AI Product Design & Innovation
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Course Modules:
Module 1: Introduction to AI Product Thinking
What makes a product “AI-powered”
Product vs. platform vs. feature in AI
Role of product managers, designers, and engineers
Module 2: Design Thinking for AI Solutions
Problem framing and empathy mapping
Ideation techniques for AI use cases
Balancing human needs with technical capability
Module 3: Market Research & Opportunity Discovery
Identifying AI product gaps and inefficiencies
Analyzing trends and competitor offerings
Use-case validation with real-world datasets
Module 4: Defining AI Product Requirements
Translating user needs into AI system goals
Setting success metrics and model performance benchmarks
Aligning user experience with backend intelligence
Module 5: Prototyping AI Features
Designing intelligent interfaces and user flows
Low-fidelity and high-fidelity AI product mockups
Wireframes and UX for dynamic, evolving systems
Module 6: MVP Development for AI Products
Scope definition for AI-powered MVPs
Collaborating with data scientists and engineers
Tools for building fast prototypes (Streamlit, Bubble, Figma + APIs)
Module 7: Data Strategy & Model Integration
Choosing the right data and models for your product
Working with APIs (e.g., GPT, Vision AI, speech models)
Iterating with feedback and fine-tuning output
Module 8: Responsible AI and User Trust
Designing for explainability and transparency
Bias prevention and ethical frameworks
Privacy, data security, and user trust signals
Module 9: Go-To-Market Strategy for AI Products
Positioning your AI product for growth
GTM channels, partnerships, and storytelling
Pricing, monetization, and feedback loops
Module 10: Final Capstone Project
Deliverables include:
Product vision and persona docs
MVP prototype or walkthrough
Go-to-market and pitch deck
Peer feedback and instructor evaluation
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