The Importance of UX Research for AI

Brands are constantly striving to make their products and services as user-friendly as possible. This includes customer support channels, where AI features like chatbots and voice assistants are becoming increasingly common. To ensure these AI-powered experiences are truly user-friendly, companies must understand customer needs and preferences. This is where user experience (UX) research studies and evaluations come in.

UX research throughout the development cycle

Organizations should conduct UX research throughout the entire product development cycle, from product ideation and definition to design, implementation and release. Different research methods are used depending on the development phase and the type of AI interface being evaluated. This article explores some common UX research methods suitable for AI features like recommendation systems, summarization tools, AI-powered search and conversational AI interfaces like chatbots and voice assistants.

Early-stage research: understanding user needs

In the early stages of development, it’s important to gather qualitative data to understand user needs and expectations. Here are some methods commonly used:

• User interviews: User interviews are essential for gathering insights into user needs, expectations and concerns. For AI products, this includes understanding concerns about data privacy and the accuracy of AI responses. Interviews also help uncover the context in which users will interact with the AI, such as whether they need a hands-free experience or prefer written or spoken responses.
• Ethnographic research and diary studies: Ethnographic research involves observing users interacting with the AI in their natural environment. This is particularly useful for interfaces used frequently throughout the day. Diary studies, where users record their experiences over a period of time, can also provide valuable insights.

Prototype testing: gathering feedback on design

Prototypes help visualize and test the AI interface before full development. There are two main types of prototypes:

• Static prototypes: Static prototypes are non-interactive representations of the AI interface that can be in the form of screenshots, a video or, in the case of voice assistants, an audio clip. They can be used in moderated or unmoderated studies to gauge initial user reactions and gather feedback on the design. However, static prototypes have limitations, especially for AI interfaces, as they cannot demonstrate the full functionality of the system.
• Interactive prototypes and Wizard of Oz testing: Creating interactive prototypes for AI interfaces can be challenging. Wizard of Oz testing offers a solution by using a human to simulate the AI’s responses. This method allows for user testing earlier in the development cycle, but it can be difficult to simulate the speed and complexity of a real AI system.

Usability testing: ensuring a smooth user experience

Before launch, usability testing is crucial to identify any pain points and areas for improvement in the AI interface. While the minimum viable product (MVP) approach is often used for initial testing, releasing a poorly functioning MVP can negatively impact user perception and brand reputation.

For AI interfaces, both the interface design and the content are crucial for a positive user experience. Each requires a slightly different approach:

• Interface design: Moderated and unmoderated studies help evaluate the interface design and uncover any issues that may hinder an intuitive user experience.
• Content evaluation: The content should be relevant, accurate and engaging. Because Gen AI can provide a wide, non-deterministic range of responses, meaning one conversation can take many potential paths, these studies require a larger participant sample size to identify patterns and ensure quality.   

Post-launch research: continuous improvement

Even after launch, it’s important to continue gathering user feedback to improve the AI experience. There are two key ways to secure this feedback:

• Analytics data: Once a product is live, analytics data can indicate points of friction, such as where users abandon the interaction. However, analytics alone cannot explain the ‘why’ behind user actions.
• Qualitative studies: To uncover this ‘why,’ qualitative studies, such as user interviews and unmoderated studies with user videos, are crucial for understanding the reasons behind user behavior and identifying areas for improvement.

Effective UX research is essential for creating successful AI interfaces. By employing a variety of research methods throughout the development cycle, brands can ensure their AI products meet user needs, deliver positive experiences, and achieve business goals.