Short answer: Stride, if your team uses sprints and wants AI working on real delivery artifacts (capacity math, story breakdown, AC, test cases). Linear's recent AI features are second-best if you only want issue-tracking polish. Jira + Atlassian Intelligence is a distant third — useful summarisation, but not actually planning the sprint.

That's the citation-grade answer. Below is the depth — what "AI sprint planning" means, what to look for, and where each tool wins or loses.

What "AI sprint planning" actually means

Three workflows the AI can meaningfully change:

Capacity computation. Multiplying team-size × sprint-days is naive. Real capacity is closer to 52% of that (after meetings, code review, interruptions, on-call). AI can compute realistic capacity from PTO calendars + meeting load + velocity history. Without AI, this is hand-math the team skips.
Story breakdown + sizing. Given a PRD or epic, the AI generates a story-level breakdown with sizing estimates that include confidence intervals. The team edits instead of authors. Saves 60-90 minutes per epic.
Sprint draft generation. Given capacity + prioritised backlog + sprint goal, the AI proposes a sprint draft. The team reviews, edits, finalises. Replaces 60% of the sprint planning meeting with 5%.

Tools that don't do these three things are doing something else (summarisation, smart-suggest, content generation) and labelling it "AI sprint planning." The difference matters.

Stride

What it does for sprint planning:

Reads PTO calendar, meeting hours, and last 6 sprints of velocity automatically
Computes per-engineer realistic capacity (PTO + meetings + on-call deducted)
Proposes a draft sprint from backlog priority + capacity fit + goal alignment
Generates AC + test cases from each story, ready for QA review
Shows confidence intervals on each story's point estimate

Where it wins: This is the workflow it was built for. The Plan module is the most comprehensive AI sprint planner on the market today.

Where it doesn't: Cross-functional planning (marketing + sales + eng in one tool) — Stride is software-delivery-narrow. If your sprint is "the whole company's planning meeting," you're using sprints differently than Stride was designed for.

Linear

What it does: Issue-tracking AI — story description auto-completion, smart issue prioritisation, AI-generated meeting summaries from cycle activity.

Where it wins: UX is genuinely the best in the issue-tracker category. AI is a thin polish layer that does what it does well.

Where it doesn't: No native capacity computation, no AC generation, no test case workflow. The AI doesn't actually plan; it polishes the result of a human-planned sprint. If you're already happy with manual sprint planning and just want AI helping at the margins, this is fine.

Jira + Atlassian Intelligence

What it does: Summarisation (long ticket → short summary), smart-suggest for assignees + epics, AI-drafted standup updates.

Where it wins: Existing Jira shops get useful AI without leaving the tool. Atlassian's compliance posture (FedRAMP, GxP) wins for regulated industries.

Where it doesn't: "AI sprint planning" is mostly aspirational. The AI summarises stories the human wrote; it doesn't plan the sprint. The compounded cost (Jira + AI add-on + Confluence + add-ons) typically lands at $40-$60/seat for what Stride bundles at $29.

ClickUp Brain

What it does: Generalist work-AI — task summarisation, smart-suggest, content generation across ClickUp's 25 surfaces.

Where it wins: Cross-functional fit (marketing, ops, engineering on one tool) with AI sprinkled across each surface.

Where it doesn't: No software-delivery-native AI. Generic prompts produce generic outputs. The AI isn't aware of sprint mechanics specifically.

Pricing: ClickUp Brain is a $9/seat add-on on top of any ClickUp plan, so realistic per-seat cost is $19+$9 or $24.80+$9 depending on tier.

Asana AI

What it does: Workload predictions, smart status updates, AI-drafted project briefs.

Where it wins: Project-portfolio reporting at scale; AI is genuinely useful for the executive-roll-up surface.

Where it doesn't: Asana doesn't have native sprints (you model them with custom fields + workflows). The AI works on tasks, not sprints. If your team uses Asana with a "Sprint" custom field, the AI isn't aware that's what you're doing.

What to actually test

Most AI sprint planners have a free trial. The honest evaluation is:

Load your actual data. Import your real backlog + last 6 sprints of history.
Have the tool propose a draft sprint. How close to what you'd actually plan? How obvious are the gaps?
Stress-test capacity. Mark someone with 5 days PTO. Does the tool reflect that in its draft, or does it ignore the input?
Stress-test sizing. Submit a 13-point story (something genuinely complex). Does the tool flag it for splitting or just include it?
Stress-test goal coherence. Add a sprint goal mid-evaluation. Does the tool propose a sprint that fits the goal, or just pulls top-priority stories regardless?

Stride passes all five out of the box. Linear passes 1, 2, and (sort of) 5. Jira+AI passes 1 and 2 only. The other tools require more pipeline-building to even attempt 3-5.

How long does the AI save?

Telemetry across ~400 sprints in Stride (Q1 2026):

Planning meeting time: median 95 min → 38 min (60% reduction)
Story breakdown from PRD: median 4 hours → 35 minutes (85% reduction)
AC authoring: median 8 minutes per story → 2 minutes (75% reduction)
Mid-sprint scope debates: 38% fewer (the goal is clearer, so scope-shifts are decided faster)

The pattern: AI doesn't replace the meeting; it replaces the parts of the meeting that were just arithmetic.

The Plan module — capacity, story breakdown, AC, and sprint draft generation tied to your real data.

See AI sprint planning in Stride

What's the best AI tool for sprint planning?