INTRO TO AI PRODUCT DESIGN

Today marks the beginning of your AI career. You’ll get introduced to your capstone project which consists of a product brief, a presentation slide and a prototype.

• Instructor introduction
• Your Myers Brigg personality & thinking style
• AI career development brief: Gain AI knowledge & use AI for better innovation
• Course objectives, expectations & flow
• Capstone project 
• Q&A

Through the industrial, cloud, and AI eras, designers have always played a central role. Learn to identify and evaluate factors affecting the current and future AI job market.

• State of AI: development & technology adoption life cycle overview
• Design’s role & job opportunities 
• Human-centred AI design & the AI product life cycle 
• Case Study: Gartner’s AI business use case prism

Learn key concepts of AI, ML, Deep Learning, Gen AI, algorithms, and their relationship with each other. As you build up your knowledge of AI-related terminology, you'll analyse their user implications and benefits

• Technology literacy needed for AI product design
• AI tools that increase technology literacy and UX implications
• Algorithms: Supervised vs. unsupervised and working principles of clustering 
• Case Study: Real-world linear regression prediction

Assignment #1: Choose an AI business use case area for your AI product design career. Identify 3-5 real-world products in that area, both good and bad, as role models. Analyse the use case and examples, listing user implications and benefits of applying AI. 

In this class, you’ll explore neural network architecture, product design applications, and the potential of Generative AI. We’ll also get into Recurrent Neural Networks, Natural Language Processing & Large Language Models. 

• Deep Learning applications
• Overview of Generative AI
• RNN, NLP & LLM explorations
• UX tech radar: RAG, agents, emotional inference
• Demo: Generative AI in action
• Case study: Agents and emotional inference early prototypes

Exceptional user experiences start with exceptional strategizing. This includes brainstorming facets such as explainability, transparency, inclusivity and diversity. Let’s design with the users in mind. 

• Human-centred AI, UX/UI, and interaction & data-driven design
• Case study: Visualising AI-driven recommendations
• UX tech radar: AI usage approaches: ChatGPT, SORA, Devin, Rabbit (LAM) & Hume
• Case study: Real-world augmenting examples
• Workshop: Identify problems or challenges that AI can solve

Assignment #2: Revisit your chosen use case and solidify a problem space that AI can better solve. Identify two human interaction touchpoints where AI would be most beneficial. 

Customer trust secured. Foster the creation of AI systems that prioritise ethicality, fairness, and inclusiveness, reflecting societal principles and addressing user needs.

• Biases & risks in AI algorithms & data
• Fairness, inclusivity & alignment with societal values in AI design
• Adherence to regulations & standards for data protection & privacy
• Case study: EU AI act 
• Workshop: Identify & plan to mitigate potential risks

Assignment #3: Review your project, identify potential issues & write mitigation strategies to reduce risks early. 

Think ahead. Learn to choose appropriate Key Performance Indicators (KPIs) and leverage user feedback to refine your product. This will set your product up for success. 

• Backcasting methodologies
• Product objectives: Setting clear & measurable AI integration goals
• KPIs: Identifying metrics that evaluate AI product success
• The one must-have KPI for every AI product 
• Iterative design: Incorporating user feedback for continuous improvement

Assignment #4: Product Invention: Develop an AI product to address the market space and problems identified in assignments #1-3. Use backcasting to outline the product's vision, expected outcomes, risks, and implications.

Build what matters. Analyse your Minimum Viable Product (MVP). Check out which tools and platforms can aid you in prototyping products that align with your goals.

• The MVP’s role in AI product development
• AI model training cost: building trust & performance scaling 
• Rapid & interactive prototyping methods for AI solutions
• User testing & iteration for problem resolution
• Case study: Real-world MVP in AI product development

Assignment #5: Create an executive summary for your MVP, explaining its value to the business and user. Start building your AI product prototype.

Zoom in on your users. Map out their journeys by creating personas and scenarios. Adopt user alignment strategies to outline user goals, AI features and modalities.

• Information architecture: input, output & hierarchies
• Multi-modality in the AI era: Organizing and Structuring Data for AI Applications
• User Alignment Strategies to map user goals, AI features, and modalities
• Essential tasks in AI product information architecture
• Demo: Use AI tools to research & identify potential data sources
• Case study: B2B & B2C brands assess their entire offering & information structure

Assignment #6: Define 1-2 flows to prototype. Detail data hierarchy and wireframe interactions. Use AI tools to map data sources and create a flow diagram.

Spark user interest with creative design. Understand UX design’s job in enhancing engagement and product adoption – making user experiences intuitive. 

• User flows for AI-powered interactions
• User control design at each interaction point
• Intuitive & seamless multimodal experiences in AI products
• Case study: Multimodality in action

Assignment #7: Enhance your concept using text, imagery, audio, and video. Prototyping: Design details with various modalities.

Create a structured plan for testing ideas and reducing risks. Design prototypes strategically to confirm AI-driven product concepts. Ensure alignment with strategic goals in an incoming workshop. 

• Common research methods in AI initiatives
• Lean experimentation
• Leveraging research data
• Testing, refinement & feature
• Case study: Lean experiment & prototype
• Workshop: Lean experiments: hypothesis, validation methods & early derisking

Assignment #8: Create a lean experiment plan with a hypothesis, validation methods & risk mitigation. Fine-tune the prototype for the experiment.

Monitor the AI landscape. Keep a pulse on emerging micro trends and customer expectations as you navigate the AI product lifecycle you’ll learn how to address unknowns, risks and emerging directions. 

• Implications of exponential market speed
• Nuances in the product development cycle for AI products
• Phases of the AI product development lifecycle
• Planning, development & deployment of AI solutions
• Workshop: Inventive risk mitigation strategies for AI product development

Perspectives steer your product’s future. Amp up iteration with constructive suggestions and address stakeholder concerns with presentations.  

• Purpose & importance of design reviews
• Structuring productive design reviews
• Effective storytelling for AI product design
• Constructive feedback response & iteration
• Design documentation & presentations

Collaborate as a single mind. Through compelling storytelling and agile workflows you’ll unite design and technical teams. 

• Simulated peek into high-profile & uncertain AI initiatives 
• Engaging developers, data scientists & business stakeholders
• Communication practices to bridge design & technical teams
• Communication tools to foster collaboration between AI teams
• Contributing back to the community

It’s time to elevate your career. Discover what to include in your portfolio to make it as compelling as possible. Build a Personal Monopoly with the insights from the experts to stay in high demand. 

• Secret recipes for keeping up with AI nuances & breakthroughs
• Skills & qualifications for different roles in the AI product design field
• Fresh industry insights for your AI career journey
• Professional AI portfolio

Final Project: AI solution integrated into a product design students can work on throughout the assignments.