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
"Creating shared coding schemes and having teams do first-pass coding helps maintain consistency in qualitative analysis."
Clemens Janssen Jane DavisEfficiently Scaling Research as a Team of One
March 28, 2023
"I finally started saying, I understand your budget, but I have one too, and I deserve to be paid."
Deanna ZandtThe Unspoken Complexity of “Self-Care” with Deanna Zandt
July 21, 2022
"You have to have a point of view, and you have to lead in order to get that impact in strategic foresight."
Sam LadnerHow Research Can Drive Strategic Foresight
March 9, 2022
"During discovery, it’s important to identify which features require exploration versus those that just need validation or small fixes."
Aditi Ruiz Christian Crumlish Farid SabitovA PM State of Mind: Empathy Mapping Your Product Manager, Pt. 1
December 6, 2022
"Maps stop you from getting lost in legacy complexity and keep everyone aligned on what’s important."
Meaghan Waters Fotina KoutropoulousLack of Product Thinking will Doom Your Legacy Modernization
June 9, 2021
"We can forecast delivery dates despite shifting and volatile project conditions with 95% efficiency."
John Paul de Guzman10k Screens Later: How We Became a Data-Driven Design Organization
September 24, 2024
"Engineers don’t want to mess with CSS; they want to write business logic."
Nalini P. KotamrajuAn Organizational Story: Salesforce Lightning Design System
June 9, 2016
"Participation without the redistribution of power is an empty and frustrating process for the powerless."
Sha HwangThe First Fifty Years of Civic Design
November 16, 2022
"Ways to reduce extraction in research is by embedding community-led practices rather than holding the keys to knowledge ourselves."
Sarah FathallahLessening the Research Burden on Vulnerable Communities
March 30, 2020
