Guide

AI in Research Analysis :
From Raw Data to Trusted Insights

AI is changing how user research analysis gets done, but not what makes it good. This guide shows you where AI helps, where it doesn't, and how to use it without losing rigor.

Use AI Where It Helps And Stay in Control Where It Counts

Speed pressure is real, and AI can take a lot of the mechanical work off your plate. But the parts that make analysis trustworthy, like judgment, context, and interpretation, still depend on you. This guide is built around that distinction. It helps you:

  • Identify which analysis tasks AI can reliably support, and which need to stay human

  • Set up a workflow that's faster without becoming less reliable

  • Evaluate AI research tools against what they actually deliver

„Focusing on scoped, well-defined tasks that can be easily verified is what allows teams to get genuine value from AI today“

CEO and Co-Founder at Condens

What’s Inside?

This guide draws on a survey of 330+ UX researchers and practitioners, plus input from experts and a real-world case study. It's organized around the practical questions you're likely asking right now.
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Chapter 1

What Good Analysis Looks Like, With or Without AI

  • The eight qualities that define trustworthy research analysis

  • Minimum Viable Rigor (MVR) and how to apply it to your projects

  • Why human judgment becomes more important, not less, as AI gets faster

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Chapter 2

The Current State of AI in Research Analysis

  • What 330+ practitioners told us about how they're using AI today

  • Where AI is delivering on speed, and where trust still falls short

  • How adoption, satisfaction, and pain points differ by role

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Chapter 3

Opportunities and Risks of AI in Analysis

  • A risk-ranked look at common AI features, from transcription to report generation

  • A clear set of do's and don'ts from research experts

  • A practical framework for evaluating AI research tools

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Chapter 4

A Practical Workflow for AI-Assisted Analysis

  • A stage-by-stage walkthrough of a human-in-the-loop workflow

  • How to ask better questions and get better outputs from AI

  • How to maintain rigor, traceability, and reproducibility

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Bonus

A Real-World Case Study

  • How a UX researcher uses AI to save 3 days per study

  • What he automates, what he keeps manual, and why

  • His advice for anyone integrating AI into their workflow

What You’ll Take Away from This Guide

Walk away with a clearer view of where AI fits in your analysis workflow and how to use it without compromising what makes your work credible.
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Know What to Automate (and What Not To)

A practical breakdown of which analysis tasks AI handles well, which it doesn't, and how to tell the difference in your own projects.
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Build a Workflow You Can Trust

A stage-by-stage walkthrough of AI-assisted analysis that keeps rigor, traceability, and human judgment in place.
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Evaluate AI Tools With Confidence

A clear framework and worksheet for comparing AI research tools, and an overview of the safeguards that separate trustworthy results from ones that just sound convincing.

Get the Full Picture on AI in Research Analysis

Fill out the form to download the full guide. Practical, evidence-based, and grounded in what 330+ researchers, designers, and PMs actually told us.