Sprint Planning with AI – Facilitation Checklist
Purpose: Leverage AI to improve forecasting, workload balancing, and planning efficiency while keeping the process human-centered.
Tools Overview
- 1. ChatGPT: Prompt-based story estimation, coaching, and sprint summaries
- 2. ClickUp AI: Backlog refinement, estimation suggestions
- 3. Jira AI: Forecasting, DoR validation, story analysis
- 4. Tability AI: Sprint goal tracking, team sentiment
- 5. Forecast by Tempo: Historical velocity and capacity analysis
- 6. MS Copilot: Sprint summaries, meeting insights
- 7. Power BI GPT: Visualize sprint risks and trends
Before Sprint Planning
- 1. Backlog Refinement AI Suggestions Reviewed
| Tools | Use AI to: | Output |
|---|---|---|
| ClickUp AI, Jira AI, ChatGPT |
|
A cleaner, pre-prioritized backlog to discuss |
- 2. Story Estimations Prepared with AI Support
| Tools | Prompt | Output |
|---|---|---|
| ChatGPT, Jira AI, GitHub Copilot | “Estimate this story based on past items: [story details]” | Reference estimate for discussion (not a decision) |
- 3. Historical Velocity Analyzed
| Tools | Output |
|---|---|
| Forecast by Tempo, Jira AI Assistant, Power BI AI | Sprint capacity baseline |
- 4. Workload Balance Report Prepared
| Tool | Prompt | Output |
|---|---|---|
| ChatGPT or AI dashboard from project data | “How is work distributed across team members over the last 3 sprints?” | Insights to avoid over/underloading individuals |
During Sprint Planning
- 5. AI as a Co-Pilot in Story Discussion
| Use AI for: | Prompt |
|---|---|
|
“What questions should we ask about this story?” |
- 6. Sprint Goal Suggestions from AI
| Tools | Prompt |
|---|---|
| Tability AI, ChatGPT | “Suggest a sprint goal for these top 5 stories” |
- 7. Sprint Forecast Visuals Presented
| Graphs to show: | Output |
|---|---|
|
Visual alignment of scope, velocity, and capacity |
- 8. Ready State or Definition of Ready (DoR) Checked with AI
| Tool | Prompt | Output |
|---|---|---|
| Custom checklist + ChatGPT Prompt | “Does this story meet the DoR: [include criteria]?” | Auto-flagging of missing data, unclear descriptions, or risky assumptions |
After Sprint Planning
- 9. AI-Generated Sprint Summary Created
| Tools | Prompt | Output |
|---|---|---|
| Notion AI, ChatGPT, MS Teams Copilot | “Summarize sprint forecast, goal, and risks” | Post-meeting doc shared with team & stakeholders |
- 10. Emerging Risk Patterns Logged for Inspection
| Tool | Look for: | Output |
|---|---|---|
| Jira AI, Power BI GPT |
|
Insights for future refinement and retrospectives |
Scrum Master Reflection Prompts
- 1. “How did the team feel about using AI in estimation?”
- 2. “Was there any reduced interaction due to tool usage?”
- 3. “Did we rely too much on AI, or did it truly support better conversations?”
Ethical & Human-Centered Use Guidelines
| S.No | Risk Description | Mitigation Strategy |
|---|---|---|
| 1 | Over-reliance on AI estimates - Team may skip conversation and accept AI-generated effort blindly | Use AI estimates as starting points, never as final decisions |
| 2 | Reduced team collaboration - AI suggestions may bypass team discussion, especially for remote teams | Discuss AI outputs together before accepting or rejecting them |
| 3 | Loss of ownership - Team may feel disconnected if backlog is too “AI-refined” in advance | Let the team revalidate AI-prepared stories and give space to challenge AI input |
| 4 | Data privacy in analytics - AI tools may analyze sensitive sprint data | Ensure tools follow org’s data policy; anonymize if needed |
| 5 | Sprint Goal creativity reduced - AI-generated goals may sound generic | Let the team refine and “own” the final sprint goal language |
If you are interested in learning more about how to integrate AI into your Sprint Planning then join our upcoming AI courses:
Are you already using AI in your team? Please share your experience and use cases.
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