From: aidotengineer

AI simulation offers a revolutionary approach to design, enabling teams to accelerate need finding and create “invisible interfaces” that are seamless and intuitive for users [00:00:11]. This method seeks to address the current “AI trust gap” by delivering better user experiences [00:00:35].

The Current State of AI in UX: A Trust Gap

Recent research from December 2024 by Edelman highlights a significant “AI trust gap” [00:00:38]:

  • Only 32% of US adults trust AI [00:00:44].
  • Only 44% of adults globally feel comfortable with how businesses use AI [00:00:48].

This trust gap is often attributed to “AI slop,” where generative AI (GenAI) fails by providing incorrect information or non-sensical outputs, such as a search result suggesting eating rocks daily or a website offering a car for $1 [00:00:56]. While some website creators are “stuffing AI chatbots” everywhere and calling it “magic,” the true magic of GenAI lies in its ability to allow users to interact with machine learning models using natural language—a significant UX revolution [00:01:31].

The Opportunity: Invisible Interfaces Through AI

Inspired by Don Norman’s concept of “invisible interfaces” from The Invisible Computer—software so seamless users forget they are using it [00:02:18]—the opportunity is to use AI to design interfaces that genuinely feel like magic [00:02:31]. This is achieved not by superficial chatbot integration, but by accelerating the “need finding” process [00:02:39].

The New Design Life Cycle with AI Simulation

Traditional design processes, pre-ChatGPT, are data-driven, relying on qualitative observations, quantitative data, and ethnographic studies to guide prototyping [00:03:33]. The proposed new approach for design in an AI era draws parallels to pilot training in a complex cockpit environment, which heavily utilizes simulation [00:03:13].

The goal is to empower designers to work with “invisible users” in the form of AI simulation [00:03:58]. This turns data artifacts from need finding into active participants in the design process, providing designers with a mini feedback cycle to enhance design outcomes [00:04:04].

Accelerated Need Finding Process in the AI Simulation Era

The new process for need finding retains core similarities with existing methods but introduces key differences in components and workflow [00:04:32]:

  1. Define Audience: Start with robust data representing target audiences, including demographic, psychographic, and contextual information [00:05:06]. Huge, for instance, uses its “Live” data platform for this purpose [00:05:14].
  2. Intent Mapping: Transform this data into “intelligent twins”—active simulations representing user behaviors, desired outcomes, needs, and motivations [00:05:34]. These intelligent twins become active participants in the design simulation [00:05:52].
  3. Identify Specific Tasks: Brief intelligent twins to evaluate interfaces by focusing on specific tasks, similar to how a human designer might conduct a heuristic analysis [00:05:56].
  4. Conduct Analysis: Leverage the scale and speed of intelligent twins to perform audits [00:06:23].
  5. Refine Insights: Roll up high-level insights or dive into granular details [00:07:50].
  6. Develop Design Alternatives: Generate focused design briefs for human teams based on AI acceleration, computer vision models, and human-in-the-loop observation [00:09:41].

Example Project: Global Sports Website Audit

To illustrate the methodology, a sample project audited global sports websites from the perspective of a business seeking to partner with sports leagues worldwide [00:06:36].

  • Audiences: Two personas were defined: a “casual fan” new to the sport and a “super fan” who is lifelong and savvy [00:07:08].
  • Tasks: Across three different sports websites (basketball, Olympics, English Premier League), a series of tasks were briefed for navigation, information architecture, and fan engagement categories, with four tasks per category [00:07:18].
  • Simulation: This allowed for the simulation of 72 AI simulated actions [00:07:34].

Findings:

  • All audited sites performed well in navigation tasks (the starting place for a fan) [00:08:12].
  • However, success rates dropped significantly as fans delved deeper into content browsing, information architecture, and engagement pathways [00:08:29].

This audit highlights user pain points, demonstrating how the methodology can help understand and solve them, thereby helping to repair the “AI trust gap” [00:08:53]. The methodology’s ability to operate at different levels of altitude (broad or deep) allows for the creation of focused design briefs [00:09:18].

Future Considerations and Limitations

As AI tools accelerate, it will become easier to create new designs, emphasizing the importance of focusing on the “why” (strategy and problem-solving for users) rather than just the “how” [00:10:31]. For example, the MCP protocol from Anthropic is already showing potential for turning prototype components in Figma into actual code components in React or Node.js [00:10:12].

This methodology is still experimental and in early stages [00:10:53]. Key areas for improvement include:

  • Reproducibility: Standardizing parameters like briefing instructions, simulated audience dimensions, number of audit runs, and task completion/failure parameters in a code repository [00:11:00].
  • Test and Control Methodology: Applying a test and control approach to isolate the strengths of intelligent twins for design need finding [00:11:24]. This will help understand how intelligent twins can be used alongside or complementary to human teams across different industries, geographies, and domains [00:11:35].

Conclusion

To repair the current “AI trust gap,” the focus should shift from poorly implemented GenAI chatbots to creating better websites, mobile apps, and interfaces that offer clarity and simplicity [00:11:57]. AI simulation is a powerful tool to empower design teams, enabling them to gather insights smarter, faster, and better, ultimately leading to interfaces that restore user trust [00:12:17].