UX for AI Products: Designing Experiences That Explain Themselves

Designing Artificial Intelligence (AI) products is like hosting a party with a magician: people are impressed by what’s happening, but they really want to know how the trick works.

Welcome to the world of UX design for AI, where building user trust isn’t just a feature—it’s the product itself. The challenge? Creating interfaces for technologies that are powerful, complex, and often about as transparent as a brick.

So how do we design AI user experiences that feel both magical and understandable? Let’s dig in.

Why AI Needs Explaining

AI can be unpredictable. It can offer surprising insights, eerily accurate predictions, and occasionally... completely bizarre outputs (hello, AI-generated octopus-pizza hybrids).

The problem isn’t always the result—it’s the lack of explanation. When users can’t understand how or why AI made a decision, they lose confidence. This is especially critical in domains like healthcare, finance, hiring, and legal tech, where opaque algorithms can have serious consequences.

A 2022 Nielsen Norman Group study discovered that users are significantly more likely to adopt AI tools and AI assistants when features are accompanied by simple, digestible explanations.

Design Principle #1: Show Your Work

Like a high school math teacher, your AI should show how it got the answer.

This doesn’t mean dropping a wall of probabilities and model weights on the user. It means clear, contextual explanations, such as:

• We recommended this article because you read similar ones last week.
• This product is trending in your area.
• This estimate is based on the last 12 months of your activity.

AI design tools like LIME and SHAP (popular in data-driven design) offer model interpretability behind the scenes. UX design best practices should translate that into natural language insights that real humans understand.

Design Principle #2: Design for Uncertainty

AI isn’t perfect—and pretending it is just makes mistakes more painful.

Good AI UX includes confidence indicators and cues about certainty. Think of:

• Confidence scores ("We're 85% sure about this prediction")
• Visual indicators (e.g., faded suggestions for lower certainty)
• Warnings or disclaimers (“This is a prediction, not a diagnosis”)

Even better: give users a way to correct the AI. Feedback loops build trust, help models improve, and keep users in the driver’s seat.

Design Principle #3: Let Users Peek Behind the Curtain

Explainability doesn’t mean overwhelming users with data—but it does mean giving them access to it when they want it.

Consider a layered approach:

• Surface level: A short, friendly summary of “why this?”
• Deeper dive: A clickable link for users who want more context
• Full audit trail (for expert users): Shows data inputs, sources, or model logic

Real-World Examples

• Grammarly highlights AI edits and explains the logic behind them ("Wordiness—try a shorter phrase")
• Spotify gives users context for recommendations (“Because you liked lo-fi beats and rainy day vibes”)
• Google Maps sometimes explains detours (“Faster route due to traffic on your usual path”)

These micro-explanations build macro trust.

No More Mysterious Machines

The future of AI isn’t just about smarter algorithms—it’s about more transparent, user-centered experiences. Users don’t just want to be wowed by AI; they want to understand it, question it, and even override it when needed.

Designers are the bridge between complex technology and human clarity. If we get it right, AI will feel less like a robot overlord and more like a capable, trustworthy teammate.

At FabCom, our integrated team of strategists, UX designers, AI specialists, and developers is uniquely equipped to create intuitive, user-centric experiences that align with the evolving demands of AI-driven products. With a proven track record in blending deep technical expertise with human-centered design, we help brands not only meet user expectations—but exceed them. Explore our full range of capabilities.