Quick Start: AI Project Management Checklist

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 the business tolerate?
• Business impact metrics (ROI, cost savings, time saved)

Validate Business Value

• Is this problem worth solving with AI? (do 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 thousands of labelled examples minimum)
• Is data labelled? (if not, budget for labelling services)
• Is data representative of production scenarios?
• Can we legally use this data for training?

POC Planning

• 2–4 week time-boxed proof of concept defined
• Clear go/no-go criteria established
• Baseline model performance target set

Phase 3: Data Acquisition (40–60% of project time)

Data Collection

• All data sources identified
• Data quality assessed (completeness, accuracy, consistency)
• Budget allocated for data labelling services if needed
• Data privacy and security compliance verified (GDPR/UK GDPR)

Data Quality Gates

• Minimum volume thresholds met
• Class balance acceptable (or imbalance strategy defined)
• No personally identifiable information in training sets (unless lawfully processed)

Phase 4: Exploratory Analysis

Data Understanding

• Data distributions explored and documented
• Missing value patterns identified and handled
• Outliers identified and strategy defined
• Feature correlations with target variable assessed
• Potential bias sources identified

Phase 5: Baseline Model

Initial Benchmark

• Simplest possible model built first (don't skip this)
• Baseline performance documented
• Go/no-go decision made against POC criteria
• Stakeholders aligned on what baseline performance means

Phase 6: Experimentation

Experiment Management

• Experiment tracking in place (MLflow, Weights & Biases, or equivalent)
• All experiments logged with hyperparameters and results
• Failed experiments documented with learnings
• Model versions tracked

Phase 7: Model Evaluation

Performance Validation

• Model tested on held-out test set (never used in training or tuning)
• Performance measured across all relevant demographic subgroups
• Bias and fairness metrics evaluated
• Edge cases and failure modes documented
• Domain experts involved in validation

Phase 8: Production Deployment

Deployment Readiness

• Model serving infrastructure built and tested
• Feature engineering pipeline identical to training pipeline
• Monitoring dashboards operational before go-live
• Rollback plan defined
• Gradual rollout or shadow deployment planned

Phase 9: Ongoing Monitoring

Production Health

• Model accuracy monitored continuously
• Data drift detection in place
• Retraining pipeline built and tested
• Retraining cadence defined (monthly, quarterly, trigger-based)
• Alerts configured for performance degradation