The trends in our changing workflows

With the focus of this AI Builders Week on reimagining entire workflows in the product development lifecycle, we saw a broad range of challenges being addressed. Here were a few of the common themes: Keeping plans and strategy aligned as work moves forward A number of our teams built solutions to tackle a common problem that can escalate as teams move faster with AI: teams lose track of key goals and need a compass that aligns them back to the big picture. One team built an agent that monitors changes to projects in Jira and gets Rovo to reevaluate their documented plan, flagging where the work has diverged from the key goals. Another project had AI run the first round of review on strategy documents with a clear set of criteria before humans checked it, to reduce the amount of time reworking for stakeholder alignment. Bringing customer input closer to the building cycle Many of our teams focused on the opportunity AI presents to integrate insights and feedback from customers into actionable work in the pipeline.

Historically, this process has been very manual: someone reads the customer feedback, someone rewrites it as a requirement, then someone routes it to the right place. Always customer fanatics, our teams had tons of energy for using AI to make this better: One team built an AI agent that pulls customer requests from Jira and automatically turns each one into a structured work item, complete with a PRD, priority recommendation backed by customer vote counts, linked tickets and strategic fit. The agent then hands the highest-priority items to a coding agent that drafts a review-ready pull request in Bitbucket. Another team built an AI-powered feedback skill for Rovo CLI that pulls scattered customer feedback on our MCP from sources like GitHub, the Atlassian Developer Community, and Hacker News, and distills it into a clean digest of themes, jobs-to-be-done, use cases, quotes, and stats.