Operations | Monitoring | ITSM | DevOps | Cloud

For most enterprises, ServiceNow has become the backbone of IT operations, the platform where workflows are governed, compliance is maintained, and every incident, change, and request is tracked from start to finish. If you’re running ServiceNow, you’ve made a serious investment in how your IT operates. PagerDuty is built to make that investment work even harder.

We recently updated our Schedules to better reflect how teams are currently managing their on-call responsibilities. Not everyone is working on weekly shifts or providing 24×7 coverage for all of their services, and that should be easy to schedule in our new tooling. To give you some examples, I’ve gone back through some of the questions we’ve gotten on the PagerDuty Commons over the past couple of years for questions about custom schedules that we weren’t really thinking about.

A year ago, AI coding tools worked alongside developers—suggesting the next line, completing a function, accelerating work that a human was already doing. Today, they’re writing entire modules and services independently, producing code that no human has reviewed line by line, built from components that no single person has fully mapped. And adoption is only accelerating: According to our recent AI Resilience Survey, 84% of organizations are now using AI to write, review, or suggest code.

This blog post is part of PagerDuty’s ongoing series on how we’re helping customers navigate their journey toward autonomous operations. Read on to learn about the PagerDuty agent app in GitHub (Early Access) and how it builds toward this vision. How many tabs do you have open right now? And how many more do you open the moment an incident hits? Context switching during incident response is one of the most persistent sources of toil in engineering.

This blog post is part of PagerDuty’s ongoing series on how we’re helping customers navigate their journey towards autonomous operations. Read on to learn about how AI Orchestrations builds towards this vision. “We should automate this.” Sound familiar? For many operations teams, that sentence never becomes action. Building event orchestration rules demands deep platform expertise, time no one has, and the ability to spot which patterns in your data actually matter.

Organizations are no longer asking whether to adopt AI; that question is settled. The focus now is on reaching a point where AI is doing meaningful operational work—or as the industry calls it, being “AI-first.” But being “AI-first” isn’t binary. You don’t go from zero AI to meaningful autonomy by flipping a switch. In reality, getting there means moving through distinct stages.

Engineering teams have plenty of ideas, but they’re often short on time to act on them. As software systems grow more complex, an increasing share of engineering capacity is consumed by non-building activities: investigating alerts, coordinating fixes, and managing operational incidents. Every hour spent diagnosing failures is an hour not spent shipping features or experimenting with new product ideas. Over time, that lost capacity compounds.

The impact vision for PagerDuty.org is to enable mission-driven teams to build a resilient world and a sustainable future for all. As a leader in modern, AI-first operations, we know that operational excellence supercharges social impact. As artificial intelligence rapidly reshapes the social sector, this commitment to resilience and efficiency has never been more vital.

When a major IT incident hits, most organizations know what it costs in the moment: lost transactions and missed SLAs. But according to the findings of our 2026 State of AI-First Operations report, the most significant consequences often don’t show up until long after the incident is closed—in customer relationships, team health, and brand reputation.

A majority of office professionals (72%) believe they understand how to use AI for their job better than the team responsible for managing AI at their company. While it’s encouraging to see employees embrace AI with such confidence, organizations will want to ensure they are providing the tools, guidance, and safeguards needed to help employees use AI safely.