What Stepsize AI is
A reporting layer over project management data. It connects to a Jira board or Linear team, reads the activity and produces metric reports with written commentary, so a manager gets a readable summary instead of a wall of tickets.
The commentary is the differentiator: the report explains what changed, what stalled and what deserves attention.
What you can do with it
- Connect a Jira board or Linear team as a data source
- Receive generated metric reports on a schedule
- Read AI commentary on what the numbers mean
- Share reports with stakeholders outside the delivery team
- Start with a free first report before committing
Who it is for
- Product managers tracking delivery across teams
- Engineering leads who report upward
- R&D managers who need recurring status summaries
- Teams that want fewer manual report-building sessions
What to watch out for
- Commentary is generated from ticket data, so inaccurate tickets produce inaccurate reports
- Reports about people can read judgementally; review before wide circulation
- Confirm data access scope and retention with the vendor before connecting production boards
- The free first report is a sample, not the whole evaluation; test a full cycle
Pros & cons
✓ What we like
- Turns ticket activity into readable reports
- Works with Jira and Linear out of the box
- Saves recurring manual reporting work
- First report is free to evaluate
! What to watch out for
- Quality is bounded by ticket hygiene
- Generated commentary needs review before sharing
- Per-board pricing can add up across many teams
FAQ
Which tools does it read from?
Jira and Linear. Reports are generated per board or per team.
What is in a report?
Metrics from the project data plus AI-written commentary explaining changes and anomalies.
Can the commentary be wrong?
Yes. It is generated from ticket data, so it needs a human read before circulation.
Last reviewed: 2026-09-15
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