AI development doesn't fit neatly into traditional sprint frameworks. Model training can take days, experiments yield unexpected results, and "done" is harder to define when dealing with probabilistic outputs. This template helps you structure AI work while preserving the flexibility ML teams need.
Why AI Sprints Are Different
Traditional vs. AI Sprint Challenges
Traditional Software
- • Predictable task duration
- • Clear definition of done
- • Deterministic outputs
- • Linear progress
AI/ML Development
- • High uncertainty in timelines
- • "Good enough" thresholds
- • Probabilistic outcomes
- • Iterative experimentation
Sprint Planning Template
Copy and paste this template for your AI sprint planning sessions:
╔══════════════════════════════════════════════════════════════╗ ║ AI SPRINT PLANNING DOCUMENT ║ ╠══════════════════════════════════════════════════════════════╣ SPRINT OVERVIEW ═══════════════════════════════════════════════════════════════ Sprint Number: [e.g., Sprint 24] Sprint Dates: [Start Date] - [End Date] Sprint Duration: [e.g., 2 weeks] Sprint Goal: [One sentence describing what success looks like] TEAM CAPACITY ═══════════════════════════════════════════════════════════════ Team Member Role Available Days Notes ───────────────────────────────────────────────────────────── [Name] ML Engineer [X] days [PTO, etc.] [Name] ML Engineer [X] days [Name] Data Engineer [X] days [Name] PM [X] days [Name] Designer [X] days ───────────────────────────────────────────────────────────── Total Capacity: [X] person-days CAPACITY ALLOCATION (AI-SPECIFIC) ═══════════════════════════════════════════════════════════════ Category Allocation Person-Days ───────────────────────────────────────────────────────────── Planned Feature Work 50-60% [X] days Experimentation/Spikes 20-25% [X] days Technical Debt/MLOps 10-15% [X] days Buffer for Unknowns 10-15% [X] days ───────────────────────────────────────────────────────────── Total: 100% [X] days SPRINT BACKLOG ═══════════════════════════════════════════════════════════════ COMMITTED WORK (High Confidence) ──────────────────────────────────────────────────────────────── ID Story Points Owner Status ──────────────────────────────────────────────────────────────── AI-101 [Feature/task description] [X] [Name] To Do AI-102 [Feature/task description] [X] [Name] To Do AI-103 [Feature/task description] [X] [Name] To Do STRETCH GOALS (If Capacity Allows) ──────────────────────────────────────────────────────────────── AI-201 [Feature/task description] [X] [Name] To Do AI-202 [Feature/task description] [X] [Name] To Do ACTIVE EXPERIMENTS ═══════════════════════════════════════════════════════════════ Exp ID Hypothesis Success Criteria Time-box ───────────────────────────────────────────────────────────── EXP-01 [If we..., then...] [Metric > X] [X] days EXP-02 [If we..., then...] [Metric > X] [X] days SPIKES & RESEARCH ═══════════════════════════════════════════════════════════════ Spike ID Question to Answer Output Expected Time-box ───────────────────────────────────────────────────────────── SPK-01 [Technical question] [Doc/POC/Decision] [X] days SPK-02 [Technical question] [Doc/POC/Decision] [X] days MODEL/DATA DEPENDENCIES ═══════════════════════════════════════════════════════════════ Dependency Owner Status ETA ───────────────────────────────────────────────────────────── [e.g., Training data v2] [Name] [Status] [Date] [e.g., GPU cluster access] [Name] [Status] [Date] [e.g., API rate limits] [Name] [Status] [Date] RISKS & BLOCKERS ═══════════════════════════════════════════════════════════════ Risk Likelihood Impact Mitigation ───────────────────────────────────────────────────────────── [Risk description] H/M/L H/M/L [Plan] [Risk description] H/M/L H/M/L [Plan] DEFINITION OF DONE (AI-Specific) ═══════════════════════════════════════════════════════════════ [ ] Model meets accuracy threshold ([X]% on test set) [ ] Latency within SLA ([X]ms p95) [ ] Evaluation metrics logged to experiment tracker [ ] Model registered in model registry [ ] A/B test framework integrated (if applicable) [ ] Monitoring dashboards updated [ ] Rollback procedure documented [ ] Code reviewed and merged [ ] Documentation updated CARRYOVER FROM LAST SPRINT ═══════════════════════════════════════════════════════════════ ID Story Original Points Remaining ───────────────────────────────────────────────────────────── [ID] [Description] [X] [X] NOTES & DECISIONS ═══════════════════════════════════════════════════════════════ • [Key decision or note from planning] • [Key decision or note from planning] ╚══════════════════════════════════════════════════════════════╝
Capacity Planning for AI Teams
The 50/25/15/10 Rule for AI Sprints
Planned Feature Work
Committed deliverables with high confidence. Features that build on proven approaches.
Experimentation & Spikes
Time-boxed exploration. Testing new architectures, data sources, or approaches.
Technical Debt & MLOps
Pipeline improvements, monitoring, model registry, retraining automation.
Buffer for Unknowns
Model debugging, unexpected data issues, production incidents.
AI Story Point Estimation
Traditional story points don't work well for AI work. Use this modified scale that accounts for uncertainty:
AI STORY POINT SCALE
═══════════════════════════════════════════════════════════════
Points Traditional AI Interpretation
─────────────────────────────────────────────────────────────
1 Few hours Well-understood, deterministic task
(e.g., update config, fix bug)
2 Half day Standard task with known approach
(e.g., add metric, update pipeline)
3 1 day Moderate complexity, proven pattern
(e.g., retrain with new data)
5 2-3 days Some uncertainty, may need iteration
(e.g., tune hyperparameters)
8 1 week Significant uncertainty, new territory
(e.g., test new architecture)
13 1-2 weeks High uncertainty, research required
(e.g., solve novel problem)
21 DON'T USE Break this down further
Story is too large/uncertain
ESTIMATION MULTIPLIERS FOR AI WORK
─────────────────────────────────────────────────────────────
Factor Multiplier
─────────────────────────────────────────────────────────────
New model architecture 1.5x
New data source 1.3x
Production deployment (first time) 1.5x
Real-time inference requirement 1.3x
Regulatory/compliance involved 1.5x
Cross-team dependency 1.2xAI-Specific Sprint Ceremonies
Recommended AI Team Ceremonies
Experiment Review (Weekly, 30 min)
Review active experiments, share learnings, decide to continue/pivot/stop. Not a status update - focus on insights and decisions.
Model Health Check (Weekly, 15 min)
Review production model metrics, drift alerts, error rates. Quick triage of any degradation.
Data Quality Standup (2x/week, 10 min)
Quick check on data pipeline health, new data availability, labeling progress.
ML Demo Day (End of Sprint, 45 min)
Show experiments (successful AND failed), model improvements, new capabilities. Celebrate learning, not just shipping.
AI User Story Templates
Use these templates to write AI-specific user stories:
AI USER STORY TEMPLATES ═══════════════════════════════════════════════════════════════ MODEL IMPROVEMENT STORY ───────────────────────────────────────────────────────────── Title: Improve [model name] [metric] from [X] to [Y] As a [user type], I want [the model to perform better at X], So that [business outcome]. Acceptance Criteria: • Model achieves [metric] >= [threshold] on [test set] • Latency remains under [X]ms p95 • No regression on [other important metrics] • A/B test shows [X]% improvement in [business metric] Technical Notes: • Current baseline: [metric value] • Proposed approach: [brief description] • Data requirements: [new data needed] • Estimated training time: [X] hours ───────────────────────────────────────────────────────────── EXPERIMENT STORY ───────────────────────────────────────────────────────────── Title: [EXP] Test [hypothesis] Hypothesis: If we [change/approach], then [expected outcome] because [reasoning]. Success Criteria: • Primary: [Metric] improves by [X]% • Secondary: [Other metrics] don't regress Time-box: [X] days Output: Decision document with recommendation Experiment Design: • Control: [Current approach] • Treatment: [New approach] • Sample size: [N] • Duration: [X] days ───────────────────────────────────────────────────────────── DATA PIPELINE STORY ───────────────────────────────────────────────────────────── Title: [DATA] Add [data source] to [pipeline] As a ML engineer, I want [new data integrated into pipeline], So that [models can use this signal]. Acceptance Criteria: • Data flows to [destination] within [X] hours of generation • Schema validation passes • Data quality checks: [list checks] • Backfill completed for [date range] • Documentation updated ───────────────────────────────────────────────────────────── SPIKE STORY ───────────────────────────────────────────────────────────── Title: [SPIKE] Investigate [question] Question to Answer: [Clear, specific technical question] Output Expected: • [ ] Decision document • [ ] Proof of concept • [ ] Architecture proposal • [ ] Go/no-go recommendation Time-box: [X] days (STRICT) Out of Scope: • Production-ready implementation • Full testing • Documentation beyond findings
Common AI Sprint Planning Mistakes
Mistake: Treating experiments like features
Problem: Committing to ship experiment results creates pressure to confirm hypotheses.
Fix: Commit to running the experiment, not to the outcome. Success = learning.
Mistake: No buffer for model debugging
Problem: Models fail in unexpected ways. 100% planned capacity = guaranteed overcommitment.
Fix: Always reserve 10-15% for unknowns. Use it for stretch goals if not needed.
Mistake: Ignoring data dependencies
Problem: Model work blocked waiting for data that was "supposed to be ready."
Fix: Explicitly list data dependencies with owners and track status daily.
Mistake: Vague acceptance criteria
Problem: "Improve model accuracy" leads to endless iteration with no clear done.
Fix: Specific thresholds: "Achieve 85% precision on test set v2.1"
Mistake: Skipping MLOps investment
Problem: Technical debt accumulates, making every sprint slower.
Fix: Dedicate 15% of every sprint to MLOps improvements. Non-negotiable.
AI Sprint Retrospective Questions
AI SPRINT RETROSPECTIVE TEMPLATE ═══════════════════════════════════════════════════════════════ WHAT WENT WELL ───────────────────────────────────────────────────────────── • [Celebration/win] • [Effective practice to continue] WHAT DIDN'T GO WELL ───────────────────────────────────────────────────────────── • [Challenge/blocker] • [Process that needs improvement] AI-SPECIFIC REFLECTION ───────────────────────────────────────────────────────────── 1. Did our experiments yield useful learnings (even if negative)? 2. Were our model performance estimates accurate? 3. Did we encounter unexpected data issues? 4. Was our buffer allocation appropriate? 5. Did MLOps debt slow us down? VELOCITY ANALYSIS ───────────────────────────────────────────────────────────── Committed Points: [X] Completed Points: [X] Experiment Success Rate: [X/Y] yielded actionable insights Carryover Reason: [Why items weren't completed] ACTION ITEMS ───────────────────────────────────────────────────────────── [ ] [Specific improvement] - Owner: [Name] - Due: [Date] [ ] [Specific improvement] - Owner: [Name] - Due: [Date]
Related Templates
AI Experiment Brief Template
Structure your AI experiments with clear hypotheses and success criteria.
AI Feature PRD Template
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AI Incident Postmortem Template
Learn from AI failures with blameless postmortem documentation.
AI Stakeholder Update Template
Keep leadership aligned with AI-specific progress updates.