Analytics and observability firm PagerDuty Inc. is leaning further into generative AI with the introduction of artificial intelligence-generated process automation and powerful new analytics capabilities, the company announced today.

Users of PagerDuty’s Operations Cloud platform, an incident detection and management notification and response system for the enterprise, can replace manual procedures using Runbook Automation and save themselves time and energy.

Now, using the power of generative AI, a similar technology that underlies OpenAI LP’s ChatGPT chatbot that can understand conversational speech, users can use natural language to set up their own runbooks. A runbook is a documented set of procedures that explain how to complete a particular task, such as how to respond to an incident. In many cases, these can be automated, especially routine responses and remediation.

Using generative AI, employees need only describe the runbook procedures they need to the AI and it will build the runbook that they need and set up the automated processes. If the first draft of their runbook does not match their expectations, they can keep prompting it until it comes out the way they want. Finishing touches can always be fine tuned however they want by going in and editing it however they need in the low-code editor.

“Autonomous runbooks are a huge leap toward safe auto-remediation of critical applications and infrastructure when time really matters to a business,” said Jennifer Tejada, chairperson and chief executive of PagerDuty.

According to Tejada, the use of generative AI to assist employees to build their own automation can save companies millions of dollars annually by reducing operating costs and also mitigate risks. It does this by helping customers alleviate repetitive work and significantly improve productivity. All the while, it keeps humans in the loop while using the AI tools.

In addition to the generative AI updates, PagerDuty Insights Reports are now generally available to all paying customers. These reports have been to provide teams with more granular visibility and control over their operational data so that they can better understand what’s happening on their platforms.

For example, it will give them a better view of which services are being affected, level of incidents being responded to, and overall team health. Team health can include things such as how many sleep or off-hour interruptions are bringing responders online.

PagerDuty users now have access to a Recommendations Report for noise compression and a User Onboarding Report, in early access, that will help administrators and mangers understand how which responders have been activated, set up and are using their PagerDuty accounts.

Between these three reports, management will be able to better see into the overall health of their operation and also get a way to drill down into their operations as well.

“By leaning into the power of Gen AI 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,” said Sean Scott, chief product development officer at PagerDuty.

Later on the day of its announcement, PagerDuty released second-quarter results, saying revenue rose 19% from a year ago, to $107.6 million. It had a net loss of $26.2 million, with earnings before costs such as stock compensation of $14.3 million. It also forecast third-quarter revenue of $106.5 million to $108.5 million, up 13% to 15% from a year ago, with adjusted earnings of 13 to 14 cents a share. For the full year, it’s reckoning revenue of $426 million to $430 million, up 15% to 16%, with adjusted earnings of 60 to 65 cents a share.

The company’s shares were falling almost 6% in after-hours trading this afternoon.

Images: Pixabay, PagerDuty