AI in user research 2026: complete playbook
How to integrate AI into every phase of user research in 2026: planning, recruitment, moderation, analysis, and delivery, with practical tool picks and guardrails.
Insights on expert networks, market research, UX research, and AI training from the CleverX team.
171 articles
How to integrate AI into every phase of user research in 2026: planning, recruitment, moderation, analysis, and delivery, with practical tool picks and guardrails.
AI tools can hallucinate during qualitative analysis, inventing insights that never existed. Here is what researchers need to watch for.
A practical guide for Research Ops teams using AI tools in studies, covering GDPR requirements, valid consent mechanics, and disclosure obligations.
AI synthesis tools turn raw transcripts, notes, and survey data into structured themes and findings faster than manual methods. Here is how to choose the right one.
AI wins on scale, speed, and analysis. Humans win on empathy, strategy, and ambiguity. Here is how to split the work across your research practice.
A ranked comparison of AI sentiment analysis tools for feedback, covering dedicated platforms, survey-native tools, and LLM-based options for every team size.
AI product teams need a different research stack. Here are the tools built for trust testing, hallucination research, and recruiting AI-literate participants at scale.
Practical ChatGPT prompt templates for every PM workflow: discovery research, PRD writing, roadmap planning, sprint prep, and stakeholder updates.
30+ copy-paste ChatGPT prompts for UX research across planning, recruiting, interviewing, analysis, and reporting, with guidance on what to trust and what to verify.
What happens inside an AI interview agent between when a participant types and when a follow-up question appears? Here is the full technical picture.
A clear-eyed look at what AI can and cannot do in research, and why the fear is mostly misplaced but not entirely wrong.
A practical guide to synthetic personas: how to build them with AI, the right and wrong ways to use them, and why they need grounding in real participant data.