Despite the hype and popularity of AI tools, they are still relatively new to users. Although working on mostly the same principle, they can overwhelm the user and cause unnecessary anxiety. As the technology is quite complex, it falls on developers and designers to facilitate this customer journey by making the interface design more user-friendly, understandable, and straightforward.
The Role of UX in AI
UX plays an important role in AI. It’s not about the aesthetics, as many might think, but about making technology transparent and controllable. AI may seem “too smart,” that’s why design should “remove fear” of it and give users a sense of control. The whole movement recently emerged around these issues and is called the Explainable AI (XAI). It focuses on making AI tools more transparent and understandable for people, explaining, for example, what kind of information the machine used to prepare its output and results. A good example of these measures could be tooltips or clear statuses about the current process stage.
Basic Design Principles
In this area of knowledge, the most important role goes to the UX design itself. It’s essential to adhere to basic design principles and create with the user in mind, ideally based on the results of UX research for specific or similar apps.
Simplicity and Minimal Barriers
Complex solutions, where a lot is going on, can be overloaded with settings and data. However, for users, simplicity wins over the abundance of choice. This can be achieved in a few main ways, such as showing only the required actions on every step of the process without the visual overload; using simple and commonly used terminology, like “Log in/log out,” “Close,” and “Open.” The more you minimize the cognitive load, the easier it will be for the user to fulfill their tasks without getting overwhelmed.
Transparency and Explainability
The thing people struggle with the most while using AI is understanding how it came to a certain decision and isn’t glitching. Users want to know the flow or the reasoning behind answers. A solution to address this issue might be as simple as using explanatory notes or providing sources for a certain option. Visualization of steps — for example, decision-making checkpoints can also be helpful. Even a partial explanation already increases trust. A good example of this is Spotify’s reasoning for music suggestions (“Because you listened to X”).
Trust and Predictability
Mistrust of the AI tools arises if the machine behaves randomly or illogically. Ideally, the system should respond consistently and without any surprises, meaning it has to behave the same in similar conditions. For example, if you ask your AI helper to reply to you always in a language you’re talking to it, or only use specific symbols (like “” instead of ‘’). The logic behind this is quite simple: first comes the consistency, then the predictability, and then the trust.
User Control
Another rule of thumb when building an AI tool is that the user should always feel in charge of the process. Whenever they want to cancel or correct a certain action, they should be able to do it without any difficulties. This can be achieved by using Undo/Redo as a mandatory UX pattern; adding options like “Edit output” or “Adjust recommendation”; or declining the suggestions. It’s important to give the user a feeling that AI is a simple helper and not a controlling force.
Design in Action: 3 Interaction Examples
Besides adhering to the UX principles, it’s important to keep in mind the interaction scenarios with the tool. User flows like first launch or onboarding, everyday usage, and delivering results are the most common ones.
1. First Launch and Onboarding
The first launch and onboarding can make or break the use of the tool. If the users are dissatisfied with these stages, they are more likely to leave and never come back. The main task at this stage is not to overwhelm them but still provide them with tips on how to utilize the tool. The best way to do it is by using minimal text and maximum interactivity. For example, instead of long guides, demonstrate with examples like “Try asking this.” Another important thing is to add the option to “skip training” and return later, so the user knows they can always go back to it if facing any issues.
2. Everyday Interface Usage
Most user scenarios are repeated, and the UX should adapt to them and the user’s habits. So instead of requiring the customer to repeat frequent tasks over and over again, add “one-click” or auto-suggest options. For example, auto-fill the search queries or save templates for repeated actions. The most common tasks should require a minimum of clicks. Of course, AI learns, but the interface should also “learn” and adapt to the user.
3. Showing Results Without Intimidating
AI results can sometimes be cumbersome – take up a lot of space and overwhelm with the amount of text. In such cases, it can be smart to use visualization methods like diagrams, cards, or previews to convey the main ideas. Instead of 20 pages of text, show the essence of the answer, such as “5 key insights” or “the best option.” If the user wants to see more, you can always use the “See more” button. Providing friendly output instead of dry machine results is equally important.
Future of UX in AI Products
UX is to become a bridge between complex technology and users. Although complex, AI should remain human and be viewed as a tool for people and not just an autonomous magic block. As long as it stays “human,” we can utilize it to our advantage and do great things together.
