PagerDuty announced new capabilities to help teams manage the time sensitive, mission critical work that is key to business success.
These new solutions reduce toil, speed up response time with automation, tackle change impacts and identify incidents with AIOps, and build on existing real-time case management features for customer service operations teams. PagerDuty also announced two new add-on products, Runbook Actions and a new plan for customer service operations.
“Modern digital operations are critical to an organization’s ability to provide perfect customer experiences,” said Sean Scott, CPO at PagerDuty. “Digital operations management is more than just incident response, it’s about empowering teams with automation capabilities that enable the flexibility, visibility and accountability to manage all urgent work across the enterprise.”
New PagerDuty Runbook Actions: PagerDuty Runbook Actions provides diagnostic and remediation automation to incident responders so they can quickly resolve incidents safely and securely from within the PagerDuty interface. With Runbook Actions, incidents are resolved quicker, and subject matter experts avoid the disruption of frequent escalations. PagerDuty Runbook Actions will be generally available this fall.
PagerDuty for Customer Service Teams: PagerDuty’s new advanced plan for customer service teams was announced for teams to get more proactive and solve customer issues faster. With this new plan, customer service teams in Zendesk and other major customer service solutions will get real-time status updates of critical customer impacting issues and be empowered to immediately drive action, and engage with experts across the organization to accelerate resolution time and improve customer satisfaction. This new plan includes a round-robin scheduling tool that helps resource-strapped customer service teams optimize organizational resources to support the digital-first customer experience.
New PagerDuty Service Graph: PagerDuty is also announcing a capability that will empower organizations to visualize complex and rapidly changing service dependencies. The PagerDuty Service Graph will assist organizations adopting full service ownership by providing a real time view across its people and technology. Service Graph enables PagerDuty users to instantly discover, map, and visualize business and technical service dependencies and serve as a real time source of truth for downstream, real time systems. Now users can view the health of their services at a glance or assess the impact radius of an incident and zero in on probable cause. This feature will be available to all PagerDuty users later this summer.
To streamline the process of identifying and better understanding an incident’s root cause, PagerDuty added two additional innovations that enhance PagerDuty’s AIOps Event Intelligence capabilities for operations teams:
- Change Correlation: Finding the exact changes associated with an incident is critical. PagerDuty has announced new AIOps-powered algorithms to determine which changes are most likely connected to the incident, helping teams identify the root cause faster.
- Outlier Incident: Understanding if an incident is unique or frequently recurring, can help teams identify systematic issues and help target their automation efforts. Outlier Incident’s AIOps-powered analysis of historical incidents helps teams identify both of these scenarios so they can quickly address them.
The Latest
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
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.