The Interface Is Not the System

Your AI model has 95% accuracy. Users still don't trust it. Your interface is clean and intuitive. The system still fails at scale. Your team shipped the feature on time. It's quietly abandoned within weeks. The problem isn't the technology. It's the gap between what users see and what the system actually does, a gap that grows wider as AI systems scale, where control becomes illusion, trust breaks down, and responsibility dissolves across teams who never agreed on what success looks like.
This book is for designers, product leaders, and AI practitioners who've learned the hard way that great models don't guarantee great products. It reveals why most AI features fail at the design level, before users ever encounter them, and provides the frameworks to build AI systems that people can actually use, understand, and trust.

Inside, you'll discover:

• Why accuracy is the wrong success metric and what to measure instead
• How to design for uncertainty without hiding it or overwhelming users
• The patterns that work (copilots, search, agents) and why others consistently fail
• How bias manifests in interfaces, not just data
• When to say no to AI features that will cause foreseeable harm
• Research methods that work for systems that change and learn.