AI Workflow for Designers: Research Done Faster, Step by Step
An AI workflow for designers research: run studies in Maze, centralise evidence in Dovetail, and use cited AI answers and themes to speed up synthesis.
By Galactic News Editorial · Published 2026-11-07T19:31:54+00:00 · Updated 2026-11-07T19:31:54+00:00
AI Workflow for Designers: Research Done Faster, Step by Step
An AI workflow for designers research cuts the slowest parts of a study: transcription, tagging and first-pass synthesis. You still design the study and judge the findings. AI handles the hours between the last session and the first insight. This guide chains two tools together. Maze runs tests and interviews. Dovetail stores the evidence and answers questions about it. Each step below names who does what.
Where an AI workflow for designers research saves time
Manual synthesis scales badly. Ten interviews can mean ten hours of video. Tagging alone can eat a full day. AI tools compress three specific stages. Transcription becomes automatic and searchable. Theme clustering gives you a starting map of the data. Cited answers let stakeholders query past studies themselves. None of this replaces analysis. It removes the clerical work around analysis. Your expertise moves to the questions and the conclusions.
Step by step: the ai workflow for designers research
- Plan the study in Maze. Build the test or interview guide in one place. Maze supports prototype tests, surveys and moderated or unmoderated interviews. Connect your Figma prototype directly. Maze integrates with Figma, so the tested file is the design file.
- Let AI help run the sessions. Maze's AI moderator can run interviews with dynamic follow-up questions. Automatic transcription captures every session. For unmoderated studies, plan enough responses. Maze notes that AI summaries and themes need at least ten open-response answers.
- Generate the first synthesis in Maze. Run the AI summaries and themes on the results. Treat the output as a draft map. Check each theme against real participant quotes. Delete themes the quotes do not support.
- Centralise the evidence in Dovetail. Dovetail brings interviews, sales calls, support tickets, NPS and reviews into one hub. New feedback is classified as it arrives. Your study now sits beside every other customer signal.
- Ask questions, get cited answers. Dovetail's AI chat answers questions about stored research. Answers link back to the underlying evidence. Stakeholders can self-serve simple questions. You keep the hard ones.
- Write the readout with AI Docs. Dovetail can generate reports and summaries from selected material. Every claim in the draft should trace to a source clip. Edit the draft into your readout. Ship it with the clips attached.
Two tools, two different jobs
| Tool | Best at | AI features used here |
|---|---|---|
| Maze | Running tests and interviews | AI moderator, transcription, summaries, themes |
| Dovetail | Storing and querying all evidence | AI chat with citations, AI Docs, channel classification |
Maze is the collection engine. Dovetail is the memory. Splitting the jobs this way keeps studies fast and findings reusable. Next quarter's question may already sit in this quarter's data.
Quality checks before you trust the output
- Read at least two full transcripts yourself.
- Open the source clip behind every headline theme.
- Check AI themes against your research questions.
- Note where AI grouped unlike comments together.
- Keep a human-written summary of surprises.
One caution comes from Dovetail itself. Its AI terms say output is provided as-is. Dovetail states it has not verified the accuracy of AI output. So verify before you present. This workflow guide is research-based, not firsthand testing.
Bottom line
Run the study in Maze. Keep the evidence in Dovetail. Let AI draft the synthesis, then audit it like a sceptic. Designers who work this way report findings days sooner. The findings also stay findable long after the project ends.
Setting up the workflow this week
You do not need a big migration to start. Pick one live study and run it through both tools. Recruit and test in Maze as usual. Export or sync the recordings into Dovetail the same day. Tag nothing by hand on the first pass. Let the AI themes form the first draft instead. Then hold a thirty-minute review with your team. Compare the AI themes against your own session notes. Note every theme the AI missed or merged wrongly. Those notes become your prompt and process fixes. Repeat the loop on the next study. By the third study, the routine runs itself. Your repository also starts compounding in value. Each new study makes the old evidence easier to find. Stakeholders begin answering simple questions without booking your time. That is the real return on the setup effort.