Quillit® highlights the importance of building trust with AI, focusing on data security and output accuracy.
We illustrated key questions or documentation to ask from an AI vendor, and illustrate how Quillit is meeting high standards by outlining our security measures and presenting features that enable output validation to ensure accuracy and relevant context.
Then we presented 3 use case examples of HOW we see researchers are, or could be, actively using AI throughout the research process, beyond the obvious “summarize my IDI transcripts.”
- Pre-Research: Using AI to dive into relevant market reports, competitor analysis, and/or relevant reports from previous research.
- Data Collection/Project Fielding: Expanding a researcher’s marketable value by giving their clients options for “mid-research deliverables” essentially using AI to deliver regular/daily summaries of each interview/group/segment, or findings on key research objectives as research is actively progressing, to engage stakeholders throughout the research process.
- Analysis and Reporting: Uncovering key insights and diving deeper into the data, generating a bird’s eye view for context and catch insights from falling under the radar, while quickly finding verbatims to include the voice of the respondent(s) in the final report.
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