🚀 Building AI-Ready M&E Teams: Practice Tool
Apply what you have learned through interactive scenarios and exercises
💡 How This Works
Work through real-world scenarios to practice building AI-ready M&E teams. Complete each exercise, reflect on the key concepts, and receive personalized feedback to strengthen your understanding.
Your Current Team:
• 5 M&E Officers with traditional evaluation backgrounds
• 2 Data Analysts familiar with Excel and basic statistics
• 1 Senior M&E Advisor with 15 years of experience
• Limited budget for external hiring
Task: Select the competencies your team should develop first to become AI-ready:
Task: For each traditional M&E role below, describe how you would expand it to include AI-related responsibilities while preserving core M&E expertise:
M&E Officer
Traditional focus: Data collection, indicator tracking
Senior Evaluator
Traditional focus: Evaluation design, stakeholder engagement
Data Analyst
Traditional focus: Statistical analysis, visualization
Challenge:
A senior team member says: "We have been doing excellent M&E work for years without AI. Why fix what is not broken? I am worried AI will make our work less human-centered and more about numbers."
Task: Write your response to address their concerns while promoting an AI-ready culture:
Task: Review the ideal team structure and responsibilities for this AI-enhanced M&E project:
Key Team Roles:
- M&E Lead: Ensures AI outputs align with M&E standards and serve program learning needs
- Data Scientist: Sets up AI systems, monitors performance, and troubleshoots technical issues
- Ethics Advisor: Reviews AI systems for bias, ensures responsible use, and protects vulnerable populations
- Community Liaison: Ensures community voices shape AI design and builds trust in AI-enhanced processes
- IT Specialist: Manages data infrastructure, ensures security protocols, and maintains system reliability
Task: Outline a phased training plan that progressively builds your team's AI capabilities: