From bug reported in Slack to assigned ticket, QA handoff, and weekly performance report — all without anyone lifting a finger.
Engineering teams lose hours every week writing tickets, chasing status updates, and compiling reports. Issues get posted in Slack and forgotten. AI Project Manager plugs into Slack and Azure DevOps and handles all of it — automatically. Here's exactly how it works.
An engineer spots a problem and describes it in a Slack channel — the way they already communicate. A structured format with description, steps to reproduce, and expected behaviour, written naturally in the channel.
No tool to switch to, no form to fill out. The AI Project Manager watches the channel and picks up everything that gets posted.
Within moments, the AI creates a formal ticket in Azure DevOps and posts a confirmation back in the same Slack channel. Everyone can see the issue is captured — ticket number, title, summary, and a credit to the original reporter.
No one needs to wonder whether something was picked up. The AI makes it visible right where the conversation happened.
The same process runs across every channel the AI monitors. Here in the ACME-SaaS-release1 channel, production issues — a 500 error on login and a dashboard latency spike — were each captured as tickets (#4101, #4102) without anyone opening Azure DevOps.
Whether your team has two channels or twenty, the AI covers all of them at once. Nothing slips through because it was reported in the "wrong" place.
Each engineer receives a direct Slack message with their specific tickets, the branch to work on, a plain-English description of the problem, and a clear numbered task list. No ambiguity about what needs doing or where to start.
Mike Hanson gets the Firefox drag-and-drop regression and the badge count bug — each with the branch name and three steps: pull, fix, PR into development.
At the same time, James Pottery receives his own DM with different tickets — the login 500 error and dashboard latency spike — and instructions tailored to the nature of those bugs: review the hotfix, add guards to prevent regression, then PR.
The AI runs for every engineer simultaneously. The message reads like it came from a senior PM who actually reviewed the work, not a generic notification.
Once tickets are ready, the AI DMs the QA engineer with exactly which tickets to review and close — no email chain, no status meeting, no one needing to remember to follow up.
The handoff from engineering to QA, which is often the most forgotten step in a sprint, now happens automatically and is fully documented in the conversation history.
Henry Hanson, the team leader, gets a direct message from the AI with a performance summary covering his engineers — tickets closed last week and over the past month, average resolution time, and plain-English context on any outlier tickets that took longer than expected.
The kind of report that used to require manually pulling data from a project board now arrives in a DM, on a regular cadence, without interrupting anyone.
AI Project Manager handles the entire administrative layer of software delivery so your team can focus entirely on building.
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