AI in User Research: Tools, Trends, and What to Watch for in 2025

User research is central to building great products. Understanding what users want, need, and expect can make the difference between a product that thrives and one that flops. Traditionally, user research required countless hours of interviews, surveys, and analysis, but Artificial Intelligence (AI) is changing the game.

In this post, we’ll dive into how AI is transforming user research, explore some popular tools (with their pros and cons), and peek into the trends shaping 2025.

Why AI is Changing User Research

AI can process vast amounts of data quickly, uncover patterns, and even predict user behaviour. Here are some reasons why AI is becoming a go-to tool in user research:

• Speed: AI can analyse data in minutes, not weeks.

• Scalability: Handle millions of user inputs without breaking a sweat.

• Objectivity: AI minimises human biases in data analysis.

• Insights at scale: From survey responses to social media chatter, AI pulls insights from everywhere.

Popular AI Tools for User Research

Let’s take a look at some leading AI tools used for user research, along with their pros and cons.

1. Dovetail: A qualitative research platform that uses AI to transcribe, analyse, and synthesise insights from user interviews and surveys.

It’s an easy-to-use interface for organising qualitative data, it has AI-assisted tagging and theme identification and it integrates with popular research tools like Zoom and Slack. However, it’s limited to qualitative data, which might not be ideal for quantitative-heavy studies and pricing can be high for smaller teams.

2. UserTesting (with AI): UserTesting now integrates AI to analyse session recordings, highlight key moments, and generate insights faster. It automates the analysis of video and voice data and identifies patterns in user behaviour across sessions, which saves time by summarising long interviews or tests. But, the video analysis isn’t always 100% accurate, a common issue with using AI, which is why it’s important to check, meaning that it still requires human validation for critical insights.

3. Qualtrics XM: Qualtrics uses AI to analyse survey data, predict trends, and provide recommendations. It uses advanced text analytics for open-ended responses, predictive analytics for user satisfaction and churn and visualisation tools for presenting data. However, it’s complexity means it takes time to learn and it can be too expensive for small or medium-sized organisations.

4. Lookback: An AI-powered tool for live user testing and remote interviews, with analysis features. Great for real-time feedback collection, as it automatically identifies common phrases or sentiments in user comments and it has collaborative features for team analysis. It’s not as robust for large-scale studies and has limited integrations compared to competitors.

5. OpenAI’s GPT (for User Surveys and Analysis): Using GPT-powered tools to analyse survey data, generate questions, or summarise findings is increasingly popular. It’s customisable for various research needs, great for generating user personas or hypotheses to give you a start and it can process large datasets quickly. But it needs strong prompts for accurate results and it lacks industry-specific insights unless fine-tuned.

Trends for AI-Driven User Research for 2025

The future of user research is looking stong, and here’s what to expect in 2025:

1. Hyper-Personalised Insights as AI will offer more tailored insights based on granular user data. Expect tools to deliver highly specific recommendations for different user segments, making it easier to cater to diverse audiences.

2. Multi-Modal Analysis: Upcoming tools will combine text, voice, video, and even biometric data into one unified analysis. This means researchers can glean insights not just from what users say, but how they say it and how they behave while saying it.

3. Real-Time Adaptive Research: AI tools will increasingly support "in-the-moment" research, dynamically adapting questions or tests based on user feedback in real-time.

4. Ethical AI and Bias Reduction: As ethical concerns grow, tools will focus on minimising bias, ensuring diverse user groups are equally represented in analyses.

5. Integration with Product Lifecycle Tools: AI-driven user research tools will integrate more seamlessly with product design and development platforms like Figma, Jira, and Notion. This integration will create a feedback loop that bridges research with actionable design decisions.

6. Voice of the User in AI Training: AI systems will evolve to learn directly from users' natural interactions with products, bypassing traditional survey mechanisms.

Should You Embrace AI for User Research?

Absolutely! Whether you’re a startup or an enterprise, AI tools can supercharge your research by saving time, reducing manual effort, and uncovering insights that might be missed otherwise. However, AI isn’t a silver bullet as it still needs skilled humans to guide its application, check its outputs, interpret its results, and ensure ethical practices.

2025 is the Year to Try AI in User Research

AI is reshaping user research, making it faster, smarter, and more impactful. Tools like Dovetail, UserTesting, and Qualtrics are already leading the pack, but the real excitement lies in the trends for 2025. Whether you’re just dipping your toes into user research or looking to upgrade your toolkit, now is the perfect time to explore AI-powered options.

Have you tried any AI tools for user research? Share your experiences in the comments as we’d love to hear how they worked for you! 😊