<\/p>\n
Offered three times in 2026; select your dates when registering:<\/p>\n
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.<\/p>\n 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.<\/p>\n Part One is about building the right thing.<\/strong> When you can build anything, how do you know you\u2019re building something valuable? We’ll start with AI Capabilities<\/strong>: learning to see AI not as a monolith but as a wide range of dead-simple abilities (sort items into categories, detect objects in images, predict an event) that can be matched to real human contexts. Then Matchmaking<\/strong>, a brainstorming technique for systematically pairing those capabilities with organizational needs to generate concepts. You’ll rapidly evaluate those concepts for technical feasibility and value, run them through Consequence Scanning<\/strong> to surface unintended harms before you build them into the world, and finish by ranking your concepts to find the ones actually worth pursuing.<\/p>\n Part Two is about building the thing right.<\/strong> Designing for AI means designing for ambiguity, in both what users ask for and what the system does in response. We’ll practice Intent Mapping<\/strong>: connecting what users actually want to what the AI does and what users do next, and deciding when a concept needs a conversational UI, a traditional GUI, or no UI at all. We’ll explore Adaptive UIs<\/strong>, which find the frequent, repetitive moments in existing products where AI can quietly save users time or personalize the product. Then Human-AI Collaboration<\/strong>: designing systems where people direct, monitor, intervene, and approve rather than blindly accept whatever the machine produces.<\/p>\n We’ll end with Explainability<\/strong>: how much users need to know about how the AI works to trust it appropriately, and when an explanation does more harm than good.<\/p>\n You’ll leave with methods you can run with your own team the next day and a sharper sense of which AI projects deserve your time and tokens. Most don’t. The good ones are worth finding.<\/p>\n","protected":false},"template":"","class_list":["post-2251","workshops","type-workshops","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/rosenfeldmedia.com\/rosenfeld-workshops\/wp-json\/wp\/v2\/workshops\/2251","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rosenfeldmedia.com\/rosenfeld-workshops\/wp-json\/wp\/v2\/workshops"}],"about":[{"href":"https:\/\/rosenfeldmedia.com\/rosenfeld-workshops\/wp-json\/wp\/v2\/types\/workshops"}],"wp:attachment":[{"href":"https:\/\/rosenfeldmedia.com\/rosenfeld-workshops\/wp-json\/wp\/v2\/media?parent=2251"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Option 1:<\/strong> May 4 and 6, 2026, 8:00am-12:00pm PT<\/del>
\nOption 2:<\/strong> June 30-July 1, 2026, 8:00am-12:00pm PT<\/del>
\nOption 3:<\/strong> October 30 and Nov 6, 8:00am-12:00pm PT<\/p>\n