All use cases
Plan

Let the AI fill the sprint. You spend the saved hours on actual work.

AI sprint planning that respects PTO, meetings, velocity, and uncertainty.

Most sprint planning meetings spend 60% of the time on capacity math the AI can do in 2 seconds. Stride's Plan module computes realistic capacity from PTO + meetings + historical velocity, then proposes a draft sprint your team can edit instead of author from scratch.

Why does AI sprint planning fail when it ignores PTO and meetings?

Sprint planning eats 2-4 hours per sprint for most teams, half of it spent on capacity arithmetic and the other half on debating points across stories nobody actually estimated independently. Naive capacity (team size × sprint days) systematically over-commits, and humans are bad at consistent point sizing across sessions. The result: teams chronically miss commitments, lose retro time to "we over-committed again", and burn out from feeling behind.

What goes into Stride's realistic-capacity estimate?

Stride takes the inputs the team already has (PTO calendar entries, meeting hours, last 6 sprints of velocity, backlog story points) and produces a sprint draft in 30 seconds. The team reviews and edits, replacing 60% of planning time with 5% of planning time and zero loss of judgment quality.

  • AI-computed realistic capacity per person (PTO + meetings + on-call deducted)
  • AI-suggested story selection from prioritised backlog matching capacity
  • Confidence intervals on each story estimate (not just a single number)
  • One-click "what if" re-planning when a key person's PTO changes
  • Auto-rollover of incomplete work into the next sprint with provenance
  • Velocity tracking with outlier filtering (holidays, on-call duty excluded)
Best for

Engineering teams running 1-2 week sprints with 5-50 engineers who feel they spend more time planning than working.

Not for

Teams running 8+ week milestones or rolling-wave plans where 'sprint' is a misnomer. Stride supports milestones too, but the AI-planning workflow specifically optimises the 1-2 week sprint loop.

Frequently asked

Does the AI just guess at velocity or does it actually use my team data?
It uses your last 6 sprints of completed work, filtered to exclude outliers (holidays, on-call duty). New teams without history start with conservative defaults and the model recalibrates after 2-3 sprints. You can see exactly what data fed each estimate; nothing is opaque.
What if my team uses t-shirt sizing, not Fibonacci points?
Both are supported. The AI maps t-shirt to numeric weights (XS=1, S=2, M=3, L=5, XL=8, XXL=13) internally so velocity math still works. Your UI stays in whichever scale your team prefers.
How does this work with Jira import?
Stride imports Jira stories, points, and historical sprint data via the Jira Cloud OAuth + webhook import. The AI sprint planner starts working with your real history from day 1, no warm-up period.
Can I override the AI suggestions?
Always. The AI produces a draft; the team edits. Every story has an "AI added this" badge so you can see what the model suggested vs what you decided. We measured the human-edit rate at ~25% across early users, high enough that the AI is a starting point, not a replacement.
What about external interruptions (production incidents, urgent customer requests)?
The capacity model reserves a configurable buffer (default 20% of an engineer's time) for unplanned work. You can adjust per-person or per-team. Real interruptions get logged against the buffer so retros can see how realistic the buffer actually is.