Quick Start: AI Project Management Checklist
⚡ Quick Reference: Use this checklist for your next AI project. Each phase includes critical checkpoints that prevent common failures. For detailed explanations, see our comprehensive AI project management guide.
Managing an AI project successfully requires different checkpoints than traditional software. Use this actionable checklist to guide your team through each phase.
Phase 1: Problem Definition
✅ Define Success Metrics
- Quantifiable accuracy targets (e.g., "detect fraud with 90% precision")
- Acceptable error rates (what false positives/negatives can business tolerate?)
- Business impact metrics (ROI, cost savings, time saved)
✅ Validate Business Value
- Is this problem worth solving with AI? (simpler solutions exist?)
- Do stakeholders understand AI limitations?
- Is failure acceptable? (some problems aren't learnable)
Phase 2: Feasibility Assessment
✅ Data Availability Check
- Do we have sufficient data? (typically need 1000s of examples)
- Is data labeled? (if not, budget for labeling)
- Is data representative of production scenarios?
- Can we legally use this data for training?
✅ POC Planning
- 2-4 week time-boxed proof of concept
- Clear go/no-go criteria
- Baseline model performance target
Phase 3: Data Acquisition (40-60% of project time)
✅ Data Collection
- Identify all data sources
- Assess data quality (completeness, accuracy, consistency)
- Budget allocated for data labeling services if needed
- Data privacy and security compliance verified
Phase 4: Exploratory Analysis
✅ Data Understanding
- Data distributions explored and documented
- Edge cases and outliers identified
- Biases in data detected and documented
- Initial assumptions validated or revised
Phase 5: Baseline Model
✅ Establish Baseline
- Simplest possible model built (never skip this!)
- Baseline performance measured and documented
- Confirmed problem is learnable (baseline > random)
- Set improvement targets vs. baseline
Phase 6: Experimentation
✅ Experiment Planning
- Multiple approaches planned to run in parallel
- Clear experiment documentation process
- Accept that 50-70% of experiments may fail
- Track learnings from failures, not just successes
Phase 7: Model Evaluation
✅ Validation Checklist
- Validated on holdout data (never seen during training)
- Edge cases tested thoroughly
- Bias and fairness metrics evaluated
- Domain experts reviewed results
- Error analysis conducted (why did model fail?)
Phase 8: Production Deployment
✅ Deployment Readiness
- Monitoring infrastructure in place
- A/B testing capability (if applicable)
- Rollback plan documented and tested
- Gradual rollout strategy (start with 5-10% of traffic)
- Alert thresholds configured
Phase 9: Monitoring
✅ Ongoing Monitoring
- Accuracy metrics tracked in real-time
- Data drift detection active
- Model performance dashboard created
- On-call rotation for model issues
- Weekly/monthly performance reviews scheduled
Phase 10: Model Refresh
✅ Maintenance Planning
- Retraining schedule established (monthly/quarterly)
- Budget allocated for ongoing MLOps
- Process for model version control
- Criteria for complete model rebuild vs. retrain
Critical Success Factors
🎯 Don't Skip These
- POC First: Never commit to full project without 2-4 week POC
- Data Quality: Allocate 40-60% of time to data work
- Baseline Model: Always establish baseline before optimization
- Expect Failures: Budget 30-40% of time for failed experiments
- Plan for Production: MLOps infrastructure from day one
- Continuous Learning: Invest in AI/ML education for PM team
Need Help with Your AI Project?
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