnson
PagerDuty
Learn more about PagerDuty

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.

That's why having "good enough" operational resilience is no longer enough, and minimizing downtime is now a business imperative. In a bid to optimize resilience, many businesses have adopted AI to solve their operations headaches, and this mentality has propelled AI from a tool for early tech adopters to an indispensable part of the operations team's suite.

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?

Downtime Costs Money and Reputation

The financial stakes couldn't be higher. More than two-thirds (68%) of organizations lose more than $300,000 per hour during IT incidents, and a third lose at least $500,000. For nearly a tenth of organizations, the figure can top $1m per hour. These high costs place intense pressure on organizations to preserve customer trust, investor confidence and the bottom line.

But it doesn't stop there. Incidents also create more pervasive problems around reduced staff productivity and an increase in developer burnout. The latter can be particularly insidious when organizations are already struggling to retain their top engineering talent. If staff are continuously dragged out of bed in the middle of the night or pulled away from their work to handle alert pings, they're more likely to leave for a competitor that can offer a better work-life balance. Those left guarding the fort will be even more stretched and demotivated.

AI is part of the problem, as well as the solution. As more companies roll out customer service chatbots, coding assistants and business process agents, they also expose themselves to more outage risks. More than four in five business report experiencing at least one AI-related outage.

This all creates a clear mandate for the C-suite: reduce the number of incidents and accelerate recovery times, and you will turn resilience into a competitive advantage. Almost all (95%) of survey respondents say their leadership understands this.

The AI Difference

Business and technology leaders are not just understanding the need for operational resilience. They're also taking action.

The "AI pioneers" are more likely (75%) to say they are operationally mature than the organizations that are discussing, but not deploying, the technology (66%). The difference is that mature organizations can recognize the value of AI at every stage of the incident resolution pipeline.

AI-first operations management tools reduce noise and streamline triage by grouping alerts into a single incident, and auto-pausing notifications for transient issues that are often resolved on their own. AI agents can also run auto-diagnostics via one-click runbooks, establishing contributing factors before humans are brought in. Alerts are then directed to the most appropriate subject matter expert (SME) based on expertise, workload and past response times. Together, these features save time and reduce alert fatigue for responders.

For more common and recurring incident types, AI agents can take on remediation and recovery autonomously, reducing the need for manual intervention. Their value in digital operations lies in the ability to operate through a continuous cycle of perceiving, reasoning, acting and learning independent of human teams. That's not just useful for remediation, but also tasks like capturing information for post-incident reviews and coordinating on-call schedules for SMEs.

Generative AI (GenAI) also plays a complementary role. It can support SMEs as a chatbot-based assistant, helping them query and investigate incidents in real-time, while also enabling proactive and automated customer-facing status updates.

The real differentiation comes from AI that operates across the entire technology stack to anticipate and prevent incidents before they ever impact customers. This shifts digital operations towards a proactive model, freeing SMEs to focus on innovation stepping in only during the most challenging incidents.

Beyond Resilience

Organizations are keen to embrace this future, seeing benefits that go beyond operational resilience to broader improvements in how operations teams work. More than two-fifths of organizations surveyed expect AI-first digital operations to improve competitiveness by allowing them more time for innovation and experimentation.

The shift to AI-first operations can also help to mitigate current talent shortages by appealing to existing employees and prospective hires. A growing number of engineers recognize that AI could liberate them from repetitive and manual toil, rather than serve as a potential rival.

Trust in the Future

Not all operations leaders are fully sold on AI. Confidence is higher for tasks like incident analysis than for activities with direct customer impact, which is why many organizations stop short of granting full autonomy in some situations. Keeping a human in the loop remains a sensible way for organizations to strike the right balance between efficiency and control.

These concerns, however, should not slow the pace of adoption. Boards that commit to AI-driven operations are starting to pull away from their competitors, demonstrating how the function can evolve from reactive response to proactive prevention.

The direction is clear, and the gap will widen for those that delay.

Eric Johnson is Chief Information Officer at PagerDuty

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Why AI Is the Differentiator for Operationally Resilient Organizations

Eric Johnson
PagerDuty
Learn more about PagerDuty

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.

That's why having "good enough" operational resilience is no longer enough, and minimizing downtime is now a business imperative. In a bid to optimize resilience, many businesses have adopted AI to solve their operations headaches, and this mentality has propelled AI from a tool for early tech adopters to an indispensable part of the operations team's suite.

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?

Downtime Costs Money and Reputation

The financial stakes couldn't be higher. More than two-thirds (68%) of organizations lose more than $300,000 per hour during IT incidents, and a third lose at least $500,000. For nearly a tenth of organizations, the figure can top $1m per hour. These high costs place intense pressure on organizations to preserve customer trust, investor confidence and the bottom line.

But it doesn't stop there. Incidents also create more pervasive problems around reduced staff productivity and an increase in developer burnout. The latter can be particularly insidious when organizations are already struggling to retain their top engineering talent. If staff are continuously dragged out of bed in the middle of the night or pulled away from their work to handle alert pings, they're more likely to leave for a competitor that can offer a better work-life balance. Those left guarding the fort will be even more stretched and demotivated.

AI is part of the problem, as well as the solution. As more companies roll out customer service chatbots, coding assistants and business process agents, they also expose themselves to more outage risks. More than four in five business report experiencing at least one AI-related outage.

This all creates a clear mandate for the C-suite: reduce the number of incidents and accelerate recovery times, and you will turn resilience into a competitive advantage. Almost all (95%) of survey respondents say their leadership understands this.

The AI Difference

Business and technology leaders are not just understanding the need for operational resilience. They're also taking action.

The "AI pioneers" are more likely (75%) to say they are operationally mature than the organizations that are discussing, but not deploying, the technology (66%). The difference is that mature organizations can recognize the value of AI at every stage of the incident resolution pipeline.

AI-first operations management tools reduce noise and streamline triage by grouping alerts into a single incident, and auto-pausing notifications for transient issues that are often resolved on their own. AI agents can also run auto-diagnostics via one-click runbooks, establishing contributing factors before humans are brought in. Alerts are then directed to the most appropriate subject matter expert (SME) based on expertise, workload and past response times. Together, these features save time and reduce alert fatigue for responders.

For more common and recurring incident types, AI agents can take on remediation and recovery autonomously, reducing the need for manual intervention. Their value in digital operations lies in the ability to operate through a continuous cycle of perceiving, reasoning, acting and learning independent of human teams. That's not just useful for remediation, but also tasks like capturing information for post-incident reviews and coordinating on-call schedules for SMEs.

Generative AI (GenAI) also plays a complementary role. It can support SMEs as a chatbot-based assistant, helping them query and investigate incidents in real-time, while also enabling proactive and automated customer-facing status updates.

The real differentiation comes from AI that operates across the entire technology stack to anticipate and prevent incidents before they ever impact customers. This shifts digital operations towards a proactive model, freeing SMEs to focus on innovation stepping in only during the most challenging incidents.

Beyond Resilience

Organizations are keen to embrace this future, seeing benefits that go beyond operational resilience to broader improvements in how operations teams work. More than two-fifths of organizations surveyed expect AI-first digital operations to improve competitiveness by allowing them more time for innovation and experimentation.

The shift to AI-first operations can also help to mitigate current talent shortages by appealing to existing employees and prospective hires. A growing number of engineers recognize that AI could liberate them from repetitive and manual toil, rather than serve as a potential rival.

Trust in the Future

Not all operations leaders are fully sold on AI. Confidence is higher for tasks like incident analysis than for activities with direct customer impact, which is why many organizations stop short of granting full autonomy in some situations. Keeping a human in the loop remains a sensible way for organizations to strike the right balance between efficiency and control.

These concerns, however, should not slow the pace of adoption. Boards that commit to AI-driven operations are starting to pull away from their competitors, demonstrating how the function can evolve from reactive response to proactive prevention.

The direction is clear, and the gap will widen for those that delay.

Eric Johnson is Chief Information Officer at PagerDuty

Related Links

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The Latest

When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

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Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

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