Executive Summary

Research teams spend enormous amounts of time organizing and synthesizing data.

I designed an AI research copilot capable of transforming interviews, surveys, analytics, and support tickets into actionable product insights.

The Problem

Research teams spend:

• 20% collecting data

• 80% organizing data

Pain points:

• Manual transcription

• Affinity mapping

• Theme extraction

• Report creation

Research

Participants:

• 14 UX researchers

• 9 product managers

• 3 designers

AI Opportunity

The assistant would:

• Analyze interviews

• Detect themes

• Create journey maps

• Identify opportunities

• Generate reports

Workflow

Users upload:

• Zoom transcripts

• Surveys

• Analytics

• Support tickets

The AI identifies:

Theme #1: Trust concerns

Frequency: 82%

Sentiment: Negative

Representative quote: "I don't understand what happens next."

Opportunity Mapping:

The assistant clusters findings into:

• Pain points

• User needs

• Opportunities

• Potential features

Insight Generation:

AI proposes:

"Users are not struggling with the workflow itself. They are struggling with uncertainty during transitions."

Insight Generation:

Researchers can:

• Approve

• Reject

• Edit

• Recluster

Outcomes

KEY METRICS:

Synthesis time: -80%

Research throughput: +300%

Stakeholder engagement: +45%

Time to insight: -70%