From: aidotengineer
AI simulation offers a new approach to accelerating design, particularly in the realm of user experience (UX) [00:00:11]. The core concept revolves around empowering designers to work with “invisible users” in the form of AI simulation [00:03:58].
The Challenge: The AI Trust Gap
Currently, AI faces a “trust gap,” with only 32% of US adults trusting AI and 44% globally feeling comfortable with businesses using AI [00:00:35]. This is largely due to “AI slop,” where AI-generated content or features on websites and products are incorrect or misleading, like search results suggesting to eat rocks or cars for $1 [00:00:56]. While website creators may present AI chatbots as “magic,” the true potential of Generative AI (GenAI) lies in its ability to enable users to interact with machine learning models in natural language, a significant UX revolution [00:01:31].
The goal is to use AI to design interfaces that genuinely feel magical, not by simply embedding chatbots, but by accelerating “need finding” – the process of understanding user requirements [00:02:31]. This aligns with Don Norman’s concept of “invisible interfaces,” where software is so seamless and intuitive that users forget they are using it [00:02:20].
Evolution of the Design Process
Traditional design processes are data-driven, relying on qualitative observations, quantitative data, and ethnographic studies to guide the prototyping process [00:03:33]. However, the modern world bombards users with information and complexity, much like a pilot operating in a cockpit, where training is often done through simulation [00:03:07].
This idea of simulation can be evolved for design. Instead of just collecting data artifacts, these artifacts can become active participants in the design process through AI simulation, providing designers with a mini feedback cycle to enhance need finding [00:03:54].
Intelligent Twins: Invisible Users in Simulation
AI simulation for design revolves around “Intelligent Twins,” which are digital representations of user behaviors, desired outcomes, needs, and motivations [00:05:41]. They become active participants in the design simulation process [00:05:52].
The new process for need finding using AI simulation has similarities to existing methods but with key differences:
- Define Audience with Data: Start with data representing target audiences, including demographic, psychographic, and contextual information, to simulate behaviors [00:05:06]. For example, Huge’s “Live” data platform collects diverse datasets for this purpose [00:05:14].
- Apply Intent Mapping: Transform this data into active “Intelligent Twins” that embody user behaviors and desired outcomes [00:05:34].
- Brief Intelligent Twins: Instruct these twins to evaluate interfaces by focusing on specific tasks, similar to how a human designer might conduct a heuristic analysis [00:05:56].
Advantages of Intelligent Twins in Design Audits
The primary advantages of this simulated methodology are scale and speed [00:06:23]. Intelligent Twins can operate with higher velocity than human teams, allowing for extensive audits.
Example Project: Global Sports Website Audit
To illustrate, a sample project applied Intelligent Twins to a global audit of sports websites [00:06:36]. The project aimed to understand how to engage audiences across different countries and cultures for a business partnering with sports leagues [00:06:41].
The Intelligent Twins were categorized into two personas:
- A casual fan (newer to the sport) [00:07:08]
- A super fan (lifelong and savvy) [00:07:11]
These twins were briefed with a series of tasks across different websites (e.g., basketball, Olympics, English Premier League) and categories like navigation, information architecture, and fan engagement. With four tasks per category, this enabled 72 AI-simulated actions for the design audit [00:07:18].
The findings demonstrated the power of this approach:
- High-Level Insights: It revealed high-level understandings across an entire category quickly. For instance, in the sample audit, navigation tasks performed well across all sites [00:08:01].
- Identifying Friction Points: However, as fans delved deeper into content browsing, information architecture, and engagement pathways, task completion rates dropped significantly [00:08:29]. This highlighted user pain points, which, if addressed, could help repair the “AI trust gap” [00:08:53].
- Granular Detail: The methodology can scale to surface friction across specific nuances of the user experience, allowing for the creation of very focused or broad and deep design briefs [00:09:06].
- Generating Design Briefs: Through AI acceleration, including computer use models and computer vision models, this process can generate precise design briefs, enabling human teams to focus on solving specific user pain points [00:09:33].
Future Outlook and Limitations
The rapid progress in AI, such as Anthropic’s MCP protocol, which shows potential to turn prototype components from Figma into actual code (e.g., React, Node.js), will make creating new designs easier than ever [00:10:06]. This emphasizes the importance of focusing on the “why” – the strategy and the problem to solve for users – as the “how” becomes increasingly automated [00:10:36].
As this methodology is experimental, key areas for improvement include:
- Reproducibility: Standardizing parameters in a code repository, such as briefing instructions, simulated audience dimensions, number of audit runs, and task completion/failure parameters [00:11:00].
- Test and Control Methodology: Applying a test and control approach to isolate the strengths of Intelligent Twins for design need finding and understand how they can complement human teams across various industries, geographies, and domains [00:11:24].
In conclusion, users don’t need more ineffective GenAI chatbots on websites [00:11:57]. Instead, they need better websites, mobile apps, and interfaces that provide clarity and simplicity [00:12:06]. AI simulation, through Intelligent Twins, can empower design teams to gather insights smarter, faster, and better, ultimately creating interfaces that restore and repair user trust [00:12:17].