A/B testing is a core part of UX research, helping teams compare versions of a product to see which performs better. Traditional methods can be slow and costly. AI tools like Neurons simplify the process by predicting attention and engagement, reducing guesswork and speeding up decision-making.

Another strong option is UserTesting, which uses AI to analyze video, text, and behavioral data. It summarizes key moments, detects friction points, and runs sentiment analysis to understand user emotions. It also identifies common themes from large volumes of open-ended survey responses.

Using AI for user testing offers several benefits:

• Faster insights without the high costs
• Iterative design through quick feedback and testing cycles
• Scalability for both small projects and large-scale campaigns

However, not all research benefits equally. In-depth methods like ethnographic studies rely on human observation, nuance, and context that AI tools still struggle to interpret. Always pair AI insights with expert review for a well-rounded perspective.