Will AI replace user researchers?

AI is shaking up the world of UX research. It's a game-changer that's got everyone in the field abuzz. Different camps have emerged—those pro using AI in User Research, and those who are skeptical or afraid of its capabilities.

In this article we’ll ask what the rise of AI really means for UX Research. Will UX be taken over by AI? Can UX research be automated? We’ll dig into all the questions top of mind for researchers around the world.

But first some context on where we are today. A recent survey by User Interviews shows that 77% of UX researchers are already using AI in some part of their work. These numbers tell us that AI isn't just coming - it's already here in UX research. 

Can UX research be automated?

Only parts of the UX research workflow can be automated. Transcription is a perfect example—AI tools do a great job of providing high quality transcripts today, so we don’t have to do that work manually. AI tools can also be powerful for auto-tagging your data, creating notes, or helping you search through your reports for a quote or insight.

To answer dig into what parts of UX research can be automated, we need to look closely at how AI fits into UX research right now.

How is AI used in UX research?

AI can automate some or part of these 5 workflows for UX researchers:

1. Quantitative analysis: AI can crunch through mountains of user data in no time. It spots patterns and trends that might take humans days or weeks to find. For example, AI could analyze thousands of user comments and quickly tell you the most common complaints or praises about your product.

2. AI-supported qualitative analysis: No, we don’t think you should feed your transcripts into ChatGPT and ask it to tell you the best insights. It doesn’t have the context you do and it’s not that accurate (yet). You can however use AI-powered research analysis tools like Looppanel to auto-tag or make AI notes for your calls.

3. Planning & setting up research:  AI can be used to create research plans, generate outreach emails, and set up discussion guides. They may not be perfect, but they’ll get you 80% of the way there in minutes. Test out these ChatGPT prompts to help you get started. 

4. Transcription: This one’s a classic—AI turns user interviews into text quickly and accurately. This saves hours of typing and lets you focus on analyzing what users said instead of writing it all down.

5. Reporting insights: AI is very good at writing. If you want to generate reports or re-phrase insights, it’s your best friend. If you’re using an open engine like ChatGPT or Claude.ai, just be careful that you’re not putting sensitive data in there without checking their privacy policies!

Interested in learning about the intersection of AI and UX? Read our detailed guide here.

Will AI replace user researchers?

The short answer? No. AI won't replace UX researchers. But it will change how we work. Here's why AI can't take over:

1. Empathy: AI can't truly feel what users feel. AI can analyze emotions, but it can't understand them deeply. As researchers, we pick up on tiny clues in how people act and speak. We can tell when someone is frustrated, even if they don't say it. AI misses these subtle hints.
   
   
2. Creativity: AI struggles to think outside the box. AI is good at finding patterns, but it's not great at coming up with new ideas. In UX research, we often need to think creatively to solve tricky problems. We can connect ideas in ways AI can't.
   
   
3. Context: AI misses nuances that humans catch. AI can collect data, but it doesn't always get the full picture. As researchers, we understand the context behind user actions. We can see how cultural differences or personal experiences affect how people use products. AI often misses these important details.
   
   
4. Ethics: AI can't handle sensitive issues like humans can. UX research often deals with personal or sensitive topics. We know how to ask tough questions in a respectful way. We can change our approach if someone feels uncomfortable. AI doesn't have this level of awareness or flexibility.
   
   
5. Communication: AI can't explain findings to teams like we do. A big part of our job is sharing what we learn with others. We know how to tell the story behind the data in a way that makes sense to different team members. We can answer follow-up questions and give examples. AI can make reports, but it can't have these rich discussions.

How will AI Impact the Future of UX Design & Research?

AI will change UX research, but it won't replace us. Instead, it will:

1. Make us more efficient: AI will handle repetitive tasks, letting us focus on deeper analysis. For example, AI might do the initial coding of user interviews, freeing us up to dive deep into what those codes mean.
2. Expand our reach: We'll be able to process more data and get insights faster. This might let us do more frequent, smaller research studies instead of big, occasional ones.
3. Improve accuracy: AI can spot patterns we might miss, leading to better insights. It might catch subtle trends across different user groups that we wouldn't notice on our own.
4. Change our skills: We'll need to learn how to work with AI tools effectively. Understanding how to prompt AI and interpret its outputs will become a key skill for UX researchers.
5. Raise new questions: As AI grows, we'll need to think about new ethical and practical issues in our research. How do we ensure AI-assisted research is fair and unbiased? How do we explain AI-generated insights to stakeholders?

How to leverage AI for user research

We’ve discussed 5 ways you can leverage AI for UX research: analyzing quantitative data, supporting analysis of qualitative data, transcription, setting up research, and reporting insights.

Let’s dig into some real-life use cases of how you’d use AI for different types of research.

How to use AI for types of UX research

AI will have different benefits for different types of UX research. For example, for user interviews you may find its transcription and summarization features helpful. For surveys you may want its help tagging open-ends.

Here’s how you might use AI for 5 common research methods:

1. User interviews: Use AI to transcribe your calls, make notes for you, and tag them with key themes.
2. User surveys: AI can help create and analyze unstructured open-ends in minutes.
3. Competitive analysis: AI tools like Perplexity can help you gather and sort data about competitor products.

AI is useful for certain parts of your workflow regardless of the method you’re using. For example you can always use AI to help prepare for your research study (e.g., writing a discussion guide or research plan), as well as for writing your final research report.

Interested in learning more about the future of AI and UX Research? Read our detailed guide here.

How to use ChatGPT for UX research

One of the easiest ways to get started with trying AI out in UX research is to test use cases with ChatGPT. While it’s not a research-specific tool, you can use it in meaningful ways to speed up your workflow: 

1. Brainstorming: Ask ChatGPT for ideas on research questions or user personas. It might suggest angles you hadn't thought of before.
2. Writing tasks: Use it to draft survey questions or user test scripts. It can help you word things clearly and avoid jargon.
3. Data summary: Feed it user comments and ask for a summary of main points. This can give you a quick overview of user feedback.
4. Quick answers: Ask it UX-related questions to get a starting point for your research. For example, "What are common usability issues in mobile apps?"

Here’s a set of prompts to get you started.

Remember, always double-check ChatGPT's output. It can make mistakes or be biased. Think of it as a helpful assistant, not a replacement for your expertise.

In the end, AI is a powerful tool, not a replacement for UX researchers. 

By learning to work with AI, we can do our jobs better and create even better user experiences. We can use AI to handle the time-consuming parts of our job, freeing us up to do what humans do best: understand other humans.