Master Study AI

Problem Definition & Business Objectives in AI Projects

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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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