Nielsen Norman Group (NNG) has conducted and continues to conduct extensive research testing various large language model (LLM) tools designed for research synthesis and analysis. Our goal was to determine whether these AI-powered tools could meaningfully accelerate the work of experienced UX researchers. Through rigorous testing across multiple models and specialized research tools, we’ve found that while a few tools provide modest speed improvements for experienced researchers, none come close to replacing human expertise in research synthesis and analysis.

The core problem is that these tools consistently exhibit critical flaws: they hallucinate findings, fail to identify meaningful patterns in qualitative data, cannot adequately consider nuanced research questions, and produce only superficial, high-level summaries of participant behavior. What makes this particularly dangerous is that these AI-generated outputs often have the veneer of legitimate research results—they look professional and sound plausible. However, closer inspection reveals significant gaps, inaccuracies, and missed insights that would mislead stakeholders and result in poor design decisions. The appearance of competence masks fundamental limitations that make these tools unreliable for serious research work.

While we’ve found several places in the research process that can benefit from LLM usage, analysis and synthesis consistently falls short. In this talk, I can share the specific research we’re doing and explain what actually works.