Key Takeaway
AI sprint planning should explicitly allocate capacity for experimentation and infrastructure, because teams that only plan feature work accumulate technical debt that eventually blocks all progress.
When to Use This Template
Use this agenda for bi-weekly sprint planning sessions in AI and ML teams. Traditional sprint planning does not account for experiment-driven work, long-running training jobs, or infrastructure tasks that AI teams require. This template adds those dimensions while maintaining the discipline of time-boxed planning and clear sprint commitments.
Meeting Flow
- 1
Previous Sprint Review (10 min)
Review outcomes from the previous sprint: completed features, experiment results (including negative results, which are valuable learning), infrastructure improvements shipped, and data tasks completed. Calculate velocity and note any patterns in estimation accuracy.
- 2
Capacity Planning (10 min)
Determine available capacity for the sprint accounting for PTO, on-call rotations, and meeting overhead. Allocate capacity across three categories: feature delivery (typically 50-60%), experimentation (20-30%), and infrastructure/tech debt (15-20%). Adjust ratios based on current priorities and debt level.
- 3
Feature Work (15 min)
Review and commit to feature work items. For each: define acceptance criteria that include model performance requirements, specify evaluation criteria, identify data dependencies, and estimate effort. Do not commit to features where data dependencies are unresolved.
- 4
Experiment Planning (10 min)
Review planned experiments. For each: state the hypothesis clearly, define the experiment design (what will be tested, how), specify success criteria (metric thresholds), estimate resource requirements (compute, data), and set a time box. Experiments without a clear hypothesis and success criteria should be refined before committing.
- 5
Data and Infrastructure Tasks (10 min)
Review data tasks (collection, labeling, pipeline fixes, quality improvements) and infrastructure tasks (monitoring, tooling, automation). Prioritize based on impact on sprint feature and experiment commitments. Flag any compute constraints or cross-team dependencies.
- 6
Sprint Goals and Risks (5 min)
Summarize the sprint goals: top 3 priorities that the team is committing to deliver. Identify the top risks to sprint success: data availability, compute constraints, cross-team dependencies, and key person dependencies. For each risk, identify a mitigation or escalation plan.
Facilitation Guidance
AI sprint planning requires accepting that some work will have uncertain outcomes. Frame experiment work as time-boxed learning: the team commits to spending a defined amount of time on the experiment, not to achieving a specific result. This prevents the frustration of treating failed experiments as sprint failures. Track experiment outcomes separately from feature delivery velocity to give the team credit for valuable negative results.
Review the capacity allocation ratios quarterly. Early-stage AI teams should allocate more to experimentation (30-40%). Mature AI teams with established models should allocate more to infrastructure and optimization (25-30%). Teams that allocate zero capacity to infrastructure will eventually hit a wall where tech debt blocks all feature work.
Version History
1.0.0 · 2026-03-01
- • Initial AI sprint planning agenda template