- UX for AI
- Topics
- Executive Leadership
Executive Leadership
AI Snowflake: Stop Asking the Wrong AI Question
One of the most common ways projects go sideways is by asking a question that AI cannot answer—or worse, one that doesn’t actually deliver ROI.(AI Snowflakes: short, focused UX for AI insights. Quick provocations designed to spark thinking and sharpen strategy, in under a minute.)
Snowball Killed the Dev-Star: Stop Handing Off, Start Succeeding in the AI-First World
AI-first UX isn’t about pixel-perfect mockups — it’s about whether your product sinks or sails. Traditional 3-in-a-box Figma wireframes are the Titanic: they doom teams to Telephone Pictionary the moment AI behavior springs a leak. This guide shows UX designers how to roll the Snowball instead — a customer-centered, vibe-coded process that delivers working code prototypes from day one, shipping products customers actually want to buy.
Today’s “DeepSeek Selloff” and What it Means for UX
Today, the stock market dumped shares of many AI companies on the announcement that DeepSeek V3 is disrupting the AI ecosystem. In this article, we unpack the tealeaves and suss out what this new development means to UX (and it’s plenty).
UX for AI Guide to Inventions and Patents (Part 1)
Few things hurt AI-driven design projects as much as a lack of imagination. We break down some signature techniques used by notable scientists and inventors and explain how to use those specific techniques to help you achieve a breakthrough with your own AI project.
Embracing AI in Design Ops: Opportunities, Trends, and the Future Workforce
DesignOps stands at the threshold of an AI-driven transformation. Recently, I had the honor and privilege to be on the panel of DesignOps leaders at the #DesignOpsLA to discuss the evolving landscape of AI in design, operational efficiency, and how we can prepare our teams for the future. Here are the key takeaways from that dynamic conversation.
The Learning Enterprise: GA is but a Starting Gun
When it comes to releasing new features and products, many companies today practice “release and forget.” This practice is about to change in a most radical way because, for AI-driven products, GA is only the first step in the lifecycle of a learning product.
