How do you make AI features feel more human and less robotic?

Use conversational language, acknowledge uncertainty, and design for user control. When users can influence the outcome—and when the system admits it might be wrong—it feels more collaborative and human.

Can traditional UX designers learn to design for AI?

Absolutely. The core UX skills still apply, but you’ll need to layer in comfort with unpredictability, faster prototyping cycles, and thoughtful handling of AI’s limits. Think of it as an evolution, not a reinvention.

What are common mistakes to avoid in AI UX?

Avoid overpromising the AI’s abilities, skipping transparency, or designing without user feedback. Also, don’t assume the AI will always behave as expected—design for failure states, not just success.

What’s Different About Designing AI-Powered Features Compared to Traditional UX?

If you're a UX designer or product person, you already know good design starts with understanding your users and what they're trying to do. But when AI enters the mix, the game changes a little.

At Hocket, we design AI-powered features every day. Here are the biggest differences we've learned to navigate:

1. Handling Uncertainty and Variability

Traditional UX relies heavily on predictability. Buttons click, menus open, and things behave as expected. But AI features are built on predictions, probabilities, and uncertainty, not reasoning in the traditional sense.

Your job isn’t just designing happy paths; it's designing smart fallbacks and graceful exits. You prepare for when the AI gets it wrong, because it will happen.

That means:

Clear ways for users to correct or override AI
Helpful messaging when predictions aren't perfect
Designing interactions that feel fluid, not rigid

2. Transparency Is Non-Negotiable

AI-powered UX demands transparency. Users want to know how decisions get made, especially if those decisions feel personal or significant.

So, explain what's happening in plain language. Show your work. For example:

Why did we suggest this product?
How did we categorize your data?
Why did the AI make this particular recommendation?

When users feel informed, they're much more forgiving of imperfections.

3. Expectation Management Is Everything

Traditional UX often means designing features users trust implicitly. But with AI, you're designing for trust that's earned.

Set expectations realistically from the outset:

Don’t oversell the AI—be honest about its limitations
Use language carefully ("suggestion," "prediction," not "definitive answer")
Clearly indicate when a feature is AI-driven

Your users' trust grows as you meet or exceed the expectations you set.

4. Testing Early, Even Before Tech Is Ready

Unlike traditional UX, where you might wait for functional prototypes or full dev cycles, AI-driven UX benefits enormously from early-stage testing.

Use Wizard-of-Oz techniques or mock AI responses to test interactions before the actual AI is built. You get invaluable user feedback early, saving time, budget, and headaches.

5. Constant Iteration, Even After Launch

With traditional UX, once something’s built and tested, it generally remains stable. AI-powered features evolve constantly. As data and models change, your UX must adapt too.

Design your processes to support continual iteration:

Regularly revisit user interactions as AI performance evolves
Monitor how AI improvements affect user experience
Stay agile and responsive—plan for ongoing updates

The Bottom Line

AI-powered UX isn't a radical departure from traditional UX—it still centers on the human experience. But it adds new layers of complexity around uncertainty, transparency, expectation-setting, early prototyping, and continuous iteration.

If you're feeling overwhelmed by those new layers, don't worry—we've got your back.

Get in touch

FAQ

Use conversational language, acknowledge uncertainty, and design for user control. A touch of personality goes a long way when the system is unpredictable.
Of course! The foundations of good UX still apply, but you’ll need to get comfortable with designing around uncertainty, testing early, and building for explainability.
Don’t overpromise what the AI can do, avoid black-box interactions, and don’t skip user testing. One of the biggest traps is designing for the “happy path” only.