Pertama Partners

AI User Research Synthesis and Insight Extraction

Use AI to synthesise user interviews, survey data, and usability tests into actionable insights, thematic patterns, and persona updates that inform product decisions across your organisation.

IntermediateAI Use-Case Playbooks2-3 weeks

Transformation

Before & After AI

What this workflow looks like before and after transformation

Before

Research teams spend 3-5 days manually coding interview transcripts and survey responses. Insights are trapped in individual researchers' notes with no systematic way to identify cross-study patterns. Persona documents are updated once a year at best, leaving product teams working from outdated user understanding. Stakeholder presentations take days to prepare from raw findings.

After

AI codes and themes research data in hours instead of days, surfacing patterns across multiple studies automatically. Persona documents update continuously as new research feeds into the system. Insight libraries are searchable and tagged, so product teams find relevant findings in minutes. Stakeholder recommendation decks are drafted from structured insights with supporting evidence.

Implementation

Step-by-Step Guide

Follow these steps to implement this AI workflow

1

Organise Raw Research Data

2-3 days

Collect and standardise all research inputs: interview transcripts, survey exports, usability test recordings and notes, support ticket themes, and analytics data. Remove personally identifiable information and create a consistent format AI can process.

Research Data Organisation Prompt
You are a user research operations specialist. Review the following raw research data from [RESEARCH METHOD] for [PRODUCT/FEATURE]. Standardise the format, tag each data point by source type, participant segment, and topic area. Flag any data quality issues or gaps.
Run this for each research source separately, then combine outputs. Always anonymise data before processing with AI tools.
2

Code Themes and Patterns

3-4 days

Apply thematic analysis to the organised research data. Use AI to identify recurring themes, cluster related observations, and quantify the strength of each theme by frequency and participant coverage. Build a codebook that can be reused across future studies.

Thematic Coding Prompt
You are a qualitative research analyst. Analyse the following organised research data for [PRODUCT/FEATURE]. Identify 8-12 recurring themes using inductive coding. For each theme, provide a definition, supporting quotes, participant coverage percentage, and sentiment breakdown.
Provide the full organised dataset from step 1. If you have themes from previous studies, include them to enable longitudinal comparison.
3

Extract Key Insights

2-3 days

Transform themes into actionable product insights. Each insight should connect user evidence to a product implication and recommended action. Prioritise insights by business impact and confidence level based on evidence strength.

Insight Extraction Prompt
You are a product research strategist. From the following thematic analysis for [PRODUCT/FEATURE], extract [NUMBER] actionable insights. Each insight must link user evidence to a product recommendation with confidence level and estimated business impact.
Include business context so the AI can prioritise insights relevant to current company goals. Share with product and design leads for validation before broader distribution.
4

Create Persona Updates

2-3 days

Use the new insights and themes to update existing user personas or create new ones. Ensure personas reflect current user behaviours, pain points, goals, and context. Add quotes and data points that make personas feel grounded in real research.

Persona Update Prompt
You are a UX research lead. Using the following research insights and themes for [PRODUCT], update the existing persona for [PERSONA NAME/SEGMENT]. Revise goals, pain points, behaviours, and context based on new evidence. Add representative quotes and data points.
Always start with the existing persona document to preserve institutional knowledge. Run separately for each persona segment you maintain.
5

Build Recommendation Deck

2-3 days

Compile insights, persona updates, and recommended actions into a stakeholder-ready presentation. Structure the deck to move from findings to implications to specific product recommendations with supporting evidence.

Recommendation Deck Outline Prompt
You are a research communications specialist. Create a stakeholder presentation outline from the following research insights for [PRODUCT/FEATURE]. Structure the deck to tell a compelling story from user evidence to product recommendations. Include slide-by-slide content suggestions.
Tailor the deck structure to your audience. For executive audiences, lead with recommendations. For product teams, lead with user evidence and pain points.

Get the detailed version - 2x more context, variable explanations, and follow-up prompts

Tools Required

AI language model for thematic analysis and insight generationSpreadsheet or research repository for data organisation and codingPresentation tool for stakeholder recommendation decksResearch database or knowledge management platform for insight storage

Expected Outcomes

Reduce research synthesis time from 3-5 days to under 1 day per study

Surface cross-study patterns that manual analysis typically misses

Keep persona documents current with continuous evidence-based updates

Cut stakeholder presentation preparation time by 60% with structured insight-to-deck workflow

Solutions

Related Pertama Partners Solutions

Services that can help you implement this workflow

AI Pilot Implementation

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AI Pilot Design Workshop

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Common Questions

No. AI accelerates the mechanical parts of coding, theming, and structuring findings, but human researchers bring contextual judgment, cultural sensitivity, and the ability to probe deeper during interviews. The best results come from researchers using AI as a power tool to handle volume while they focus on interpretation and strategic implications.

Always anonymise transcripts and data before processing with any AI tool. Replace names with participant IDs, remove company names, and strip any information that could identify individuals. If your organisation uses an enterprise AI platform with data processing agreements, that provides an additional layer of protection. Document your anonymisation process for ethics review.

This is a valuable signal. Sometimes AI surfaces patterns the researcher missed because it processes all data equally without recency or salience bias. Other times, the researcher caught contextual nuances that AI cannot detect from text alone. Use discrepancies as a prompt for deeper investigation rather than defaulting to either the AI or human interpretation.

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