Using AI to conduct faster, more efficient user research
As you can see, gathering insights has traditionally been a lengthy process—a primary reason that AI is poised to be a real advantage.
Gone are the days of manually trawling endless data. AI-based tools are ready to do a lot of the heavy lifting, allowing you to focus on things that truly require a human element.
Here are a few areas where you can put AI to work like the assistant you’ve always dreamed of.
Creating research questions with less effort and bringing ideas to life sooner
Developing research materials and questions takes time, effort, and a lot of discussions. Perhaps you know what it's like to sit in on an ideation session and, by the end, feel like your brain is bursting from information overload. Or maybe you’ve felt the tediousness of brainstorming for materials like discussion guides.
If you break these processes down, they involve a significant number of steps, usually including
Determining what you already know
Figuring out what you need to find out
Agreeing on research
Landing on a hypothesis to test
Crafting excellent (clear) questions
Developing a discussion guide, from start to finish
Testing your questions before posing them to participants
While you’ll still have to brainstorm to discover the most relevant topics, nowadays, you can plug your chosen topics into a tool like Notion AI, and it’ll write a discussion guide for you based on your bullet points.
It can even change the tone of your text or translate it for international participants.
If you feel your thoughts are almost coherent but want to spend less time on document creation and formatting, a tool like this could be your dream come true.
Again, check any genAI output to ensure nothing weird slips in.
Bringing your ideas to life sooner and testing usability
Sometimes, product development can be incredibly resource-intensive and still miss the mark 😭. For instance, some startups try to fill market gaps that simply don’t exist or focus too little on the UX. For instance, closing gaps between market understanding and UX is a significant area where AI comes in handy. Your ideas will take shape quicker than ever when you use AI for
Prototyping—you can transform late-night musings into rapid prototypes, efficiently validating practical value.
Market insights—AI can aid in identifying and filling legitimate market gaps.
Usability testing—AI enables the automation of usability testing through human-like bots. These bots are improving at conducting live chat interviews, facilitating virtual focus groups, and executing usability tests.
Ultimately, you could recover plenty of precious time to focus on applying your research findings.
Expediting data analysis and predictive modeling
AI, specifically machine learning, accelerates traditional survey analysis. In the past, analysis has been a challenging area for researchers without coding skills, but that’s changing. Significant advances include:
Text mining—swiftly analyzes extensive text, like call center transcripts, extracting valuable insights without human effort.
AI can supercharge sentiment analysis, determine feedback tone, and offer real-time adaptability and nuanced sentiment tagging.
Topic modeling identifies recurring themes in interview transcripts, streamlining analysis.
Automated thematic analysis extracts novel insights, while tools like Grain automate reporting, saving time and providing a concise overview of customer insights.