EvalCommunity Tutorial

How to Use Dovetail AI for User Research and Stakeholder Feedback

A practical guide for using Dovetail AI to organize research data, analyze stakeholder feedback, identify themes, create insights, and share evidence-backed findings.

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Tutorial Summary

Dovetail is a customer intelligence and user research platform that helps teams centralize, analyze, and share customer data, research notes, interviews, feedback, and insights.

This tutorial explains how to use Dovetail AI for user research and stakeholder feedback analysis, including data preparation, project setup, AI-assisted summaries, highlights, tags, insights, source-linked evidence, and responsible AI use.

What You Will Learn

What Dovetail AI is and how it supports user research and feedback analysis.
How to prepare interviews, notes, recordings, documents, and stakeholder feedback.
How to organize a Dovetail project or research repository.
How to use AI-assisted summaries, search, chat, highlights, tags, and insights.
How to identify themes, patterns, risks, opportunities, and recurring feedback.
How to link findings back to source material.
How to validate AI-assisted outputs before using them in reports or decisions.

Authoritative Sources Used

This tutorial is based on Dovetail’s official website and documentation. Product features, AI capabilities, integrations, pricing, and security terms can change, so users should review Dovetail’s current documentation before using the tool with sensitive research or stakeholder data.

Dovetail AI User Research Workflow

Use this workflow to move from raw research and feedback data to evidence-backed insights.

Step 1

Define Questions

Clarify the research or feedback focus.

Step 2

Prepare Data

Clean, anonymize, and organize files.

Step 3

Upload Sources

Add interviews, notes, recordings, or feedback.

Step 4

Analyze

Use summaries, tags, highlights, and chat.

Step 5

Validate

Check findings against source material.

Step 6

Share Insights

Prepare evidence-backed outputs.

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1. What Is Dovetail AI?

Dovetail AI refers to Dovetail’s AI-supported features that help users summarize, analyze, search, tag, and generate insights from customer, user research, and stakeholder data.

Dovetail can support work with interviews, notes, recordings, transcripts, documents, feedback, customer calls, support tickets, and other customer or stakeholder data, depending on the workspace setup and available features.

Key Principle

Dovetail AI can accelerate research synthesis, but it does not replace user researchers, evaluators, or stakeholder engagement specialists. Human teams remain responsible for research design, interpretation, validation, ethics, and final decisions.

2. Why Use Dovetail AI?

User research and stakeholder feedback often create large volumes of qualitative data, including interview transcripts, usability testing notes, customer calls, open-ended survey responses, support tickets, workshop notes, and field observations.

Dovetail helps teams centralize this data, surface patterns, create summaries, build insights, and share evidence-backed findings. This can help teams move faster from raw feedback to decisions while keeping findings connected to source material.

3. Where Dovetail AI Fits in the Research Workflow

Research Phase	How Dovetail AI Can Support	Human Responsibility
Planning	Organize objectives, questions, and feedback sources.	Define research design, sampling, and decision needs.
Data preparation	Import interviews, notes, recordings, documents, and feedback.	Check consent, confidentiality, metadata, and data quality.
Analysis	Surface summaries, tags, highlights, trends, and signals.	Validate themes and interpret meaning in context.
Insight generation	Create insights linked to evidence and source material.	Check source support and avoid overclaiming.
Reporting	Share summaries, docs, charts, or insight pages.	Verify accuracy, sensitivity, and actionability.

4. Before You Start: Prepare Research and Feedback Data

Data You May Use

User interview transcripts
Usability testing recordings
Stakeholder consultation notes
Customer call transcripts
Customer support tickets
Open-ended survey responses
Product feedback
Workshop notes
Field notes
Evaluation interviews

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Preparation Checklist

Clarify the research or feedback question.
Confirm consent for digital or AI-assisted processing.
Remove unnecessary personal identifiers.
Check confidentiality and data protection requirements.
Clean transcript formatting.
Label speakers where useful.
Organize files by project, topic, product area, or stakeholder group.
Prepare metadata such as segment, role, location, or feedback channel.

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Data Protection Note

Do not upload confidential, sensitive, or personally identifiable stakeholder data unless consent, organizational policy, client agreement, and applicable data protection rules allow it.

5. Set Up a Dovetail Project or Repository

Open Dovetail and create a new project, workspace, or research area.
Name the project clearly, for example: Stakeholder Feedback Analysis — Climate Adaptation Programme.
Add the research objective or feedback question.
Upload interviews, notes, recordings, documents, or feedback files.
Organize materials by source type, research activity, stakeholder group, or product area.
Add metadata such as participant type, customer segment, organization type, country, location, product area, or feedback channel.
Check that files were uploaded and processed correctly.
Review automatic transcripts, summaries, highlights, or tags where available.

6. Organize Tags, Fields, and Metadata

Tags, fields, and metadata help researchers compare evidence across participant groups, customer segments, regions, product areas, feedback channels, or stakeholder categories.

Useful Metadata Fields

Participant type
Customer segment
Stakeholder group
Country or region
Product area
Feature tested
Feedback channel
Interview round
Organization type
Priority level

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Example Questions

Do new and experienced users describe different onboarding barriers?
Do stakeholders in different regions identify different implementation challenges?
Are negative comments concentrated around one feature?
Which customer segment reports the strongest unmet need?
Do programme staff and beneficiaries describe the same risks?

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7. Start with Human Familiarization

Before relying on AI summaries or AI-generated insights, researchers should manually review a sample of the source material. This helps preserve context, tone, emotion, contradiction, and nuance.

Read at least two transcripts or notes manually.
Review one audio or video recording if relevant.
Read a sample of survey responses or support tickets.
Write a short familiarization memo.
Note early patterns and surprising findings.
Identify sensitive content or ethical risks.
Define what must be checked during AI-assisted analysis.

8. Use Dovetail AI for Summaries

Dovetail AI can support summaries that help researchers quickly understand interviews, calls, feedback items, or research notes. These summaries should be treated as first-pass outputs and checked against source material.

Select a transcript, note, call, or feedback item.
Review the AI-assisted summary where available.
Compare the summary with the original source.
Check whether important details were omitted.
Check whether the summary overstates consensus.
Add human notes to preserve context.
Mark outputs as AI-assisted where appropriate.

9. Ask Questions Across Customer or Stakeholder Data

Dovetail AI can help users ask questions across customer or stakeholder data. This can support faster synthesis when feedback is spread across interviews, tickets, notes, and research sessions.

Example Questions to Ask

What are the most common onboarding barriers?
What do users say about trust and confidence?
Which product features cause the most confusion?
What pain points appear repeatedly in support feedback?
What do stakeholders identify as implementation risks?
How do different segments describe the same issue?

10. Create Highlights, Tags, and Insights

Dovetail supports research synthesis by helping users highlight key moments, tag data, and create insights that connect findings back to evidence. Tags organize evidence, while insights explain why that evidence matters.

Review source material.
Highlight important quotes, moments, or feedback items.
Apply tags to organize evidence.
Review AI-suggested tags or highlights where available.
Merge duplicate tags.
Rename vague tags into research-relevant language.
Group related highlights.
Create an insight based on source evidence.
Link the insight back to supporting highlights.
Add researcher interpretation and implications.

Quality Warning

A tag is not the same as an insight. A tag organizes evidence. An insight explains what the evidence means and why it matters for a product, programme, service, or decision.

11. Identify Themes, Patterns, Trends, and Signals

Dovetail AI can help surface patterns, trends, themes, and signals across a large research or feedback dataset. These outputs should be treated as analytical suggestions, not final conclusions.

Start with one research or stakeholder feedback question.
Ask Dovetail to surface relevant themes or signals.
Review the evidence linked to each theme.
Check whether the theme is too broad or generic.
Compare patterns across metadata groups.
Identify contradictory or minority views.
Refine theme names.
Add human interpretation.
Create a final insight summary.
Share only validated findings.

12. Use Dovetail for Stakeholder Feedback Analysis

Task	Dovetail Use	Human Validation
Stakeholder consultation	Identify concerns, priorities, risks, and recommendations.	Check political sensitivity and source context.
Beneficiary feedback	Surface themes and repeated barriers.	Check representativeness and inclusion.
User research synthesis	Identify pain points and product opportunities.	Review usability context and source evidence.
Learning review	Extract lessons and action points.	Validate with project teams.
Donor reporting	Prepare source-linked insight summaries.	Verify claims and limitations.

13. Share Insights with Stakeholders

Dovetail can help teams share research outputs with product teams, leadership, programme teams, donors, or stakeholders. Shared insights should be clear, evidence-backed, and appropriate for the audience.

Possible Outputs

Insight pages
Research summaries
Charts and evidence summaries
Evidence-backed findings
Customer voice summaries
Stakeholder feedback briefs
Learning notes
Product opportunity summaries
Decision-support memos

14. Validate AI-Assisted Insights

Validation Checklist

Is the insight supported by source material?
Can the finding be traced back to transcript, note, ticket, or feedback data?
Are highlights representative?
Are minority or contradictory views included?
Does the insight overstate the evidence?
Are sensitive details removed?

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Validation Methods

Source checking
Peer review
Team sense-making
Negative case review
Comparison across metadata groups
Triangulation with product metrics, survey data, or monitoring data

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15. Responsible AI Use in Dovetail

Did participants consent to AI-assisted processing?
Was personal or sensitive data removed or protected?
Were AI-generated outputs reviewed?
Were source links checked?
Were marginalized voices considered?
Were contradictions included?
Were stakeholder quotes used ethically?
Was AI use disclosed where appropriate?
Were final insights written by human researchers or evaluators?

16. AI Use Statement

Dovetail AI was used to support selected stages of user research and stakeholder feedback analysis, including transcript review, AI-assisted summaries, tagging support, theme exploration, evidence organization, and insight development. All AI-generated outputs were reviewed, revised, accepted, or rejected by the research team. Final insights, interpretations, and recommendations were developed by human researchers and checked against source evidence. AI-generated outputs were not treated as standalone evidence.

17. Practical Exercise for Learners

Exercise: Analyze User Research and Stakeholder Feedback with Dovetail AI

Use three user interview transcripts, one stakeholder consultation transcript, ten open-ended survey responses, one research objective, and three metadata fields.

Create a Dovetail project.
Upload transcripts, notes, or feedback responses.
Add metadata where available.
Review one transcript manually.
Review an AI-assisted summary.
Ask one focused research question across the dataset.
Highlight key moments.
Apply or review tags.
Create one insight linked to evidence.
Compare findings across metadata groups.
Check source evidence for each insight.
Draft a short stakeholder feedback summary.
Validate the summary against source material.
Write one recommendation.
Draft an AI use statement.

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18. Frequently Asked Questions

What is Dovetail AI?

Dovetail AI refers to Dovetail’s AI-supported features that help users summarize, analyze, search, tag, and generate insights from customer, research, and stakeholder data.

Can Dovetail AI replace user researchers?

No. Dovetail AI can support analysis and synthesis, but human researchers remain responsible for research design, interpretation, validation, ethics, and final insights.

What types of data can Dovetail support?

Dovetail can support user interviews, research notes, recordings, transcripts, documents, feedback, customer calls, support tickets, and other customer or stakeholder data depending on the workspace setup and available integrations.

Can Dovetail be used for stakeholder feedback?

Yes. Dovetail can be used to analyze stakeholder interviews, consultation notes, open-ended survey responses, workshop feedback, beneficiary feedback, and recurring issues across feedback channels.

Should AI-generated insights be used directly in reports?

No. AI-generated outputs should be reviewed, validated, and revised by human researchers before being used in reports, product decisions, evaluation findings, or stakeholder communication.

19. Final Quality Checklist

Consent and privacy requirements were reviewed.
Sensitive data was protected.
The project or repository was organized clearly.
Tags, fields, or metadata were added where useful.
Human familiarization happened before AI-assisted interpretation.
AI-generated summaries were checked against sources.
Insights were linked to source material.
Themes were supported by evidence.
Minority and contradictory views were considered.
Stakeholder quotes were used ethically.
Recommendations followed from the data.
AI use was documented transparently.
Final interpretation was completed by human researchers.

Conclusion

Dovetail AI can help research, product, evaluation, and stakeholder engagement teams move faster from raw feedback to structured insights. It can support summaries, tagging, themes, trends, search, insights, charts, docs, and evidence-backed sharing.

However, Dovetail AI should be used as a support tool, not as a replacement for research judgment. Strong user research and stakeholder feedback analysis still require human interpretation, ethical safeguards, source verification, and careful communication.