PagerDuty launched a new release of its Digital Ops platform, applying machine learning and automation to break down the complexity of managing digital operations by reducing interruptions and minimizing the time to resolve issues.
PagerDuty’s latest platform release empowers teams in both large enterprises and high growth disruptors to prevent incidents that cause customer dissatisfaction and negative business impact, so they can confidently scale services and accelerate initiatives that capitalize on strong consumer demand for digital services.
“We now live, work, and learn primarily online. Digital is the new operating system, and operations teams are now on the frontline that keeps businesses running, as they manage the technologies that deliver the customer experience and revenue,” said Jennifer Tejada, CEO at PagerDuty. “These teams deserve a cloud native, real-time platform designed for unpredictable emergent work that automates in service of people. PagerDuty is the modern platform for action in the digital default world.”
The new capabilities in Event Intelligence (an event management and AIOps solution within PagerDuty’s platform) are as follows:
- Intelligent recommendations – New machine learning capabilities in PagerDuty’s Event Intelligence offering automatically identify the noisiest services and provide recommendations to reduce noise so teams can focus on the incidents that matter. While Event Intelligence has long been known for noise reduction, what’s new is the prescriptive nature of the recommendations which are powered by PagerDuty’s machine learning and don’t require training to use.
- Change impact mapping – By linking changes in a customers’ software deployment pipeline with incidents in its digital operations, PagerDuty allows teams to quickly find and resolve an incident’s root cause. With an estimated 80% of incidents caused by change1, this is an important advancement. This new capability integrates change events from code repositories via new integrations with GitHub, Puppet, and Evolven.
- Dynamic service dependencies – New capabilities in PagerDuty’s Service Directory which provides a single view of a customers’ entire digital operations, dynamically identifies dependencies between people, changes, incidents, and services in real time. It then applies machine learning to automatically keep a company’s service directory up to date, preventing redundant work between teams, and surfacing recommendations for automation of incidents without needing to follow manual steps, or learn advanced skills.
- Flexibly automation controls – Applying AI and automation to something as critical as a company’s digital operations requires complete trust. The platform now includes flexible automation controls to safely ensure that a human is in control at all times by pausing incident notifications, to give systems a chance to auto-remediate before a responder steps in, and by providing push button automation so teams can run automated response play and monitor results.
Industry-first advanced new insights and analytics capabilities in the PagerDuty platform are as follows:
- Analytics Lab – Building on PagerDuty’s powerful new analytics API, the Lab curates the most important queries from teams and provides them with an easy to use interface so technical and business leaders alike can extract insights from PagerDuty’s deep dataset.
- Analytics Maturity Model – This new release codifies PagerDuty’s Maturity Model and benchmark data from over 13,300 customers to help users gauge where their businesses are in their digital journey. It provides recommendations on how teams can improve their practices, for example by notifying leaders when team members are consistently working outside business hours, so schedules can be more equally distributed to avoid burnout.
“Nearly every company (92%) is accelerating their digital transformation2, subsequently increasing the complexity of their tech ecosystem. Managing that complexity and improving the performance of their digital operations is business critical,” said Jonathan Rende, SVP of Products at PagerDuty. “The road to modern DevOps practices and full service ownership is hard. Our new release unlocks the power of our rich data set to align teams around their most critical business services so they can resolve incidents up to 67% faster.”
According to Nancy Gohring, Senior Research Analyst at 451 Research, “The incident management category is important in terms of collecting and analyzing data about automations that have been performed, and in terms of collecting and analyzing data in order to identify impactful activities to automate. These functions can help drive the success of new and ongoing automation efforts. PagerDuty is one vendor that has been emphasizing and enabling these capabilities, surfacing data about incident resolutions that allow users to make data-driven decisions about what activities to automate and what automations have been performed.”
In addition to the platform release, PagerDuty is simplifying pricing and adding more value to each plan.
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.