retrospective
**AI project retrospectives** are **structured reviews of AI initiatives to extract learnings and improve future work** — examining what worked, what didn't, and why, with special attention to AI-specific challenges like data quality, model behavior, and evaluation, enabling teams to systematically improve their AI development practices.
**Why AI Retros Matter**
- **Learn from Failure**: AI projects often fail in unexpected ways.
- **Share Knowledge**: Capture tacit knowledge explicitly.
- **Improve Process**: Fix systematic issues.
- **Build Culture**: Normalize learning from mistakes.
- **Avoid Repetition**: Don't make the same mistakes twice.
**AI-Specific Challenges to Review**
**Data Issues**:
```
- Data quality problems discovered late
- Labeling inconsistencies
- Data drift after deployment
- Insufficient training data
- Unexpected data distributions
```
**Model Issues**:
```
- Model performance vs. expectations
- Unexpected behaviors/edge cases
- Evaluation metric vs. real-world fit
- Inference costs vs. budget
- Model degradation over time
```
**Process Issues**:
```
- Scope creep during development
- Unclear success criteria
- Integration challenges
- Communication gaps (ML ↔ product)
- Timeline estimation errors
```
**Retrospective Format**
**Standard Structure** (60-90 minutes):
```
1. Set the Stage (5 min)
- Purpose and rules
- Confidentiality, blame-free zone
2. Gather Data (15 min)
- Timeline of events
- Key metrics and outcomes
- Individual observations
3. What Worked Well (15 min)
- Successes to repeat
- Effective practices
- Team strengths
4. What Didn't Work (20 min)
- Challenges faced
- Root cause analysis
- AI-specific issues
5. Action Items (15 min)
- Concrete improvements
- Owners and timelines
- Follow-up plan
```
**Key Questions for AI Projects**
**Technical**:
```
- Did we have the right data? How could we know earlier?
- Was our evaluation realistic? Any production surprises?
- Were our infrastructure assumptions correct?
- What would we measure differently?
```
**Process**:
```
- How accurate were our estimates?
- Did we have the right expertise?
- Where were communication gaps?
- What caused the biggest delays?
```
**Outcome**:
```
- Did we solve the right problem?
- How does user experience match expectations?
- What would we do differently from day one?
- Are there quick wins we're missing?
```
**5 Whys for AI Issues**
**Example: Model performs worse in production**:
```
Why 1: Model accuracy dropped in production
Why 2: Production data distribution differs from training
Why 3: We trained on historical data that's now outdated
Why 4: We didn't have monitoring for data drift
Why 5: Data monitoring wasn't part of our launch checklist
Root cause: No data monitoring process
Action: Add data drift monitoring to launch requirements
```
**Documenting Learnings**
**Post-Mortem Template**:
```markdown
# [Project Name] Retrospective
## Summary
One paragraph overview of project and outcome.
## What Worked
- Item 1: Description + why it worked
- Item 2: ...
## What Didn't Work
- Issue 1: Description + root cause
- Issue 2: ...
## Key Learnings
1. Learning 1
2. Learning 2
## Action Items
| Action | Owner | Due Date | Status |
|--------|-------|----------|--------|
| ... | ... | ... | ... |
## Metrics
| Metric | Expected | Actual |
|--------|----------|--------|
| ... | ... | ... |
```
**Sharing Learnings**
```
Channel | Content
------------------|----------------------------------
Team meeting | Full walkthrough
Wider org | Summary + key learnings
Documentation | Searchable reference
Onboarding | Case studies for new hires
```
**Best Practices**
- **Blameless**: Focus on systems, not individuals.
- **Timely**: Do retros soon after project ends.
- **Inclusive**: Include all team members.
- **Actionable**: Every learning needs an action.
- **Follow Through**: Review past action items.
AI project retrospectives are **how teams compound their learnings** — the field moves fast and projects often fail in novel ways, so systematic reflection transforms individual project lessons into organizational capabilities.