⚡ Supercharging Design Research with AI
⚡ Supercharging Design Research
with AI
05 Aug 2025 • 6-7 min read
Introduction
Introduction
Modern product teams face a challenge. Stakeholders want faster delivery and data-driven decisions, but thorough research takes time. This creates tension between doing good research and shipping quickly.
Most senior product designers spend 60% to 70% of their time on research, conducting interviews, analyzing data, and creating reports. While this research is essential, it leaves little time for actual design work.
What if you could keep research quality high while cutting down on the time it takes? AI agents can now handle many research tasks that used to require hours of manual work. This is not about replacing human insight. It is about allowing designers to focus on strategy and creative problem-solving instead of data processing.
Modern product teams face a challenge. Stakeholders want faster delivery and data-driven decisions, but thorough research takes time. This creates tension between doing good research and shipping quickly.
Most senior product designers spend 60% to 70% of their time on research, conducting interviews, analyzing data, and creating reports. While this research is essential, it leaves little time for actual design work.
What if you could keep research quality high while cutting down on the time it takes? AI agents can now handle many research tasks that used to require hours of manual work. This is not about replacing human insight. It is about allowing designers to focus on strategy and creative problem-solving instead of data processing.
The Challenge: Research Bottlenecks in Design
The Challenge: Research Bottlenecks in Design
After completing a round of user interviews, the real challenge begins: turning hours of conversations into clear, actionable insights. It’s a process that requires both detail-oriented focus and big-picture thinking and it can be surprisingly draining.
Transcription – Before you can analyze, you need an accurate record of each interview. This often means either paying for a transcription service or painstakingly replaying recordings to capture every word pauses, tangents, and all.
Synthesis – With transcripts in hand, the goal is to identify patterns, recurring pain points, and unexpected insights. This involves combing through quotes, highlighting key observations, and connecting dots across multiple interviews.
Actionable Items – The final step is translating findings into specific recommendations. Stakeholders need clarity: what should be built, improved, or prioritized based on what users shared?
While synthesis is where research delivers its greatest value, it’s also the most time-consuming stage. Much of this process can and should be streamlined, freeing researchers to focus less on manual labor and more on making confident, evidence-based decisions.
After completing a round of user interviews, the real challenge begins: turning hours of conversations into clear, actionable insights. It’s a process that requires both detail-oriented focus and big-picture thinking and it can be surprisingly draining.
Transcription – Before you can analyze, you need an accurate record of each interview. This often means either paying for a transcription service or painstakingly replaying recordings to capture every word pauses, tangents, and all.
Synthesis – With transcripts in hand, the goal is to identify patterns, recurring pain points, and unexpected insights. This involves combing through quotes, highlighting key observations, and connecting dots across multiple interviews.
Actionable Items – The final step is translating findings into specific recommendations. Stakeholders need clarity: what should be built, improved, or prioritized based on what users shared?
While synthesis is where research delivers its greatest value, it’s also the most time-consuming stage. Much of this process can and should be streamlined, freeing researchers to focus less on manual labor and more on making confident, evidence-based decisions.
Setting Up My AI Research Agent
Setting Up My AI Research Agent
I first heard about Genway.ai on the dive.club podcast by ridd.design. I was hesitant to test it with work data, so I used my side project, gaaclubfinder.com, as a safe space to experiment.
What stood out was that Genway.ai did not just transcribe interviews, but It conducted them, asking smart follow-up questions. This helped uncover the deeper “why” behind user behavior, which was exactly what I needed. It required no coding, which suited me as a designer, and I could evaluate data practices before using it professionally.
Setup was quick. I defined my research goal, outlined key themes, chose a conversational tone, and configured follow-up logic based on user responses. I also set clear quality standards: detailed answers, recognizable patterns, actionable insights, and minimal bias.
Best of all, it fit right into my workflow. I still recruited participants manually. The big test was whether AI-led interviews could match real conversations. They came surprisingly close.
I first heard about Genway.ai on the dive.club podcast by ridd.design. I was hesitant to test it with work data, so I used my side project, gaaclubfinder.com, as a safe space to experiment.
What stood out was that Genway.ai did not just transcribe interviews, but It conducted them, asking smart follow-up questions. This helped uncover the deeper “why” behind user behavior, which was exactly what I needed. It required no coding, which suited me as a designer, and I could evaluate data practices before using it professionally.
Setup was quick. I defined my research goal, outlined key themes, chose a conversational tone, and configured follow-up logic based on user responses. I also set clear quality standards: detailed answers, recognizable patterns, actionable insights, and minimal bias.
Best of all, it fit right into my workflow. I still recruited participants manually. The big test was whether AI-led interviews could match real conversations. They came surprisingly close.
The Five Questions Framework
The Five Questions Framework
The Experiment: What I Delegated
The Experiment: What I Delegated
The Five Questions Framework: Giving the AI What It Needs
To get useful results from the AI, I gave it a simple five-question brief for every project:
What is the goal? I explained the background, existing insights, and what we still needed to learn.
What topics are we exploring? I listed assumptions, knowledge gaps, and research questions.
What does success look like? I defined clear outcomes, types of insights needed, and how they would be used.
Who are we talking to? I described target participants so the AI could tailor conversations.
Any extra guidelines? I added notes on tone, focus areas, or priority questions.
Why It Matters
The more context I gave upfront, the better the AI performed. It asked sharper questions, found deeper insights, and stayed aligned with project goals. Without this, results felt generic and surface level.
The Five Questions Framework: Giving the AI What It Needs
To get useful results from the AI, I gave it a simple five-question brief for every project:
What is the goal? I explained the background, existing insights, and what we still needed to learn.
What topics are we exploring? I listed assumptions, knowledge gaps, and research questions.
What does success look like? I defined clear outcomes, types of insights needed, and how they would be used.
Who are we talking to? I described target participants so the AI could tailor conversations.
Any extra guidelines? I added notes on tone, focus areas, or priority questions.
Why It Matters
The more context I gave upfront, the better the AI performed. It asked sharper questions, found deeper insights, and stayed aligned with project goals. Without this, results felt generic and surface level.
Demo of the AI Agent Interview
Demo of the AI Agent Interview
Results & Lessons Learned
Results & Lessons Learned
Time Savings & Efficiency
AI-led interviews removed scheduling headaches by allowing participants to join at their convenience, making it ideal for global feedback. Insights were automatically grouped into themes with direct transcript links, and a structured tagging system improved consistency while saving hours. The ability to query findings conversationally felt like a long-awaited research superpower.
Insight Quality
The AI often surfaced patterns I might have missed manually. Some participants became more candid than in traditional interviews, suggesting AI can create a comfortable environment for sharing. Intelligent follow-up questions dug deeper into important areas, though occasional misses reminded me that human oversight is still important.
Time Savings & Efficiency
AI-led interviews removed scheduling headaches by allowing participants to join at their convenience, making it ideal for global feedback. Insights were automatically grouped into themes with direct transcript links, and a structured tagging system improved consistency while saving hours. The ability to query findings conversationally felt like a long-awaited research superpower.
Insight Quality
The AI often surfaced patterns I might have missed manually. Some participants became more candid than in traditional interviews, suggesting AI can create a comfortable environment for sharing. Intelligent follow-up questions dug deeper into important areas, though occasional misses reminded me that human oversight is still important.
Project Insights
Project Insights
Challenges & Limitations
Clear, specific briefs were critical vague input produced shallow results. Delays between questions could disrupt flow, and the animated avatar distracted some participants (one even compared it to the “Eye of Sauron”). Video recordings were limited to enterprise plans, and usability testing support was restricted to Figma links, limiting flexibility.
Impact on My Role
The AI shifted my role from conducting every interview to designing the research framework and monitoring quality. This freed me to focus on interpreting findings and planning next steps, though handing over full control still felt risky when key follow-ups were missed.
Opportunities for Improvement
I’d like to see features that humanize the AI experience such as short researcher video intros and expanded support for more complex research topics and diverse prototype formats. These changes would make AI-led interviews even more effective for a wider range of projects.
Challenges & Limitations
Clear, specific briefs were critical vague input produced shallow results. Delays between questions could disrupt flow, and the animated avatar distracted some participants (one even compared it to the “Eye of Sauron”). Video recordings were limited to enterprise plans, and usability testing support was restricted to Figma links, limiting flexibility.
Impact on My Role
The AI shifted my role from conducting every interview to designing the research framework and monitoring quality. This freed me to focus on interpreting findings and planning next steps, though handing over full control still felt risky when key follow-ups were missed.
Opportunities for Improvement
I’d like to see features that humanize the AI experience such as short researcher video intros and expanded support for more complex research topics and diverse prototype formats. These changes would make AI-led interviews even more effective for a wider range of projects.
AI Agent Interview Responses
AI Agent Interview Responses
Conclusions
Conclusions
AI-assisted research is reshaping design work making it more flexible, scalable, and deeply integrated into product development. Soon, these tools will handle more complex tasks like usability testing, deeper interviews, and real-time pain point detection, with broader integrations beyond Figma.
To get the most value, designers must learn to structure research clearly, craft precise prompts, and define success metrics. The human role is shifting from doing every task to guiding the process, interpreting insights, and making key decisions areas where judgment and empathy still matter most.
Start small, give AI clear direction, and use it to scale research rather than shortcut it.
My own work has shifted toward designing research processes instead of performing every step, freeing time for strategic thinking. With the right setup, AI can uncover patterns faster, cut manual work, and open new opportunities. The future of design research is here and it’s ready for you to explore.
AI-assisted research is reshaping design work making it more flexible, scalable, and deeply integrated into product development. Soon, these tools will handle more complex tasks like usability testing, deeper interviews, and real-time pain point detection, with broader integrations beyond Figma.
To get the most value, designers must learn to structure research clearly, craft precise prompts, and define success metrics. The human role is shifting from doing every task to guiding the process, interpreting insights, and making key decisions areas where judgment and empathy still matter most.
Start small, give AI clear direction, and use it to scale research rather than shortcut it.
My own work has shifted toward designing research processes instead of performing every step, freeing time for strategic thinking. With the right setup, AI can uncover patterns faster, cut manual work, and open new opportunities. The future of design research is here and it’s ready for you to explore.
AI Agent Auto Tags
AI Agent Auto Tags