TL;DR: if you know exactly what you are looking for, AI tools are a great method to validate it, but they cannot replace actual communication with users. UX research is not just A/B test or a survey, there is more than that. It is mostly about firsthand information from real (potential) users and learn how to listen to them. I have seen several examples of a UX research leading to a new solution or perspective. So AI tools are great to validate the outcome, but not a replacement for the real process.

If your data collection process feels overwhelming, it may be worth asking yourself if you are trying to collect too much data, and have you clearly defined the purpose of what you are collecting the data for? I enjoy iterative data collection that is based on grounded theory research methods where you start with a question, collec data to find the scope and scale of the answer and then keep collecting data until you have a clear view of the issues or outcomes you're trying to understand. This automatically scales the data collection process to a manageable level and helps you narrow in on what matters most to get the best results.

AI tools can really help lighten the load. I use AI to analyze large datasets quickly, spotting trends and patterns that might take longer to identify manually.

When the UX research process feels overwhelming, I use AI tools to simplify and speed up key tasks: Automating Surveys & Analysis User Behavior Insights Transcription & Sentiment Analysis: I use AI tools like Otter.ai or Dovetail to transcribe user interviews and detect emotional cues, cutting transcription time by 80%. Recruiting Participants Rapid Data Visualization: Platforms like Tableau or Looker use AI to generate instant, clear visualizations of complex data, making insights easier to communicate. AI helps me focus on interpreting findings and making decisions, rather than getting bogged down in tedious data collection.

Integrating AI into UX research involves automating data collection through tools that handle surveys and data entry, using AI analytics to swiftly identify trends in user behavior, and employing AI simulations to enhance user testing. These methods streamline the research process, allowing for deeper insights with less manual effort. Sharing experiences with specific AI tools can further enrich understanding and application in UX research.