A guide to using AI in UX research

The rise of artificial intelligence (AI) is impacting almost every industry. Experts predict that roles and industries will change, and how we live may look very different in the future. 

AI could have widespread effects on user experience (UX) research. This could be a huge win for researchers whose roles are complex, time-consuming, and challenging. 

It’s already integral in optimizing the workflow, minimizing time spent, and reducing the risk of manual errors.

Researchers may be able to lean into AI tools to speed up their research process, simplify methods, and boost overall accuracy. 

You should apply caution, though. Implementing AI comes with potential pitfalls that you should consider before starting. 

This guide will explain AI’s implications, benefits, and challenges to help you make the most of these emerging technologies.

AI is already impacting UX, and that’s only set to grow. 

Researchers use AI to enhance aspects of the data collection process, analysis, and the subsequent presentation of insights and findings. 

Data collection 

Collecting data from multiple sources requires significant human power to complete. Often, researchers trawl through many sources manually, such as:

• Social media comments

• Website analytics

• Survey results

• Focus group notes

• Usability testing

This is a time-consuming and complex task. Performing it without errors requires a high level of expertise, but AI tools may change all that. 

These tools can collect, categorize, and organize data from multiple sources without as much need for human intervention. This boosts accuracy and speed. 

AI tools can also compile evidence in UX research. Through text-mining, sentiment analysis, heatmap tools, and more, AI can discover key data about user behavior which can feed into decision-making. 

These faster techniques boost knowledge and power across an organization in areas that may otherwise be out of reach. 

Analyzing big data 

When it comes to large data sets, manual analysis processes are outdated, sluggish, and riddled with potential errors––even if a highly-skilled practitioner performs the task. 

AI tools can analyze big data and build predictive models that humans simply can’t. 

Pairing historical data analysis with adequate training enables AI to make reliable predictions. These forecasts can be critical for planning, decision-making, and supply chain management in various industries. 

Identifying themes, patterns, and trends

AI algorithms and machine learning can spot data sets' themes, patterns, and trends through pattern recognition and anomaly detection.

This means uncovering things that humans might miss, such as: 

• The precise moment customers drop off a website

• Which layout option certain demographics prefer

• Offering relevant, specific recommendations that convert 

AI tools can spot trends significantly faster and more accurately than humans, allowing teams to lean into accurate insights for more reliable decision-making. 

Automating UX research 

AI bots development could be significant for UX research in its entirety. AI-led user research could mean human-like bots perform UX research rather than people. 

Imagine these scenarios:

• A bot runs people through usability tests

• The bot asks humans questions during a virtually run focus group

• A chatbot interviews customers through a live chat interface

This offers huge potential to free up time for researchers to focus their energy on creating the right questions for the bots and applying the research findings for faster action. 

Essentially, AI may make the researcher's role faster, more accurate, and more beneficial to teams overall––albeit with limitations. 

Bringing ideas to life 

Developing digital products can be expensive, time-consuming, and hit or miss. Not all ideas work for the user or succeed in the marketplace, but AI can give users a better chance to trial prototype products. 

AI tools make it faster than ever to turn a set of instructions or a basic idea into a highly-realistic image. Plus, these tools will likely improve rapidly. 

UX researchers may use AI more and more for generating new layouts, wireframing, and prototyping to get feedback faster than ever. This can save time in reworks and ensure you’re only creating valuable ideas. 

Unfortunately, this could also leave our UI-designing friends competing against AI-generated interfaces.

Presenting findings 

It’s essential to present any research findings to the broader team and key stakeholders to turn insights into action. A range of learning styles in any organization means you’ll need to present your findings in simple ways, using color, graphs, and highlights. 

AI tools can hasten and improve the presentation process by: 

• Writing some of the content

• Summarizing the core findings

• Turning insights into easy-to-read, digestible graphs 

As efficient as this sounds, data scientists are likely to be most empowered by this while competing for roles more as the technology matures.

While we can’t deny the power of AI, it’s also important for researchers and all stakeholders to recognize that AI in UX research is not a fix-all. 

There are limitations, challenges, and considerations when it comes to AI.

Let’s take the increasingly well-known chatbot, ChatGPT. The bot can produce incorrect information, harmful instructions, and biased content. Plus, people are concerned about heavy reliance on the tool.

AI tools are commonly limited in the following areas:

Context

Context is key for accurate insights. An AI algorithm cannot understand the complete context of a situation, especially if it involves the complexity and nuance of human emotions. 

An AI tool is often not best positioned to pose relevant or suitable follow-up questions. It could also mean qualitative insights may not be as reliable as a human analysis.

Empathy

Human behaviors, thinking, and emotions are not a science: They are multi-layered, changing, and challenging to understand. Human empathy is not a skill an AI tool has today. 

Yet, empathy is a critical component of research to deeply understand participants, put them at ease, and see things from their perspective. 

Flexibility

If a research session moves in an unexpected direction, a researcher can understand this move and go with it. However, an AI tool may be fixed on a certain path of questioning, making it challenging to gain new and unexpected insights. 

Nuance

AI tools rely on training data to generate answers. Therefore, these tools are naturally limited in new ideas, innovation, and nuance. 

Creativity and innovation

We’ll always need human creativity and innovation regardless of how advanced AI tools become. 

While tools can perform many advanced tasks, they are no replacement for human insight, empathy, and flexibility. 

AI tools rely on training data to generate answers, meaning any creativity must be human-led.

Accuracy

While AI tools tend to be relatively reliable, they are not always accurate. They improve over time based on data inputs. Caution and graceful degradation should be in place in case something goes wrong. 

Researchers may need to explore how the algorithm analyzes data to fully understand and report findings.

AI is likely to significantly impact the role of UX researchers in the coming years. The researcher's role is expected to be faster, more efficient, more in-depth, and more consistent.

Faster research 

Using AI may grant researchers more time for: 

• Defining the core problems

• Setting the most useful goals

• Asking better questions

• Uncovering more beneficial insights

The most obvious benefit of leaning into AI tools is speed. Data collection, storage, and analysis are incredibly time-consuming processes. 

Shifting from manual ways of working to AI and automation lead practices will significantly accelerate the role of the researcher.

This could lead businesses to undervalue the important work of UX researchers––something that shouldn’t be replaced by AI, given the role’s various limitations and nuances. 

Reduced costs 

Speeding up the process of UX research means fewer human hours are needed for tasks. 

This may minimize the need to outsource projects to data analytics firms, reduce resourcing allocations, and maximize time––all core cost savers for the business. 

Consistency 

Less reliance on human processes could mean that research results are more likely to be consistent across the board. AI tools may help maintain output consistency by reducing human errors and biases. Still, AI is human-trained, so biases are slipping into AI output. 

As AI is an emerging technology, UX researchers must continue managing the process and run an individual analysis as a backup in case of errors. 

To maintain a level of unprecedented consistency in research, researchers can use:

• AI-powered sentiment analysis

• Automated data analysis

• AI-powered virtual assistants

• AI algorithms

Ease of use 

AI will also impact the researcher’s workflow, bringing ease of use into the role with tools like:

• Natural language processing (NLP)

• Automated user testing

• Predictive analytics

• Automated data collection

Quantity

Boosted research quantity is another positive of using AI in UX research. 

Thanks to significantly faster processes, researchers can perform more activities to discover even deeper insights about their current and potential customers. 

This may mean conducting more studies, collecting data from different methods, or analyzing more data sets. More insights mean more reliable decision-making and successful projects. 

AI in UX research is rapidly evolving, and the number of tools is expected to increase significantly in the coming years. 

Many researchers already use tools to simplify their processes, boost their accuracy, and improve their research findings. 

Some existing tools include: 

Uizard

Using the power of AI, Uizard helps UX teams mock up designs in minutes.

Mocking up products is simpler than ever before with features like:

• Turning screenshots into editable designs

• Scanning sketches to automatically generate designs, prototyping, and wireframing

Uizard helps research teams gain faster insights from users to ensure the products produced are fit for purpose and delight the end customer. 

UserZoom 

Recruiting participants for UX research can be one of the most challenging aspects of the process. UserZoom helps researchers do just that. 

An AI-powered recruiting engine for participants automates the process, helping teams source research participants and customers from across the globe. 

Synthetic Users

Limited testing time, small budgets for testing, and participant recruitment challenges can all be highly restrictive for UX teams.

Synthetic Users is still in beta and has yet to release. Through the power of AI, it promises the chance to test products with AI participants. 

This will help UX researchers discover insights, identify pitfalls, and optimize products without a big budget, timeline, or real participant group. 

Amped Research 

Many researchers require a research assistant to take notes, search through data, and analyze findings. Amped Research is essentially an AI-powered research assistant.

Currently waitlist only, OpenAI GPT-3 powers Amped, and it can generate insights and summaries. It also sends automatic updates to stakeholders and assists with presenting findings for reduced paperwork and faster action. 

Dovetail 

Dovetail leverages the power of AI to move teams from insights to actions in record time. The new workflow will hasten manual tasks and offer tools to analyze data even quicker. As part of this, AI will reduce bias and errors for more reliable insights. 

For example, you’ll soon be able to summarize lengthy conversations into core bullet points. AI can also automate draft insights, discover related trends, and increase accuracy for classifications in the future. 

Rather than replacing how customers work, Dovetail uses AI to support teams in their projects. 

Due to the limitations of AI in UX research, researchers can’t expect an AI tool to fulfill all tasks. Overreliance on AI tools can be problematic. 

If researchers feed an AI tool incorrect or biased information or don’t train it sufficiently, the output will be unreliable. This could lead to negative consequences. 

AI tools are still developing, so we shouldn’t see them as solutions for all tasks or pillars of accuracy. Human discernment is still critical as this technology progresses. 

Using AI tools? Remember: 

• They’re not always accurate

• They improve over time and may be more reliable in the future

• They do not necessarily replace human inputs

• They aren’t a replacement for human empathy, nuance, or creativity 

In the coming years, AI is likely to significantly impact all industries. We expect big changes in UX design and research. 

AI in UX research will likely: 

• Increase personalization for customers

• Boost data-led decision-making

• Rapidly speed up the design process

• Improve research reliability and insights

In the day-to-day, AI may reduce menial tasks for researchers, granting them more time to create questions, set appropriate goals, and produce improved results. 

Holistically, AI could boost UX research for better, more usable products, ultimately helping teams create more satisfying products for users.

While AI will significantly affect the UX research process, it's unlikely to replace UX researchers. Rather, we expect it to automate certain aspects of the role and speed up processes. 

It’s essential that organizations still value the role of UX researchers. The results of UX-performed research depend heavily on empathy and the right questions written by humans. 

Humans can do things that AI can’t, like:

• Design research studies

• Provide nuance and context

• Interview participants with empathy and understanding

• Consider ethical factors

• Generate creative ideas and solve problems

While AI can speed up UX design processes, how long it takes depends on many factors:

• The requested task

• The information you give to the AI tool

• How advanced and relevant the AI system is

• The specific project requirements

The more complex the project, the longer it will take. 

To improve efficiency in UX writing, you can use AI tools to: 

• Generate content

• Improve the speed of content writing

• Make language suggestions

• Provide a consistent tone of voice

• Consider accessibility and inclusivity

• Optimize content for SEO.

However, AI writing bots have limitations. They may provide incorrect, biased, or inconsistent content. That’s why it’s important to check any AI-generated content for accuracy.