PagerDuty has signaled its intention to add generative artificial intelligence (AI) capabilities to its cloud platform for managing IT operations via integrations with large language model (LLM) providers that it will disclose at a future date.

These capabilities will make it possible to invoke natural language via PagerDuty Operations Cloud to automatically generate status updates, create drafts of incident postmortem reports and even co-author workflows in any programming language to automate remediation of an issue.

Damon Edwards, senior director of product for PagerDuty, said as AI continues to evolve, the goal will be to minimize the need for human involvement as much as possible. That doesn’t mean there won’t be a need for humans to be involved, but it does mean that many rote tasks associated with managing an IT incident can be automated, he added.

For example, the reports that are run to determine the root cause every time there is an incident can be automated, noted Edwards. In addition, summarizations of status reports or postmortems after incidents were resolved can be made readily available to any member of an IT team on demand, he added.

Those capabilities will also make it easier to onboard new members to an incident management team as required, said Edwards. Today, it’s difficult to add new members to a team after an incident has unfolded because it takes time to bring them up to speed, he noted.

In addition, generative AI also makes it easier to capture a lot of the tribal knowledge within an IT organization that would otherwise disappear when staff either leave the company or shift roles, said Edwards.

The generative AI additions to the PagerDuty platform extend an artificial intelligence for IT operations (AIOps) capability that PagerDuty made available this spring. The PagerDuty AIOps platform leverages the data model embedded in its incident management software to reduce the amount of time required for an AI platform to learn how an IT environment operates and begin surfacing valid recommendations to optimize workflows.

It’s not clear at what pace AI will be adopted within enterprise IT organizations, but it is rapidly becoming pervasive. Each DevOps team will need to decide the degree to which they can rely on AI to manage IT processes, but as IT processes become more complex, there is a clear need for AI to navigate all the dependencies that exist in an IT environment. As more routine tasks are automated, however, there will also inevitably be a realignment of roles and responsibilities within an IT organization. The overall goal is not so much to eliminate IT staff as much as it is to enable them to manage IT environments at a much higher level of scale.

In the meantime, DevOps teams need to plan for not only what is possible using generative AI today but also tomorrow. After all, there’s not a lot of point in training someone to take on a new task if that task is automated a few months from now.