Log in or create a free Rosenverse account to watch this video.
Log in Create free account100s of community videos are available to free members. Conference talks are generally available to Gold members.
This video is featured in the Josh's test playlist playlist.
Summary
Rapid advances in Artificial Intelligence and machine learning are transforming the world in many ways. For the product designer or design strategy practitioner this megatrend manifests itself in 2 orthogonal dimensions: AI as a product design material – AI enables solutions that are smarter, faster and can answer questions well beyond human capability alone, but you must deploy them effectively and responsibly to be successful. AI designing the product for you – AI generation of competent oil paintings and music based solely on a set of input requirements has been repeatedly demonstrated in the past decade. Emerging AIs can design entire digital user experiences, code them, and deploy to the cloud with one button click. While AI automation can provide huge benefits in both megatrend dimensions it carries spectacular risk when deployed within life and death systems such as autonomous vehicles and medical products. Concurrently, generative AI for product design carries significant liability risk plus the potential of employment disruption for creative and strategic job careers.
Key Insights
-
•
AI in UX splits into using AI as a design material versus AI augmenting or replacing designers in creative processes.
-
•
Soft AI, which uses structured data and domain rules, is more explainable and suitable for critical applications like genomics than hard AI.
-
•
Trust and perceived credibility in AI-driven medical systems depend heavily on both explainability and interface design quality.
-
•
The genomics AI case analyzes massive, changing DNA variant data impossible for humans alone to process in real time.
-
•
Ben Schneiderman’s classification of AI as super tools or teammates helps frame AI’s role in augmenting human work.
-
•
Clean Software’s AI builds entire UX workflows and code through semantic interaction models, speeding up app development for enterprises.
-
•
Generative AI UX designs face risks like sameness and depend heavily on accurate, high-quality input data to avoid creating useless outputs.
-
•
AI can accelerate UX exploration by generating multiple alternatives quickly, supporting iterative design and decision-making.
-
•
Accessibility and localization best practices can be baked into AI-generated UX code automatically.
-
•
Ethical and regulatory oversight become crucial when AI influences high-risk decisions like clinical diagnoses.
Notable Quotes
"If you don’t trust it, then there’s nothing here."
"The AI is looking through material and that material’s changing every day."
"Visual design quality actually affects perceived trustworthiness."
"You can’t evaluate bias if the AI can’t explain itself."
"Garbage in, garbage out—if the requirements are wrong, the AI will instantly create a useless UX."
"You don’t want to game anybody here. This is persuasion by evidence, not by trickery."
"The marketplace is going to decide if it’s close enough in cost-benefit tradeoff."
"AI-generated UX is not about replacing designers, but removing grunt work to focus on higher-order design."
"Human beings understand graphical user interfaces as composed of objects and actions—this grammar is key to AI design."
"The future was already here. It’s just not evenly distributed."
Or choose a question:
More Videos
"I made sure the LLM would only use terms from my predefined taxonomy, but it still introduced some of its own tags."
Jorge ArangoScale Smart: AI-Powered Content Organization Strategies
September 24, 2024
"We built an equity learning community with 25 virtual sessions, 4 interagency roundtables, and one-on-one consultations with 37 agencies."
Aaron Stienstra Lashanda HodgeLeveraging Civic Design to Advance Equity and Rebuild Trust in the US Federal Government
December 8, 2021
"There is no unbiased creation of machine learning or artificial intelligence; the idea of agnostic algorithms is a myth."
Ovetta SampsonManaging the Human Engagement Risks of AI
June 10, 2025
"The front door of our discipline is narrow, and while it serves a purpose, I don’t think it’s serving us well."
Mackenzie GuinonM.C. Escher’s UX Research Career Ladder
March 9, 2022
"Don't fight invisible enemies. Find the people who support and appreciate you."
Shahrzad SamadzadehWhat Is My Value? Two Takes and Some Mistakes
January 8, 2024
"We use this data not to dehumanize, but to elevate each person’s uniqueness and fairly incentivize performance."
John Paul de Guzman10k Screens Later: How We Became a Data-Driven Design Organization
September 24, 2024
"Innovation is not a spice that you sprinkle on and suddenly magical new things come out."
Jeff GothelfInnovation Studios: the Engines of Enterprise Experimentation
May 14, 2015
"Human biases are the real problem behind algorithmic bias because organizations embed those biases first."
Lisa WelchmanCleaning Up Our Mess: Digital Governance for Designers
June 14, 2018
"Both iOS and Android have built-in screen magnification and voice control that don’t require extra software."
Sam ProulxMobile Accessibility: Why Moving Accessibility Beyond the Desktop is Critical in a Mobile-first World
November 17, 2022