Problem Definition & Business Objectives in AI Projects
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Table of Contents
- 1. Course Modules:
- 2. Module 1: Introduction to AI Problem Definition
- 3. Module 2: Identifying Business Needs
- 4.
- 5. Module 3: Translating Business Needs into AI Use Cases
- 6. Module 4: Defining Success Metrics
- 7. Module 5: Feasibility Assessment
- 8.
- 9. Module 6: Final Project – Build an AI Problem Brief
- 10. Tools & Techniques Used:
- 11. Target Audience:
- 12. Global Learning Benefits:
Course Modules:
Module 1: Introduction to AI Problem Definition
Why proper scoping is the foundation of success
Business problems vs. AI problems
Aligning AI with strategy and operations
Module 2: Identifying Business Needs
Interviewing stakeholders and gathering requirements
Root cause analysis and pain point discovery
Documenting business goals and constraints
Module 3: Translating Business Needs into AI Use Cases
What makes a problem suitable for AI or ML?
Classifying use cases: prediction, classification, NLP, vision, etc.
Case studies: high-value AI opportunities in various industries
Module 4: Defining Success Metrics
Business KPIs vs. model performance metrics
Measuring ROI, accuracy, efficiency, and user impact
Creating metric-driven project charters
Module 5: Feasibility Assessment
Data availability and quality
Technical feasibility vs. organizational readiness
Evaluating risk, bias, and regulatory concerns
Module 6: Final Project – Build an AI Problem Brief
Select a domain (e.g., retail, health, education, finance)
Define a real AI use case with business value
Submit a full problem statement with goals, metrics, and feasibility plan
Tools & Techniques Used:
Business Model Canvas (AI-adapted)
AI Use Case Template (provided)
Interview and survey design
Google Docs, Notion, or PDF for final brief
Target Audience:
Product managers and business leaders exploring AI
Data scientists needing clearer project scoping
Consultants proposing AI solutions to clients
AI students preparing for real-world work
Global Learning Benefits:
Launch AI projects with a clear, business-aligned direction
Prevent costly misalignment between teams and outcomes
Strengthen collaboration between tech and non-tech stakeholders
Build a reusable framework for AI problem scoping
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