[Demo] Stress-testing GenAI in user research synthesis
Summary
In this high-pressure scenario, the challenge was to conduct 17 user interviews in three days and synthesize a comprehensive report in just one additional day. I’ll explore how we used AI to streamline the research process, from transcription to synthesis, and how tools like ChatGPT contributed to efficient data processing and insight generation. We’ll reflect on the potential and pitfalls of using AI in accelerated user research, from practical aspects to more philosophical considerations on potential changes to the research process. Takeaways Practical insights into integrating AI with traditional research methodologies to expedite the research process An overview of the effectiveness of AI transcription and synthesis tools in real-world research scenarios Critical examination of AI's role in data processing and how it compares with human analysis Strategic considerations for service designers when employing AI to support rapid user research Reflection on the ethical implications and potential impact on the quality of insights and researcher well-being when relying on AI to speed up research processes
Key Insights
-
•
Generative AI speeds textual content generation and navigation but needs human guidance for meaningful interpretation.
-
•
AI struggles to track and assign specific interviewees to user archetypes without explicit human instruction.
-
•
A semi-automated workflow combining AI brainstorming and human refinement delivers faster synthesis under tight deadlines.
-
•
ChatGPT excels at brainstorming clustering parameters and generating detailed archetype descriptions when properly guided.
-
•
AI can retrieve relevant interview quotes but often requires multiple prompt iterations to produce longer, contextually fitting excerpts.
-
•
Visual generation of archetypes using DALL·E currently yields poor results and often contradicts instructions.
-
•
Token limits can constrain transcript processing, but newer models like GPT-4 improve handling of larger inputs.
-
•
AI behaves like an 'alien intern'—powerful but unaware of design research processes and simple context details unless taught.
-
•
Using AI as a creative partner helps overcome cognitive overload, especially in rapid, solo research scenarios.
-
•
Less experienced researchers require caution when using AI tools, as expert evaluation is crucial for output quality.
Notable Quotes
"Gene AI can really help to speed up synthesis moments connected to textual content generation and navigation."
"AI behaves like an alien intern because it’s not capable of making sense of some things easy for us."
"I approached the GPT as if it were my human colleague, asking very open questions."
"It couldn’t find specific interviewees for user archetypes, so I had to adapt and guide the process."
"This is a hero moment because it accelerates and simplifies exploration of clustering options."
"Text generation is the biggest superpower of ChatGPT in research synthesis right now."
"Visuals from DALL·E didn’t work well; I asked multiple times to remove cables but they still appeared."
"AI can help manage cognitive overload and speed up work when you have short timeframes and no team."
"You need a lot of annoying handholding with the AI: reminding it of how many people you have or simple things."
"I wouldn’t leave AI tools to juniors because it’s crucial to have your own experience to evaluate outputs."
Or choose a question:
More Videos
"People on the project were reporting back to management in their silo, not talking to one another."
Carl TurnerYou Can Do This: Understand and Solve Organizational Problems to Jumpstart a Dead Project
March 28, 2023
"If you have somebody on your team for three years, for five years, that’s amazing from a longevity perspective given enterprise legacy and complexity."
Karen PascoeDeveloping Experience Teams and Talent in the Enterprise
June 8, 2016
"Sending small care packages to remote team members goes a long way in showing we care and building connection."
Jilanna WilsonDistributed Design Operations Management
October 23, 2019
"A government designer aims to reduce UX variation while a contractor may optimize only their single product, creating potential conflicts."
Shawna Hein Kevin HoffmanCreate a Cohesive Civic Design Practice Across Agency, Vendors, and Contracts
November 17, 2022
"You need to find your own mattress—a grounding purpose or phrase—that you can rely on when progress feels hopeless."
Barb SpantonDoing Work That Matters: A Look Beyond The Idealistic Notion of 'Doing Meaningful Work'
June 10, 2022
"We had no recipe, no formula, just vibes."
Briana ThomasThe Quiet Force: Uncovering Hidden Leadership in High-Impact Design Teams
September 24, 2024
"In large complex systems, the dots are scattered across silos and time zones, making progress feel elusive."
Gina MendoliaTherapists, Coaches, and Grandmas: Techniques for Service Design in Complex Systems
December 3, 2024
"We need to become data ethicists and curators, not just data vacuum cleaners, to prioritize quality data."
Lija Hogan Milan Mijatovic Sam Proulx Louis RosenfeldThree Years Out: Perspectives on the Near-Term Future of User Research
March 15, 2024
"With our new operating model, partners are more confident funding UX ops because of increased clarity and focus."
John Calhoun Rachel PosmanTwo Sides of the DesignOps Coin: Teams Ops and Product Ops
January 8, 2024
