It's finally 2025! I know this post is a bit late to wish you a Happy New Year, but I'll do it anyway: Happy New Year 2025 to all! But that's not all. I'm back with new ideas, and this time, let's talk about AI, but from the perspective of UX research. Yes, I see you, UX Researchers! Today, we're going to explore how AI can improve research phases and how UX researchers can leverage this technology.
What is UX research?
Most of you have probably already played the role of UX researcher, whether voluntarily or not. For those who have never heard of this field, let me quickly explain what it entails.
Generally, you'll easily recognize a UX researcher: they're the ones who ask tons of questions, all the time, all day long. They're constantly pestering the client or project manager to get answers to their questions. But more seriously, they're the person in charge of the discovery, definition, and ideation phases.
But we all know that, depending on the company and its level of maturity, research is sometimes limited to an interview with the customer, a few user interviews, the creation of personas, a quick benchmark, and that's it.
Except that in reality, research is much more than that:
- Analyze data
- Conduct interviews
- Conduct user tests
- Conduct competitive analyses
- Using eye-tracking
- Analyze heatmaps
- Create experience maps and empathy maps
- Organize focus group workshops with stakeholders
- Shadowing
- Organize Design Studios
… And much more !
As you can see, there are many different roles, and the UX researcher profession is very varied. It's an essential role, but unfortunately, it's not always well understood by everyone.
UX research in business
UX research is a bit like the company's quiet superhero. It optimizes the user experience and eliminates friction. It helps create more intuitive and satisfying products. Pretty handy, right?
I have to advertise this profession, because often companies prefer to have one or two people in-house who juggle all the UI and UX hats.
And I think it's safe to say: it's very difficult to be an expert on every subject.
Thanks to its continuous approach, UX research allows the company to remain agile and relevant in a constantly evolving market – because we don't want to be the one stuck in the 2000s, that's a stain.
And then, it fosters collaboration between teams, aligning everyone around real user needs and optimizing strategic decisions. In short, UX research is our best ally for product success.
The benefits that the company derives from UX research work
UX research isn't just about improving the experience of a product or service. It also generates concrete and measurable business results for companies. Whether in terms of profitability, customer loyalty, or market performance, UX research has proven its effectiveness on several levels.
According to several studies, each euro invested in user experience can generate a return on investment ranging from 2 100 to € €This variation depends on the efforts put in place and the intensity of engagement with users. This figure highlights that companies that invest in UX research reap considerable financial benefits, while also improving customer satisfaction.
Companies that prioritize user experience often outperform their competitors. According to a McKinsey study, the best-performing companies in UX outperformed the S&P (Standard & Poor's) index by 35%.
This difference in performance shows that UX research is not only an advantage in terms of customer satisfaction, but also a differentiating factor in an increasingly competitive market. By offering a better experience, these companies attract more customers, increase their loyalty, and strengthen their market position.
AI and research
Finally, let's talk about AI, because I know that's why you're reading this article. But I had to give UX Research its due; this profession is too exciting not to be highlighted.
But what is AI?
To avoid having to copy and paste my definition of AI into my previous article “Is AI killing traditional user testing or optimizing it?” Here's a quick summary of what AI is: AI (artificial intelligence) aims to mimic certain human abilities such as language understanding, image recognition, decision-making, and learning from data. It is ubiquitous in everyday life, from voice assistants (Siri, Alexa) and smartphones (facial recognition) to aiding medical diagnosis and personalizing treatments.
AI is also used in self-driving cars, navigation systems like Waze, and even in entertainment to generate artistic works or assist visual creation. In short, AI has become an extension of our daily lives, similar to the role of smartphones in our lives.
Can AI do research?
AI is a bit like our super-efficient teammate, it's our R2-D2.
It's there to automate tasks, analyze data, and discover patterns, allowing researchers to focus on deeper insights and strategic decisions (basically, it does all the boring work while we can be creative, think of brilliant ideas...).
But be careful, AI is not here to take our place! It must be seen as a starting point, not as a replacement for our human expertise.
Here's how AI helps our UX researchers:
- Automation of research tasks : Simplifies participant recruitment, research planning, and data organization, reducing manual effort.
- Transcription and analysis of interviews : Transcribes interviews in real time, highlights key themes and generates summaries.
- Improving data accessibility : Organizes large volumes of data, making them easily searchable and retrievable.
- Generating research reports : Summarizes information into reports and visual presentations for better communication of results.
- Processing of survey responses : Speeds up analysis by categorizing open-ended responses, identifying sentiments, and summarizing key points.
AI tools that can be used in research
Here is a non-exhaustive list of tools available on the market to improve UX research.
Benefits:
- Content generation : ChatGPT can create personas, problem statements, and user stories, accelerating the discovery and specification phases.
- Editorial assistance : It helps write user support copies and generate realistic data for prototypes.
Boundaries :
- Contextual understanding : ChatGPT may lack nuance in interpreting specific contexts, which may affect the relevance of the generated content.
- Dependence on input data : The quality of responses depends heavily on the prompts provided, requiring precise wording to obtain useful results.
Benefits:
- Automated user testing : Maze facilitates unmoderated online testing, allowing researchers to gather feedback on prototypes without direct supervision.
- In-depth analyzes : The tool generates heatmaps and sentiment analyses, helping to identify user friction points and emotions.
Boundaries :
- Lack of human moderation : Lack of supervision can lead to misinterpretations of tasks by users, affecting the quality of the data collected.
- Dependence on prototype quality : Poorly designed prototypes can lead to unreliable feedback, limiting the effectiveness of testing.
Benefits:
- Transcription and analysis of interviews : Looppanel transcribes interviews in real time, identifies key themes and generates summaries, reducing the time spent on manual analysis.
- Data organization : The tool structures research data, making it easily searchable and retrievable.
Boundaries :
- Accuracy of transcriptions : Strong accents or poor audio quality may affect the accuracy of transcriptions.
- Contextual interpretation : AI may struggle to capture contextual nuances or subtle emotions expressed during interviews.
Benefits:
- Simulation of behaviors : Synthetic users allow for modeling various behaviors without the need for real participants, thus speeding up the testing process.
- Exploration of varied scenarios : They allow a wide range of user scenarios to be explored without logistical constraints.
Boundaries :
- Lack of authenticity : Simulated behaviors may not accurately reflect the actions of real users, limiting the validity of tests.
- Lack of emotional feedback : Synthetic users do not provide emotional reactions, which are essential for understanding the full user experience.
Benefits:
- Facilitation of ideation : FigJam AI helps generate ideas, organize brainstorming sessions and structure planning sessions, promoting collaboration between teams.
- Data visualization : It transforms raw data into understandable visualizations, helping to identify trends and insights.
Boundaries :
- Learning curve : Users may need time to fully master the tool's features.
- Dependence on connectivity : A stable internet connection is essential for optimal use, which can be a disadvantage in certain situations.
Benefits:
- Quick information search : Perplexity.ai provides concise answers in real time, facilitating information gathering during the research phases.
- Reliability of sources : It clearly cites the sources of information, allowing researchers to verify and deepen the data provided.
Boundaries :
- Limited scope : The tool is primarily designed for general information research and may not cover very specific or technical topics.
- Dependence on available sources : The quality of the responses depends on the information available online, which may vary in reliability and completeness.
AI is great, but it's not going to solve all our problems, especially those that fall within human reach. Therefore, it's essential to use it alongside traditional methods to achieve truly optimal results.
Conclusion
In my research, I found that 51% of UX researchers already use AI in their daily work. AI is there to support us in collecting and analyzing data, while also allowing our expertise to interpret, question, and, above all, provide creative and human solutions.
AI can quickly analyze tons of user data, spot trends that would have taken days to discover, and even generate reports with key insights. Pretty handy, right?
It can also automate tasks like interview transcription, saving us precious time (because we all know how tedious it is to replay hours of discussions). And then, when it comes to analyzing user behavior with heatmaps or eye-tracking, AI can interpret all of that in a blink of an eye, helping us understand how users really interact with our products.
So I hope that thanks to this article you were able to discover new tools and that I was able to open your eyes to the role and the advantages that AI has to offer us.
Erwan Nisas, UX designer at UX-Republic
