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SOURCE: Nudelman G. UX for AI. A Framework for Designing AI-Driven Products 2025
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COVER
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MEDIAINFO
Textbook in PDF format Learn to research, plan, design, and test the UX of AI-powered products. Unlock the future of design with UX for AI—your indispensable guide to not only surviving but thriving in a world powered by artificial intelligence. Whether you're a seasoned UX designer or a budding design student, this book offers a lifeline for navigating the new normal, ensuring you stay relevant, valuable, and indispensable to your organization. In UX for AI: A Framework for Designing AI-Driven Products, Greg Nudelman—a seasoned UX designer and AI strategist—delivers a battle-tested framework that helps you keep your edge, thrive in your design job, and seize the opportunities AI brings to the table. Drawing on insights from 35 real-world AI projects and acknowledging the hard truth that 85% of AI initiatives fail, this book equips you with the practical skills you need to reverse those odds. You'll gain powerful tools to research, plan, design, and test user experiences that seamlessly integrate human-AI interactions. From practical design techniques to proven user research methods, this is the essential guide for anyone determined to create AI products that not only succeed but set new standards of value and impact. Users increasingly expect modern software applications to deliver intelligent behaviors above and beyond basic functionality. The subjective difference between the delight of using an app that magically recommends what you might want to watch, eat, or purchase versus one where you have to laboriously specify your desires can be huge. If you aren’t willing to invest in making that happen, your competitors surely are. In practice, this magic is delivered via Machine Learning (ML) or Artificial Intelligence (AI) techniques. The purely technical aspects of these systems form a fascinating and fast-moving field in their own right. Still, here, we will focus on the product design and User Experience (UX) implications of these technologies. How can we integrate AI/ML capabilities into a product experience in such a way that the risks and challenges are minimized while our users reap the maximum benefits? Some advantages of AI/ML are its ability to provide answers to problems for which it is difficult or impossible to hand-craft “classic” software solutions and to deal with intrinsically noisy or probabilistic domains. Consider the problem of handwritten digit recognition; in the absence of AI/ML, one would likely write a bunch of strange overlapping rules, essentially reinventing the AI/ML approach poorly. One major downside of AI/ML approaches, especially from an experience perspective, is the lack of determinism or calibration in its outputs. By determinism, we mean that, for any given input, the quality of the resulting output cannot be known in advance. For a given user interaction, the result may be good or bad, but we generally do not know for sure which it will be until we generate the result, and we cannot drive the probability of bad results down to zero. By calibration, we mean that, by default, the system itself is often unable to detect or qualify the confidence in the result. That is, it does not “know” whether or not its output is good and may be “confidently wrong.” Therefore , the fun part for system designers is to consider how we might try to build reliable and consistent user experiences atop unreliable and inconsistent foundations. INTRODUCTION HOW TO USE THIS BOOK PART 1: Framing the Problem 1: Case Study: How to Completely F*ck Up Your AI Project 2: The Importance of Picking the Right Use Case 3: Storyboarding for AI Projects 4: Digital Twin—Digital Representation of the Physical Components of Your System 5: Value Matrix—AI Accuracy Is Bullshit. Here’s What UX Must Do About It PART 2: AI Design Patterns 6: Case Study: What Made Sumo Copilot Successful? 7: UX Best Practices for SaaS Copilot Design 8: Reporting—One of the Most Important Copilot Use Cases 9: LLM Design Patterns 10: Search UX Revolution: LLM AI in Search UIs 11: AI-Search Part 2: “Eye Meat” and DOI Sort Algorithms 12: Modern Information Architecture for AI-First Applications 13: Forecasting with Line Graphs 14: Designing for Anomaly Detection 15: UX for Agentic AI PART 3: Research for AI Projects 16: Case Study: MUSE/Disciplined Brainstorming 17: The New Normal: AI-Inclusive User-Centered Design Process 18: AI and UX Research 19: RITE, the Cornerstone of Your AI Research PART 4: Bias and Ethics 20: Case Study: Asking Tough Questions Through Vision Prototyping 21: All AI Is Biased 22: AI Ethics 23: UX Is Dead. Long Live UX for AI!
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