PagerDuty announced the introduction of AI-generated runbooks in early access, as well as powerful new analytics capabilities for the PagerDuty Operations Cloud℠.
Using PagerDuty Runbook Automation from the Operations Cloud, customers replace manual procedures with automated self-service workflows, potentially saving hundreds of working days per year. With PagerDuty’s new generative AI capabilities, customers could realize hundreds of thousands of dollars in annual operational cost savings by using natural-language prompts paired with prompt-engineering automation – high quality prompts iteratively guiding users – to initiate low-code autonomous runbooks for frequently deployed workflows and procedures within a company's IT operations process.
“Autonomous runbooks are a huge leap towards safe auto-remediation of critical applications and infrastructure when time really matters to a business,” said Jennifer Tejada, Chairperson and CEO at PagerDuty. “For large enterprises, this human-in-the loop, generative AI application can reduce operating costs by hundreds of thousands of dollars annually, mitigate risk and also support teams in protecting and growing revenue through ensuring more reliable customer-facing applications.”
This new feature in PagerDuty Runbook Automation is the latest in a series of PagerDuty’s GenAI capabilities, which will be available across the PagerDuty Operations Cloud to help organizations automate time-critical, high-impact work, improve productivity and meaningfully reduce operating costs in uncertain economic times.
PagerDuty Insights Reports help engineering and IT leadership act with greater confidence and advance operational maturity with data that predicts and prevents issues.
Additionally, PagerDuty's newly generally available analytics capabilities are available to all paying customers. The PagerDuty Insights Reports have been designed to provide teams with more granular visibility and control over operational health and maturity. Customers can now glean critical insights into the state of their operations, including which services are most impacted, SLA achievement, and team health including how many sleep/off-hour interruptions responders are receiving.
Customers leveraging PagerDuty analytics improved mean time to acknowledge (MTTA) incidents by 28%, as well as more equitable distribution of work and consistent response hours, equating to saving 100 hours of work time per year, per team. PagerDuty users can also utilize the Recommendations Report for potential noise compression, and a User Onboarding Report (early access) to help Admins and Managers understand which of their responders have activated, set up and are using their PagerDuty account properly.
“Both automation and data-driven, actionable insights are imperative for today’s organizations to succeed amidst a constantly evolving environment, lower barriers to entry and the accelerating pace of innovation,” said Sean Scott, Chief Product Development Officer at PagerDuty. “By leaning into the power of GenAI and the democratization of analytics, PagerDuty customers can speed up and ameliorate their operations, reduce costs and redeploy resources to delivering innovative solutions for their customers.”
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.