When adding AI to our products, designers often envision AI systems that can’t be built. Most ideas are not buildable as they require near-perfect AI performance to be useful in real-world scenarios. Data scientists and engineers envision AI systems that have little user’s interest. So, most AI products fail pre-deployment due to solving the wrong problem, and do so with high costs and expectationas.In this session, we’ll explore new, practical design patterns for better AI experiences, with daemons, clustering, style lenses, structured presets and templates, dynamic editing, temperature knobs and everything in-between. We’ll study real-life examples of AI usage in successful products — and how we as designers can improve input UX, output UX and refinements UX for AI products.You’ll learn:- How to avoid product-market-fit gap for AI products,

– How to help users specify their intent more precisely,

– How to turn AI’s static output in a dynamic and responsive interface,

– How to help users scope generated output based on their interests and preferences,

– Real-life case studies of how companies embed AI into their products successfully- How to adapt products for Gen-Z, from content to design,

- What to keep in mind when designing for older adults,

- How to help people minimize errors in stressful situations,

- How to design for people who are neurodivergent.