AI Projects

AI Projects focus on delivering AI/ML capabilities from data through models to production value.

What makes these projects distinctive

AI Projects carry their own signature challenges. The ones that most often decide success or failure are poor or insufficient data, models that don’t generalise, unclear roi and adoption — so the plan should be built around managing exactly these.

Typical phases

1. Initiation — business case and charter
2. Planning — scope, schedule, budget, risks
3. Execution — building the deliverables
4. Monitoring & control — tracking against baseline
5. Closure — acceptance, handover, lessons learned

Sample WBS outline

• Initiation & business case
• Planning (scope, schedule, budget)
• Use-case & data assessment
• Data pipeline
• Model development
• Evaluation & validation
• Deployment & monitoring
• Testing / quality assurance
• Deployment / handover
• Closure & lessons learned

Key deliverables

• Use-case & data assessment
• Data pipeline
• Model development
• Evaluation & validation
• Deployment & monitoring

Top risks to plan for

• Poor or insufficient data
• Models that don’t generalise
• Unclear ROI and adoption

Key roles

Typical duration

3–12 months, depending on scale and complexity.