From generic to contextual research insights with AI | Live Q&A
Summary
Built on insights from hundreds of interviews with product researchers at global companies, Survicate’s new Research Projects feature is designed to work with full research hypothesis context and real-world workflows.
We’ll run a research project live using real data and sources, then open the floor for honest feedback, questions, and discussion around your hesitations and challenges with AI in research.
All participants will receive early access to the product, and help shape the final direction of this next-gen Research Repository!
Key Insights
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Fully autonomous AI synthesis of customer feedback can produce low-quality results and lacks user control.
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Human-in-the-loop involvement remains crucial for trustworthy, actionable research insights.
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Using AI-powered chat-based prototyping tools like Cloud Code accelerates design iterations and enables instant user testing.
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Allowing users to explore realistic, interactive prototypes freely yields richer, contextual feedback over fixed scenarios.
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Project-based research in products empowers users to select data sources and tailor AI synthesis focus.
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Integrating AI to generate company-specific context improves relevance and quality of automated analysis.
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The traditional research, design, and product management process must adapt holistically, not in isolation, due to AI's impact.
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Low-fidelity prototypes should be retained longer until fundamental issues are resolved before moving to high-fidelity.
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AI-powered prototypes resemble an updated Wizard of Oz method with consistent, scalable simulation rather than manual mimicry.
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Rapid prototype updates between interviews drive more efficient feedback loops and better product evolution.
Notable Quotes
"Stop trying to replace researcher with AI."
"Full automation of research is simply not working."
"The human-in-the-loop element is absolutely crucial to each research."
"We entirely stopped using Figma at Survicate and are all working on these AI-powered prototypes."
"Users were more curious and paid closer attention because some interactions actually produced results."
"Unlike a human operator, AI cannot get tired or struggle to maintain the illusion of automation."
"We decided not just to adapt the research team, but to change the entire product development process."
"Done is better than perfect, but after AI revolution, we can do things way faster than before."
"Low-fidelity prototypes let us keep fundamental changes flexible as long as possible."
"The new product approach lets researchers proofread, edit, and decide which insights should be tracked."
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