Workshops

AI is rapidly changing how teams conduct UX research–but most practitioners still lack a reliable way to determine where AI improves business outcomes and where it introduces too much risk.

In this workshop, you'll use transparent, custom-built AI research tools to observe exactly how AI impacts research data and business decisions. Through hands-on experimentation, grounded in academic research and controlled testing, you'll gain an accurate understanding of AI's capabilities and failure modes for common research approaches.

You'll leave with a framework for evaluating AI research tools and evidence-based best practices for making informed trade-offs between AI and traditional methods.

Somewhere between 50 and 80% of AI projects fail. That's at least double the failure rate of traditional IT projects. Companies have poured tens of billions into AI, and the returns so far are dismal: one recent MIT report found 95% of organizations getting zero return on their investment. The problem usually isn't the technology. It's that teams keep picking the wrong projects: technically ambitious, high-risk endeavors that demand near-perfect accuracy to be useful, when what most organizations actually need are the many modest, valuable, low-risk AI projects hiding in plain sight. The traditional user-centered design process alone won't find them.

This workshop teaches a set of techniques that designers, product managers, and researchers can start using immediately. It adapts what we've been teaching our graduate students at Carnegie Mellon's Human-Computer Interaction Institute for working practitioners: short lectures to introduce each method, then hands-on exercises to practice it. You'll work in teams on real concepts, not hypotheticals.

AI is full of promise for users, but it introduces risk as well: over-reliance and deskilling. The good news is that you as a designer can do something about each of these.

Come hear Christopher Noessel introduce the problems, share examples, and walk us through the patterns we can implement to help take some of the sting out of AI.