Course Goal
Prepare participants for effective integration of artificial intelligence (AI) into digital product development, providing practical understanding of AI tools and their impact on innovation, design, planning, and validation.
The course is designed for:
Product managers
Designers
Strategists
Digital teams
Entrepreneurs
Who aim to integrate AI into the product development process
Learning format:
Course duration: 18 hours (9 sessions × 2 hours)
Online classes with an instructor
Homework between sessions
What participants will gain:
🔗 View benefits
Program of the course "AI for Product Teams"
1
Introduction to AI in the Product Context
How AI is changing approaches to product and UX/CX strategy
What AI is (and what it is not)
Generative AI vs. predictive models
Basic concepts product specialists need to know
Tool overview: ChatGPT, Claude, Perplexity, Midjourney, custom GPT models
2
Product Vision, Strategy, and Roadmap
Forming a product vision and aligning it with business goals
North Star metrics
Roadmap planning using AI: Now/Next/Later, RICE, ICE
3
Product Discovery with AI
Market and competitor analysis using AI
User persona generation with AI
Identifying unmet needs through data + AI
4
Ideation and Validation
Brainstorming product ideas with AI
Rapid validation techniques: mockups, MVPs created via prompts
Applying AI in JTBD and value proposition design frameworks
5
AI in Product and UX Design
Idea generation using LLM + AI design tools
Creating mockups and MVPs in Uizard, Figma AI, Midjourney
Simulating product functionality based on AI personas
Generating user scenarios, wireframes
Collecting feedback via AI analytics
6
Data and Decision-Making
Product analytics + AI = new insights
Building predictive models for feature prioritization
A/B testing and optimization with AI support
7
Stakeholder Management and Communication
Collaboration with development, marketing, operations teams
Product presentations and influencing skills
AI-assisted communication:
Generating meeting summaries, briefs, updates
Product visualization and storytelling with AI
8
Ethics, Risks, and Governance in AI Implementation
Ensuring a responsible approach to AI in products
Examples of AI failures and why they happened
Risks: bias, hallucinations, lack of transparency
What product teams should know about AI literacy
9
Building AI-Native Products
What is an AI-native product and how is it different
Using LLMs as part of product functionality
Prompt engineering basics for product teams