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.