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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
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Dan categorizes AI in UX into AI as a material to design smart systems and generative AI that assists or automates UX design tasks.
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In medical AI systems handling genomics, trust and explainability are paramount because errors could have catastrophic impacts.
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Illumina’s product analyzes massive, continuously updated genetic data by mapping DNA variants to phenotypes to aid expert decision-making.
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Ben Schneiderman's human-centered AI framework helps balance augmentation and autonomy, classifying AI tools as super tools or teammates.
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Clean Software's no-code UX platform generates fully coded applications from natural language requirements via semantic interaction models.
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Semantic interaction design, based on decades-old principles and Dan’s book "UX Magic", enables AI to understand actions, objects, and workflows to create coherent UX.
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The generated UX is modular, layered, and optimized, connecting data schemas with UI components and producing localized, accessible, and styled outputs.
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AI-generated UX risks include uniformity of designs across cases and uncompensated errors when requirements are incomplete or incorrect (garbage in, garbage out).
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AI can speed the design iteration cycle dramatically by generating multiple alternatives quickly, enabling faster user testing and refinement.
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The future role of UX professionals is likely to shift toward product design and requirements engineering, focusing less on pixel-level UI design.
Notable Quotes
"If you don’t trust it, then there’s nothing here."
"The AI is looking through material and that material is changing every day – no human can keep up with that flood of information."
"Visual design quality actually affects perceived trustworthiness."
"The minute you say AI is telling you take A over B, you’ll get audited by the FDA."
"The AI understands how you map user stories to workflows to screens and code, based on semantic models and decades of HCI research."
"Garbage in, garbage out – if your requirements are wrong, no AI can fix that."
"AI-generated UX may suffer from sameness across solutions, but it fits well for enterprise internal apps where uniqueness is less critical."
"The AI-generated prototype can run before the backend exists, enabling quick validation and iteration."
"These systems generate accessible, localized UX automatically, including support for multiple languages and WCAG compliance."
"With generative AI handling grunt work, designers can focus more on getting requirements right and higher-order product decisions."
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