PoC Validation Checklist
Use Case Name: _________________________________
PoC Start Date: _________________________________
PoC End Date: _________________________________
PoC Owner: _________________________________
PoC Success Criteria (Define BEFORE Starting)
Define these criteria BEFORE starting the PoC. They determine if the PoC succeeds or fails.
1. Model Accuracy
Baseline Performance:
- Metric: _________________________________
- Baseline value: _____%
- Target to beat: _____%
ML Model Performance:
- Target accuracy: ≥ _____%
- Or: Beat baseline by ≥ _____ percentage points
Decision:
- ✅ PASS: Accuracy ≥ target
- ❌ FAIL: Accuracy < target
Actual Result: _____%
2. Data Quality
Data Completeness:
- Target: ≥85% completeness
- Actual: _____%
Data Accuracy:
- Target: ≥85% accuracy
- Actual: _____%
Data Freshness:
- Target: Data is current (within _____ months)
- Actual: Data is _____ months old
Decision:
- ✅ PASS: Quality acceptable for production
- ❌ FAIL: Quality insufficient
3. Team Readiness
Technical Skills:
- Team can build this
- Team can maintain this
- Training plan is defined
Operational Readiness:
- Can integrate with existing systems
- Monitoring/observability plan exists
- Support process is defined
Decision:
- ✅ PASS: Team is ready
- ❌ FAIL: Team not ready
4. ROI Math Holds
Projected ROI (from assessment):
- Annual benefit: €_____
- Implementation cost: €_____
- Operating cost: €_____ / year
- Payback period: _____ months
Actual PoC Results:
- Performance achieved: _____%
- Estimated annual benefit (based on PoC): €_____
- Estimated implementation cost (refined): €_____
- Estimated operating cost: €_____ / year
- Revised payback period: _____ months
Decision:
- ✅ PASS: ROI still positive, payback <18 months
- ❌ FAIL: ROI negative or payback >18 months
5. Technical Feasibility
Integration:
- Can integrate with existing systems
- No major technical blockers discovered
- API/access is available
Performance:
- Meets latency requirements (if applicable)
- Meets throughput requirements (if applicable)
- Scalability is feasible
Decision:
- ✅ PASS: Technically feasible
- ❌ FAIL: Technical blockers exist
PoC Structure (4 Weeks)
Week 1: Data Collection and Quality Assessment
Tasks:
- Extract data from source systems
- Validate data completeness
- Assess data accuracy
- Identify data gaps
- Document data quality issues
Deliverables:
- Data inventory spreadsheet
- Data quality report
- Gap analysis document
Blockers Identified:
- _________________________________
- _________________________________
Decision:
- ✅ Continue to Week 2
- ❌ STOP: Data quality insufficient
Week 2: Baseline Model
Tasks:
- Build simple rule-based baseline
- Measure baseline performance
- Set target to beat
- Document baseline approach
Deliverables:
- Baseline model implementation
- Baseline performance metrics
- Target performance definition
Baseline Results:
- Metric: _________________________________
- Performance: _____%
Target to Beat: _____%
Decision:
- ✅ Continue to Week 3
- ❌ STOP: Baseline already meets requirements (no AI needed)
Week 3: ML Model Development
Tasks:
- Feature engineering
- Model training
- Model validation
- Performance comparison vs baseline
- Error analysis
Deliverables:
- ML model implementation
- Validation results
- Performance comparison report
- Error analysis document
ML Model Results:
- Metric: _________________________________
- Performance: _____%
- Improvement over baseline: _____ percentage points
Decision:
- ✅ Continue to Week 4
- 🟡 PIVOT: Model needs refinement (extend PoC 1-2 weeks)
- ❌ STOP: Model doesn't beat baseline
Week 4: Decision Gate
Tasks:
- Review all findings
- Compare against success criteria
- Assess technical feasibility
- Refine ROI calculations
- Make GO/PIVOT/STOP decision
Deliverables:
- PoC summary report
- Success criteria assessment
- Recommendation (GO/PIVOT/STOP)
- Next steps plan
Decision:
- ✅ GO: Proceed to full build
- 🟡 PIVOT: Change approach, extend PoC, or simplify
- ❌ STOP: Not viable right now
PoC Decision Matrix
| Success Criteria | Status | Notes |
|---|---|---|
| Model Accuracy | ✅ / ❌ | |
| Data Quality | ✅ / ❌ | |
| Team Readiness | ✅ / ❌ | |
| ROI Math Holds | ✅ / ❌ | |
| Technical Feasibility | ✅ / ❌ |
Decision Rules:
- All criteria met (5/5)? → ✅ GO to full build
- 1-2 criteria missed (3-4/5)? → 🟡 PIVOT (change approach, simplify, extend PoC)
- 3+ criteria missed (≤2/5)? → ❌ STOP (not viable right now)
Final Decision: _________________________________
PoC Costs
Actual Costs:
- Team time (FTE): _____ hours × €_____ / hour = €_____
- Infrastructure: €_____
- Tools/licenses: €_____
- Data access: €_____
- Total PoC Cost: €_____
Budget: €_____ (target: €50K-100K for 4 weeks)
Variance: €_____ (over/under budget)
Key Learnings
What worked well:
What didn't work:
Surprises:
What we'd do differently:
Next Steps
If GO:
If PIVOT:
If STOP:
- Document learnings
- Revisit in 6-12 months
- Consider alternative approaches
Approved By
- PoC Owner: _________________ Date: _______
- Business Owner: _________________ Date: _______
- Technical Lead: _________________ Date: _______
- AI Architect: _________________ Date: _______