Exploring Invisible Interfaces: AI Simulation for Accelerated UX Design

Join me, Alex Les, VP of Data Science and AI at Huge, as we explore how AI simulation can accelerate design by empowering designers to work with 'invisible users' in a smarter, faster, and better process.

1. Alex Les is the VP of Data Science and AI at Huge, a design and technology company.
2. The talk focuses on "invisible users" and "invisible interfaces," using AI simulation to accelerate design.
3. There is currently a trust gap in AI, with only 32% of US adults and 44% of adults globally trusting AI.
4. This lack of trust is due to "AI slop," where websites, products, or interfaces provide incorrect information through AI, chatbots, or search results.
5. The real magic of Genai (Generative AI) lies in its ability to revolutionize UX by allowing users to interact with machine learning models using natural language.
6. Don Norman's principle of "invisible interfaces" emphasizes simplicity and efficiency, making software feel seamless and intuitive.
7. The goal is to design interfaces that feel like magic through AI-accelerated need finding, not by stuffing chatbots into websites.
8. The current design process involves data-driven approaches where designers collect qualitative, quantitative, and ethnographic observations to guide prototyping.
9. In the future, designers could work with "invisible users" in the form of AI simulation, making data artifacts active participants in the design process.
10. This new approach empowers designers with a mini feedback cycle to work with AI simulation as part of the broader need-finding process.
11. The example project demonstrates an accelerated need-finding process for AI simulation, starting by defining the audience and mapping their intentions.
12. In the era of AI simulation, designing audiences involves data representing these groups, enabling simulated behaviors in real-world contexts.
13. Intent mapping is used to create "intelligent twins," active participants in the design simulation process, representing user behaviors, desired outcomes, needs, and motivations.
14. Intelligent twins are briefed to evaluate interfaces by focusing on specific tasks, much like human designers conduct heuristic analysis.
15. The methodology provides unique advantages for a global audit of sports websites, helping businesses partner with sports leagues across different countries and cultures.
16. AI simulation can surface friction in specific areas or nuances of the user experience, allowing for focused, broad, or deep design briefs.
17. As tools become more efficient, designers must focus on the "why" (strategy and problem-solving) rather than just the "how" (creating designs).
18. Limitations of this methodology include reproducibility, which will be addressed by standardizing parameters in a code repository.
19. Applying AI simulation through test and control methodologies will help identify the strengths of intelligent twins for design need-finding and their use alongside or in complementary ways with hu
20. Different industries, geographies, and domains must consider how this methodology can best be applied to their specific needs.
21. The goal is to create better websites, mobile apps, and surfaces that provide clarity and simplicity to users, helping restore and repair the AI trust gap.
22. AI simulation can help design teams gather insights smarter, faster, and better, ultimately resulting in more user-friendly interfaces that build trust.
23. In 2025, there has been significant progress in integrating AI tools into design processes, making it easier to create designs with a focus on strategy and problem-solving.
24. The key takeaway is the importance of focusing on user needs and providing clear, simple interfaces to repair the AI trust gap.

Source: AI Engineer via YouTube

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