AI projects often start with excitement and end with disappointment. The reason is usually not the algorithm. It’s the lack of a disciplined delivery process. Use this 7-step checklist to build AI that actually moves business outcomes.
1) Define the business decision
Instead of “build a model,” start with:
- What decision will improve?
- Who makes it?
- How often?
- What does success mean in business terms?
2) Choose the right AI approach
Not every problem needs deep learning. Many wins come from:
- Regression / classification
- Gradient boosting
- Time-series forecasting
- Rules + ML hybrid
3) Audit data readiness
Check for:
- Missingness, bias, labeling quality
- Data leakage risk
- Freshness (latency) and ownership
4) Build a baseline first
A baseline model sets expectations and prevents over-engineering. Sometimes a simple model beats a complex one in production stability.
5) Validate properly
Use the right metrics for the business:
- Precision/recall for fraud, medical, compliance
- MAE/MAPE for forecasting
- Uplift or incremental revenue for marketing
6) Deploy with monitoring
In production, models drift. Monitor:
- Data distribution changes
- Model performance
- Outliers and edge cases
7) Measure ROI
Tie results to business outcomes:
- Cost reduced
- Revenue uplift
- Time saved
- Risk minimized
- Customer satisfaction improved
Bottom line: AI ROI is a process outcome. The model is just one step in a bigger system.