If you’re a product manager, reporting is one of those necessary but frustrating parts of the job. Weekly status updates, sprint summaries, roadmap progress reports, they all take time.

And let’s be honest: many of us have copied Jira tickets into slides at 11pm the night before a leadership meeting.

AI can ease that load. Not by eliminating reporting altogether, but by taking care of most of the grunt work so you can focus on the story behind the numbers.

Why Reporting in Product Management Feels Broken

The pain isn’t just the time it takes. It’s the constant context switching. One minute you’re in Jira checking backlog progress, the next you’re in Microsoft Teams pulling screenshots from a channel, and then you’re stitching it all together for stakeholders who will skim it in 30 seconds.

AI can’t make your leadership team read every line, but it can dramatically reduce the effort it takes to assemble updates.

How AI Can Help in Practice

Think about all the different inputs a PM juggles: tickets in Jira or Linear, commits in GitHub, comments in Microsoft Teams. Instead of manually piecing those together, AI can pull the data, interpret it, and generate a draft update that covers progress, blockers, and next steps. The draft isn’t final—you’ll still add the context and judgment—but it means starting from a working version instead of a blank page.

In some teams, AI is even set up to deliver these updates on a schedule. After each sprint, or every Monday morning, the draft summary simply appears in Teams where the team already communicates. Most of the work is done before you even think about writing.

Practical Ways to Use AI for Reporting

1. Generating sprint summaries from Jira or Linear

At the end of a sprint, AI can scan ticket data and create a summary: what was completed, what rolled over, and what blockers emerged. You refine the output and add commentary, but most of the heavy lifting is already done.

2. Turning Teams threads into stakeholder briefs

Updates shared in Microsoft Teams often get buried. AI can gather these posts and turn them into a concise report that highlights decisions made and risks flagged. Instead of rewriting, you just approve or adjust.

3. Combining code changes with product updates

For technical products, AI can even look at GitHub commits alongside Jira tickets to explain what changed and why it matters. This helps connect engineering work directly to product outcomes in a way that’s easier for stakeholders to follow.

4. Drafting executive-ready updates

Instead of spending late nights polishing raw notes into formal updates, you can use AI to produce a first draft. One PM told me she pastes in sprint notes, and the AI gives her a structured, executive-ready version. She adds the strategic framing, and it’s done.

The Human Layer Still Matters

Reporting isn’t just about data—it’s about narrative. AI can prepare the skeleton, but only you can explain why velocity dipped, why priorities shifted, or what trade-offs need executive attention. The value isn’t in removing the PM from the loop, but in giving you back the time to focus on the story instead of the formatting.

Takeaways for Product Managers

• Use AI to automate the repetitive parts of reporting—especially pulling from Jira, Linear, GitHub, and Teams.

• Deliver updates where your team already communicates, so reporting doesn’t feel like an extra chore.

• Keep yourself in the loop to provide the judgment and framing only a PM can add.

AI won’t replace reporting in product management, but it can change the way it feels. By handling the data gathering and drafting, it frees you up to focus on meaning. Most of the work is done automatically, so you can spend your time on the insights that move the product forward.