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Building Digital Resiliency in a Hyper-Connected World

Eric Johnson
PagerDuty

Operational resilience is an organization's ability to predict, respond to, and prevent unplanned work to drive reliable customer experiences and protect revenue. This doesn't just apply to downtime; it also covers service degradation due to latency or other factors. But make no mistake — when things go sideways, the bottom line and the customer are impacted.

According to a survey conducted by PagerDuty, resilience was ranked as one of the top three operations priorities by IT and business leaders across industries. But while organizations must protect against operational failures, many nevertheless struggle to identify and respond to service disruptions before they impact customers, reputation and revenue. High-profile incidents like the July 19 global IT outage offer an opportunity for organizations to learn from mistakes — either their own, or that of others.

Image
Pagerduty

How to Build More Resilient Systems

Organizations must reduce the frequency and severity of major incidents to improve business continuity and digital operations resiliency. This ensures always-on access to mission-critical services to meet customer expectations, compliance requirements and SLAs.

Think automation-first

The influx of data, as well as the increase in noise and incidents, means that humans simply can't keep up with the sheer amount of information coming in. Not only that, but responding to each and every problem leaves room for error and takes subject matter expert (SME) time away from other, more critical work, to say nothing of the cost of the time spent on resolving them. The survey of over 500 IT leaders, estimated the true cost of downtime to be $4,537 per minute, so when you consider that the average resolution time takes 175 minutes, each customer-impacting digital incident can cost nearly $800k. It all adds up to a giant waste of resources and exacerbates customer impact.

With automation as the first line of defense, organizations can let machines enrich and normalize data, run diagnostics, remediate issues and coordinate response efforts before responders are even alerted to the issue. This preserves human capacity and makes systems more resilient against human error, thus minimizing customer disruption, reputational risk, and revenue loss from operational failures.

Make it people-centric

Resilience is also reliant on the humans that power these technical systems. In cases where automation can't resolve problems without intervention, it's important to have processes in place that support teams doing their best work under challenging circumstances with as little disruption to both them and the customer as possible. Consider all the processes that go into ensuring that systems stay up and available. From on-call rotations to how postmortems are conducted and fixes prioritized, the people involved should feel like the processes help them become more efficient and proactive.

Keeping the humans — internal stakeholders, technical teams, customer support agents, leadership and customers — in the loop and informed with timely and critical updates regarding incidents is key.

Our CEO also offers this human-centric gem: Instead of looking for who to blame, look for the learning opportunity in the form of "blameless postmortems," and you'll get better outcomes. Turning every incident into an opportunity for everyone to learn from them and continuously improve will build more resilient operations.

Leverage the power of AI/ML-assist

Resilience is, in part, about speed. We can't predict everything that could go wrong, but we just know that somewhere, sometime, something will. But being able to fix a broken system and provide a more reliable customer experience is time-sensitive. Every minute of downtime has a cost to the business.

Organizations need to leverage AI and ML to assist technical teams in triaging, communicating and reporting problems faster. With the right information at responders' fingertips:

Teams can bring incidents to a resolution faster. By having the ability to query the data, they're able to get a much richer and detailed understanding of what's happening in a fraction of the time, so they're able to get to the work of resolution quickly.

Teams are able to communicate with less time and toil required. These tools can act as a "first drafter" for communications, postmortems, automation runbooks and more, helping teams use their capacity for more value-add work.

Teams can create post-incident reviews easier to ensure that the system is hardening over time. The system uses these tools to incorporate learnings into future response strategies, including how to standardize incident management, streamlining operations and automating key workflows to scale smoothly and reduce cognitive load.

Teams can have confidence in their compliance with emerging digital resiliency requirements. With the rise in critical digital infrastructure incidents, organizations can expect regulatory and compliance constraints to gain strength. The EU is already there, with DORA set to become law in early 2025, and similar attempts ramping up globally. AI and ML tools can help build out documentation of actions taken during an incident to create auditable records for compliance purposes later.

Resilience Is a Modern "Must Have"

In today's hyper-connected world, and with cyber threats growing more frequent and sophisticated, building digital resilience is as critical as the electricity running our systems. Organizations that adopt a proactive approach — focusing on both technical resilience and empowering their teams — will be better equipped to navigate challenges before they impact the customer experience.

Ultimately, resilience is an ongoing journey. By learning from past incidents and continuously improving systems and processes, companies can not only prevent failures but turn challenges into opportunities for growth and innovation. With the right mix of technology and human expertise, businesses can stay ahead of disruptions and build a future where digital operations are as robust as they are adaptable.

Eric Johnson is Chief Information Officer at PagerDuty

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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? ...

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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 ...

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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 ...

Building Digital Resiliency in a Hyper-Connected World

Eric Johnson
PagerDuty

Operational resilience is an organization's ability to predict, respond to, and prevent unplanned work to drive reliable customer experiences and protect revenue. This doesn't just apply to downtime; it also covers service degradation due to latency or other factors. But make no mistake — when things go sideways, the bottom line and the customer are impacted.

According to a survey conducted by PagerDuty, resilience was ranked as one of the top three operations priorities by IT and business leaders across industries. But while organizations must protect against operational failures, many nevertheless struggle to identify and respond to service disruptions before they impact customers, reputation and revenue. High-profile incidents like the July 19 global IT outage offer an opportunity for organizations to learn from mistakes — either their own, or that of others.

Image
Pagerduty

How to Build More Resilient Systems

Organizations must reduce the frequency and severity of major incidents to improve business continuity and digital operations resiliency. This ensures always-on access to mission-critical services to meet customer expectations, compliance requirements and SLAs.

Think automation-first

The influx of data, as well as the increase in noise and incidents, means that humans simply can't keep up with the sheer amount of information coming in. Not only that, but responding to each and every problem leaves room for error and takes subject matter expert (SME) time away from other, more critical work, to say nothing of the cost of the time spent on resolving them. The survey of over 500 IT leaders, estimated the true cost of downtime to be $4,537 per minute, so when you consider that the average resolution time takes 175 minutes, each customer-impacting digital incident can cost nearly $800k. It all adds up to a giant waste of resources and exacerbates customer impact.

With automation as the first line of defense, organizations can let machines enrich and normalize data, run diagnostics, remediate issues and coordinate response efforts before responders are even alerted to the issue. This preserves human capacity and makes systems more resilient against human error, thus minimizing customer disruption, reputational risk, and revenue loss from operational failures.

Make it people-centric

Resilience is also reliant on the humans that power these technical systems. In cases where automation can't resolve problems without intervention, it's important to have processes in place that support teams doing their best work under challenging circumstances with as little disruption to both them and the customer as possible. Consider all the processes that go into ensuring that systems stay up and available. From on-call rotations to how postmortems are conducted and fixes prioritized, the people involved should feel like the processes help them become more efficient and proactive.

Keeping the humans — internal stakeholders, technical teams, customer support agents, leadership and customers — in the loop and informed with timely and critical updates regarding incidents is key.

Our CEO also offers this human-centric gem: Instead of looking for who to blame, look for the learning opportunity in the form of "blameless postmortems," and you'll get better outcomes. Turning every incident into an opportunity for everyone to learn from them and continuously improve will build more resilient operations.

Leverage the power of AI/ML-assist

Resilience is, in part, about speed. We can't predict everything that could go wrong, but we just know that somewhere, sometime, something will. But being able to fix a broken system and provide a more reliable customer experience is time-sensitive. Every minute of downtime has a cost to the business.

Organizations need to leverage AI and ML to assist technical teams in triaging, communicating and reporting problems faster. With the right information at responders' fingertips:

Teams can bring incidents to a resolution faster. By having the ability to query the data, they're able to get a much richer and detailed understanding of what's happening in a fraction of the time, so they're able to get to the work of resolution quickly.

Teams are able to communicate with less time and toil required. These tools can act as a "first drafter" for communications, postmortems, automation runbooks and more, helping teams use their capacity for more value-add work.

Teams can create post-incident reviews easier to ensure that the system is hardening over time. The system uses these tools to incorporate learnings into future response strategies, including how to standardize incident management, streamlining operations and automating key workflows to scale smoothly and reduce cognitive load.

Teams can have confidence in their compliance with emerging digital resiliency requirements. With the rise in critical digital infrastructure incidents, organizations can expect regulatory and compliance constraints to gain strength. The EU is already there, with DORA set to become law in early 2025, and similar attempts ramping up globally. AI and ML tools can help build out documentation of actions taken during an incident to create auditable records for compliance purposes later.

Resilience Is a Modern "Must Have"

In today's hyper-connected world, and with cyber threats growing more frequent and sophisticated, building digital resilience is as critical as the electricity running our systems. Organizations that adopt a proactive approach — focusing on both technical resilience and empowering their teams — will be better equipped to navigate challenges before they impact the customer experience.

Ultimately, resilience is an ongoing journey. By learning from past incidents and continuously improving systems and processes, companies can not only prevent failures but turn challenges into opportunities for growth and innovation. With the right mix of technology and human expertise, businesses can stay ahead of disruptions and build a future where digital operations are as robust as they are adaptable.

Eric Johnson is Chief Information Officer at PagerDuty

Hot Topics

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 ...