How are teams using AI without losing control of their Jira development workflow?

[Wyatt Lewis]

AI-assisted development is moving pretty quickly, but I’m curious how other teams are handling the practical side of it.

We’re seeing more teams use AI for coding, documentation, ticket analysis, testing, and repetitive engineering tasks, but the challenge seems to be keeping everything traceable through the existing Jira workflow.

For example:

Are you using AI to create or refine Jira issues?
How are you reviewing AI-generated code before it moves through the workflow?
Are you connecting AI agents to Jira Automation?
What do you do when an AI-generated change needs human approval?
Have you found a good way to measure whether AI is actually improving delivery rather than just increasing activity?
I’ve been looking at how engineering teams approach this from a production perspective, and Software Developers Pro has also been exploring the broader shift toward AI-assisted engineering rather than treating AI as a separate tool.

Curious what has actually worked for teams here, especially in larger Jira environments. Are you keeping AI mostly outside the workflow, or starting to make it part of the development process?