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Arriving at actionable user insights faster with AI-powered research

Last updated

9 January 2024

Author

Claire Bonneau

Reviewed by

Jean Kaluza


Chapters

1. Current state of AI adoption in user research
4. A 7-step research strategy framework
5. Using AI to conduct efficient user research
Overview
Creating research questions with less effort and bringing ideas to life soonerExpediting data analysis and predictive modeling

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

  • Creating an affinity map 

  • 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.


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