PagerDuty builds upon previous generative AI (genAI) capabilities with PagerDuty Advance, which is embedded across the PagerDuty Operations Cloud platform, including Incident Management, AIOps, Automation and Customer Service Operations customers.
With PagerDuty Advance, organizations can accelerate digital transformation initiatives — from operations center modernization to automation standardization and incident management transformation — elevating their operational excellence. The evolution of PagerDuty Advance empowers responder teams to work faster and smarter by using genAI to surface relevant context or automate at every step of the incident lifecycle.
“Global IT disruption and outages are becoming the new normal due to organizations’ technical debt and a rush to harness the power of generative AI. These are contributing factors to a greater number of outages which last longer and are more costly,” said Jeffrey Hausman, Chief Product Development Officer at PagerDuty. “Building upon our genAI offerings, PagerDuty Advance provides customers with generative AI solutions that help them scale teams by surfacing contextual insights and automating time-consuming tasks at every step of the incident lifecycle. Organizations can take the next step in unlocking the full potential of AI and automation across the digital enterprise with the help of PagerDuty.”
PagerDuty Advance includes AI-powered capabilities built to streamline manual work across the incident lifecycle, including:
- PagerDuty Advance Assistant for Slack – A genAI chatbot that provides helpful insight at every step of the incident lifecycle from event to resolution directly from Slack. Using simple prompts, responders can quickly get a summary of the key information about the incident. It can also anticipate common diagnostic questions and suggest troubleshooting steps, resulting in faster resolution.
- PagerDuty Advance for Status Updates – This feature leverages AI to auto-generate an audience-specific status update draft in seconds, offering key insights on events, progress and challenges. It helps to streamline communication while saving cycles on what to say to whom, allowing teams to focus on the real work of resolution.
- PagerDuty Advance for Automation Digest – Part of the Actions Log, this feature summarizes the most important results from running automation jobs in one place. Responders can make informed decisions based on diagnostic results and even load the output as key values into variables in Event Orchestration for dynamic automation.
- PagerDuty Advance for Postmortems – Once an incident is resolved, the user can elect to generate a postmortem review, accelerating an otherwise time-consuming task of collecting all available data around the incident at hand (including logs, metrics, and relevant Slack conversations). In addition to highlighting key findings, this AI-generated postmortem includes recommended next steps to prevent future issues and indicates areas of improvement.
- AI Generated Runbooks – AI-generated Runbooks accelerate automation development and deployment even among non-technical teams. Operators and developers can quickly translate plain-English prompts into runbook automations or leverage pre-engineered prompts as a starting point.
Interviews with early access customers revealed that PagerDuty Advance for Status Updates can save up to 15 minutes per responder per incident. Given the average number of responders responsible for status updates in enterprise organizations is five and the monthly average number of incidents is 60, PagerDuty Advance can save at least 75 hours a month; more than nine business days.
PagerDuty Advance Assistant for Slack is generally available now in the U.S. and EU service regions.
PagerDuty Advance for Status Updates is generally available now in the U.S. and EU service regions.
PagerDuty Advance for Automation Digest is generally available now in the U.S. and EU service regions.
PagerDuty Advance for Postmortems is currently in early access in the U.S. service region.
AI Generated Runbooks is currently in early access in the U.S. service region.
The Latest
Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...
AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...
Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...
Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...
Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...
In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...
Virtual Private Networks became a cornerstone of enterprise security at a time when corporate infrastructure looked very different from today ... For years, this model worked well. But the architecture behind VPNs assumed a centralized corporate environment—one where the network itself was the hub of activity. In a cloud — first world, that assumption no longer holds ...
Website outages get resolved just as fast in August as they do in November. I went looking for the opposite: the summer slowdown everyone assumes is there once the people who fix things are away. It isn't in the data we collected, covering 1.8 million confirmed outages across tens of thousands of websites ...
This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...
There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...