28 September 2021
We had a great discussion with Anna Steensig and Jökull Snæbjarnarson about how AI has been helping UX researchers do their work better and faster than ever before. In case you missed the discussion, here are the 3 biggest takeaways:
UX researchers face major challenges when processing data, a step that requires transcribing interviews, tagging quotes, clustering topics and other very repetitive and time-consuming tasks.
The next big challenge is related to sharing study results, where specialists must report to different stakeholders through different data formats, which also requires hours of data juggling and crafting presentations. In the past 3 years, however, AI and machine learning have seen massive improvements and provided new ways to overcome these challenges.
Especially when it comes to qualitative interviews, technologies like Speech-to-Text and Natural Language Understanding have enabled professionals to automate most of the repetitive tasks involved in the research process.
In simple terms, these technologies allow computers to understand texts and their contexts, and thanks to that, tasks such as transcribing interviews, translating participants’ speeches and sorting the data prior to analysis can now be done in a matter of seconds.
AI is not here to replace researchers, but to make their lives easier. Humans and machines have different strengths, and the more they work together, the better the results are.
The role of the researcher is still crucial for the research process, as they can best interpret data and draw conclusions from it. Smart tools like Sonar are around to simply facilitate this work by automating manual and repetitive tasks in a way to take the researcher one step closer to generating insights and sharing results consistently and clearly.