How Intuit Unlocked Fast, Scalable Research with AI and User Interviews

Intuit, a global financial technology leader behind tools like QuickBooks and TurboTax, needed a faster way to understand how small business users were interacting with new AI-powered product features. Like many product teams working on fast timelines, they faced pressure to validate assumptions and uncover potential issues—without the luxury of weeks-long research cycles. Traditional qualitative methods were too slow and limited in scale to meet these demands. 

By combining User Interviews for rapid participant recruitment with Outset’s AI moderation platform, Intuit developed a scalable research process that allowed them to quickly test, learn, and iterate. In just two days, they gathered insights from 36 participants, uncovered unexpected errors impacting users, and identified clear product opportunities—all while spending less time on manual tasks and more time on actionable insights.

The Problem

Intuit needed to rapidly explore how small businesses were interacting with their QuickBooks product, particularly focusing on new AI features designed to automate tasks. While the goal was to validate existing assumptions, the research process needed to move quickly, as decisions had to be made within days. Traditional qualitative methods would not meet the team's urgent needs, and the research team faced limitations on time, resources, and scale.

Intuit wanted to explore deeper, unexpected questions around how AI could reduce errors like incorrect invoice amounts. However, the project required a larger sample size to gain confidence in the findings, which meant the research needed to be both scalable and fast.

The Solution

Intuit turned to User Interviews for quick participant recruitment, a key factor in speeding up the entire research process. The combination of User Interviews’ fast recruitment and Outset’s AI moderation allowed Intuit to run iterative studies at scale. This gave the team the flexibility to gather qualitative insights across a broad range of participants while dynamically probing deeper into unanticipated findings.

Using Outset’s AI moderation platform, the team was able to craft tailored interview scripts and introduce dynamic questions based on participant responses. With the ability to probe deeper into open-ended answers, the AI moderation method provided flexibility and depth—key aspects missing in traditional linear research methods. The platform’s automated transcription and analysis also reduced the time spent on manual coding, enabling the team to focus on uncovering new insights quickly.

By leveraging User Interviews, Intuit was able to recruit participants swiftly, enabling them to conduct the study in a fraction of the time that would typically be needed for traditional recruitment and data collection. This meant that Intuit could run multiple studies in a week, allowing them to stay agile and responsive to new findings.

“As a customer of both Outset and User Interviews, we’re excited to use this integration. We’re leveraging AI-moderated research with Outset more and more to accelerate our research, and the quality of participants is critical. Integrating with the User Interviews panel will save us a ton of time and hassle, and allow us to capture user insights faster,” 
-Joanne McGourty, Senior Manager, Research | Intuit

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The Results

• Speed and Scale: In just two days, Intuit completed three studies, gathering insights from 36 participants. AI moderation allowed the team to iterate quickly on the research, collecting diverse responses without sacrificing depth.
• Unexpected Insights: One of the key findings from the research was the "fat finger phenomenon"—small business owners reported accidentally entering incorrect invoice amounts due to manual data entry errors. This issue would have been difficult to identify without AI’s dynamic probing capabilities.
• Quick Data Analysis: The Outset AI platform transcribed and analyzed responses automatically, which saved the team hours of manual effort. Intuit could quickly refine the thematic analysis, identifying key trends without being bogged down by repetitive tasks.
• Engagement with Stakeholders: The dynamic nature of the AI tool allowed Intuit’s stakeholders to ask real-time questions about the data, which led to deeper engagement and faster decision-making. This responsiveness would have been difficult with traditional methods that require longer report cycles.
• Impact on Decision-Making: The findings from the research led to the formation of a new engineering team focused on addressing invoicing errors—a direct result of the fast, iterative process enabled by AI moderation and the quick recruitment capabilities of User Interviews.

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