Automated, Flexible and Proactive: 3 Keys to Reducing Toil and Burnout in DevOps

Dan McCall
PagerDuty
Learn more about PagerDuty

Every business is in a constant battle to maximize efficiency, minimize toil, and scale sustainably in a moment of macroeconomic pressure. These goals are challenging in the best of times, but our current environment — continued staffing shortages, hiring freezes, and economic uncertainty — all make it significantly harder.

Because of these pressures, and the increased importance of digital operations to customer experience, teams are under more stress than ever to deliver seamless customer experiences. A recent report found that over 60% of developers are responding to off-hours work alerts on weekly basis and nearly half worked more hours in 2021 than they did in 2020. Companies are working urgently to mature their digital operations, including making incident response strategies more intelligent.

Resiliency at scale requires businesses to become more data-driven than ever before to get ahead of problems before they arise Incident response is essential to digital infrastructure and is at the crux of building a resilient enterprise. Addressing customer issues in real-time means adopting an incident response strategy that is automated, flexible, and proactive.

This next-generation approach enables the automation of repetitive and mundane work, while separating important signals from the flood of noise across all digital services. With this in place, teams can address the most mission-critical incidents when they occur and get ahead of the underlying issues behind attrition and burnout.

By combining the expertise of humans and machines to reduce the manual toil that causes burnout, we allow our teams to have more time to focus on innovation, and mission-critical digital transformation initiatives, instead of firefighting.


1. Leverage machines for automation

First, it's time to recognize that leveraging machines for automation is key to not only achieving key business outcomes, but to reducing burden on the humans that build and maintain digital operations. Beyond automating manual tasks, the right tools can reduce alert fatigue and cut down on system noise by using a mix of data science techniques and machine learning to intelligently group alerts and remove interruptions. In turn, automation empowers teams to balance critical workloads, helping humans to work smarter and reduce the burden. This is paramount when teams are tightly staffed due to attrition, inability to back-fill, or just new team members

2. Adopt a flexible tech stack

Second, technical teams must adopt a flexible tech stack that addresses a multitude of unique business needs at scale. Businesses should look for tools that can easily plug into their existing systems, while maintaining security and compliance. When the market can change at a moment's notice, teams must have the resources at their disposal to react to change as it happens to minimize disruption to their workloads and to operations.

3. Shift from reactivity to proactivity

Finally, we must shift from reactivity to proactivity. The same report as above found only 8% of teams are currently classified as proactive. Proactive businesses often use intelligence to identify root problems to anticipate and prevent disruption down the line. We must help DevOps teams move toward a state of proactivity and prevention to manage and maintain their IT infrastructure's consistency, reliability, and resilience — which will in turn help teams streamline work and free up time.

Get Started

The path to improved incident response depends on where your business falls within the spectrum of operational maturity.

Those still in the manual and reactive stage must start small and stay focused. Put energy into turning manually documented steps into automated steps to enable opportunities for pockets of automation across your organization.

Companies in the responsive stage should work to standardize the incident response process and enable self-service. Standardization helps to build automation that can be reused across teams and services, while self-service empowers more than just your subject matter experts to leverage automation for greater value.

Once you're in the proactive stage, you should be running automation in response to incidents, creating auto-remediation capabilities, and removing some of the real-time burden placed on teams that do critical monitoring and remediation work.

This next phase of incident response will build resilient enterprises in the face of constant challenges. Once we combine the expertise of humans and machines to enable humans to do their most innovative work and embrace an approach that is automated, flexible, and proactive, teams will be able to do their jobs more efficiently and effectively than ever before.

Dan McCall is VP of Product Management, Incident Response, at PagerDuty

Related Links

Hot Topics

Related Vendors

The Latest

Manufacturing Organizations Doubled AI Investment Yet Only 37% Fully Prepared to Operationalize AI

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Optimizing Decisions with Edge-First Cognitive Intelligence

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

The New Perimeter Is the User: Why Identity Is the Real Network Edge

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

When AI Infrastructure Stops Behaving Like Infrastructure

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Enterprise Resilience: Understanding the Shift from Static to Dynamic

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile Users Expect Perfection in 2026

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

AI Confidence Is High. Readiness Is Not. What the Data Tells Us - and Why It Matters

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI, Realistic Expectations and the Future of IT Operations in 2026

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

Trust, But Verify: Building Confidence in AI Through Human Oversight

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

Data Centers Plan to Reduce Reliance on Grid

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...

Automated, Flexible and Proactive: 3 Keys to Reducing Toil and Burnout in DevOps

Dan McCall
PagerDuty
Learn more about PagerDuty

Every business is in a constant battle to maximize efficiency, minimize toil, and scale sustainably in a moment of macroeconomic pressure. These goals are challenging in the best of times, but our current environment — continued staffing shortages, hiring freezes, and economic uncertainty — all make it significantly harder.

Because of these pressures, and the increased importance of digital operations to customer experience, teams are under more stress than ever to deliver seamless customer experiences. A recent report found that over 60% of developers are responding to off-hours work alerts on weekly basis and nearly half worked more hours in 2021 than they did in 2020. Companies are working urgently to mature their digital operations, including making incident response strategies more intelligent.

Resiliency at scale requires businesses to become more data-driven than ever before to get ahead of problems before they arise Incident response is essential to digital infrastructure and is at the crux of building a resilient enterprise. Addressing customer issues in real-time means adopting an incident response strategy that is automated, flexible, and proactive.

This next-generation approach enables the automation of repetitive and mundane work, while separating important signals from the flood of noise across all digital services. With this in place, teams can address the most mission-critical incidents when they occur and get ahead of the underlying issues behind attrition and burnout.

By combining the expertise of humans and machines to reduce the manual toil that causes burnout, we allow our teams to have more time to focus on innovation, and mission-critical digital transformation initiatives, instead of firefighting.


1. Leverage machines for automation

First, it's time to recognize that leveraging machines for automation is key to not only achieving key business outcomes, but to reducing burden on the humans that build and maintain digital operations. Beyond automating manual tasks, the right tools can reduce alert fatigue and cut down on system noise by using a mix of data science techniques and machine learning to intelligently group alerts and remove interruptions. In turn, automation empowers teams to balance critical workloads, helping humans to work smarter and reduce the burden. This is paramount when teams are tightly staffed due to attrition, inability to back-fill, or just new team members

2. Adopt a flexible tech stack

Second, technical teams must adopt a flexible tech stack that addresses a multitude of unique business needs at scale. Businesses should look for tools that can easily plug into their existing systems, while maintaining security and compliance. When the market can change at a moment's notice, teams must have the resources at their disposal to react to change as it happens to minimize disruption to their workloads and to operations.

3. Shift from reactivity to proactivity

Finally, we must shift from reactivity to proactivity. The same report as above found only 8% of teams are currently classified as proactive. Proactive businesses often use intelligence to identify root problems to anticipate and prevent disruption down the line. We must help DevOps teams move toward a state of proactivity and prevention to manage and maintain their IT infrastructure's consistency, reliability, and resilience — which will in turn help teams streamline work and free up time.

Get Started

The path to improved incident response depends on where your business falls within the spectrum of operational maturity.

Those still in the manual and reactive stage must start small and stay focused. Put energy into turning manually documented steps into automated steps to enable opportunities for pockets of automation across your organization.

Companies in the responsive stage should work to standardize the incident response process and enable self-service. Standardization helps to build automation that can be reused across teams and services, while self-service empowers more than just your subject matter experts to leverage automation for greater value.

Once you're in the proactive stage, you should be running automation in response to incidents, creating auto-remediation capabilities, and removing some of the real-time burden placed on teams that do critical monitoring and remediation work.

This next phase of incident response will build resilient enterprises in the face of constant challenges. Once we combine the expertise of humans and machines to enable humans to do their most innovative work and embrace an approach that is automated, flexible, and proactive, teams will be able to do their jobs more efficiently and effectively than ever before.

Dan McCall is VP of Product Management, Incident Response, at PagerDuty

Related Links

Hot Topics

Related Vendors

The Latest

Manufacturing Organizations Doubled AI Investment Yet Only 37% Fully Prepared to Operationalize AI

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Optimizing Decisions with Edge-First Cognitive Intelligence

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

The New Perimeter Is the User: Why Identity Is the Real Network Edge

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

When AI Infrastructure Stops Behaving Like Infrastructure

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Enterprise Resilience: Understanding the Shift from Static to Dynamic

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile Users Expect Perfection in 2026

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

AI Confidence Is High. Readiness Is Not. What the Data Tells Us - and Why It Matters

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI, Realistic Expectations and the Future of IT Operations in 2026

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

Trust, But Verify: Building Confidence in AI Through Human Oversight

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

Data Centers Plan to Reduce Reliance on Grid

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...