AI products fail when they try to replace human judgment. They succeed when they collaborate with it. This talk presents practical patterns for building AI features that support people instead of surprising them, focusing on trust, reversibility, and real-world use.

Many AI features are built with the assumption that automation is always the goal. In practice, users lose trust when systems act without context, fail silently, or make decisions they cannot inspect or undo. The more powerful the model becomes, the more dangerous this assumption can be.

This talk explores a different approach: designing products where humans and AI work together. Drawing from real product decisions, I’ll introduce interaction patterns such as co-pilot, critic, and preview-based workflows, and explain when each one is appropriate.

We’ll examine what users tolerate when AI fails and what they never forgive, how to design for transparency and reversibility, and how to build guardrails that allow safe experimentation. We’ll also discuss how to measure success beyond first-time usage and how to gradually increase autonomy without breaking trust. Attendees will leave with concrete principles for building AI features that scale responsibly and improve human decision-making instead of replacing it.