PagerDuty announced new PagerDuty Operations Cloud capabilities to rapidly identify time-sensitive opportunities and incidents while freeing up team capacity and improving efficiency.
These updates expand PagerDuty’s automation capabilities across the entire product portfolio and enable customizable responses when incidents occur in real time, by responders, or even customer service and business teams. The new Incident Workflows capabilities streamline complex orchestration of processes during an incident and make the response process more rapid, automated and consistent.
PagerDuty has also integrated Automation Actions across the Operations Cloud experience to orchestrate automated diagnostics and remediation steps.
“Modern work realities and the systems teams use aren’t aligned, and knowledge workers are facing increased operational complexity as a result of continued digitization and a massive influx of data,” said Sean Scott, chief product officer at PagerDuty. “Today’s businesses must have operations that transform from manual, reactive, ticket queue-based systems to those that are heavily automated and empower proactivity across the entire organization.”
A brand-new capability, Incident Workflows are quick to design and activate, with no-code, if-this-then-that logic that simplifies setup and accelerates time to value. With the automation Incident Workflows deliver, businesses can trigger sequences to common incident actions, such as sending automated status updates to key stakeholders, creating per-incident communications channels that immediately add relevant responders, and more. These customizable workflows enable consistent, predictable responses across the organization. PagerDuty Incident Workflows will be in early availability later this year.
Now integrated across the entire PagerDuty platform, Automation Actions enables easy automated diagnostics and remediation. Automation capabilities can now be immediately triggered by events via PagerDuty’s Event Intelligence or manually during incidents by responders to diagnose and remediate issues and improve customer experience and support. With Automation Actions, responders are able to run automated diagnostics to investigate status, gain context or directly initiate runbook automation to remediate an incident as it occurs and prevent paging a subject matter expert.
With Automation Actions for Customer Service Operations, agents are now empowered to validate customer impacting issues by running automated actions directly from the PagerDuty application in the Salesforce Service Cloud. This can reduce resolution time, as well as the number of incidents escalated to back-end teams.
More than 22 new features and other product enhancements were introduced to customers along with Incident Workflows and Automation Actions, including: Terraform support for Event Orchestration, Status Update Notification templates, Service Standards, Service Performance report, and an integration with Salesforce Service Cloud Incident Object.
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.