The secret to leveraging real value from AI in qualitative research is thinking like a user, not an investor—and discerning the data. <br /><br />In this episode, we’re joined by <a href="https://www.linkedin.com/in/a-a-ron-cannon?utm_source=share&amp;utm_campaign=share_via&amp;utm_content=profile&amp;utm_medium=ios_app" target="_blank" rel="noreferrer noopener">Aaron Cannon</a>, CEO &amp; Co-Founder at <a href="http://outset.ai" target="_blank" rel="noreferrer noopener">Outset</a>, as he explores how AI can eliminate the trade-offs between depth and speed in qualitative research to gather richer, more actionable insights at scale—without the traditional time and cost limitations.<br /><br />We also discuss: <ul><li>How to avoid common pitfalls in AI adoption by focusing on user needs and real-life applications.</li><li>The importance of thinking iteratively and adapting as research progresses.</li><li>How to balance depth and scale when conducting research.</li><li>The power of mixed-methods research + how AI can enhance both qualitative and quantitative approaches.</li></ul>

The Consumer Insights Podcast

Stravito

Cutting Through the AI Noise with Aaron Cannon, CEO & Co-Founder at Outset

OCT 10, 202425 MIN
The Consumer Insights Podcast

Cutting Through the AI Noise with Aaron Cannon, CEO & Co-Founder at Outset

OCT 10, 202425 MIN

Description

The secret to leveraging real value from AI in qualitative research is thinking like a user, not an investor—and discerning the data. <br /><br />In this episode, we’re joined by <a href="https://www.linkedin.com/in/a-a-ron-cannon?utm_source=share&amp;utm_campaign=share_via&amp;utm_content=profile&amp;utm_medium=ios_app" target="_blank" rel="noreferrer noopener">Aaron Cannon</a>, CEO &amp; Co-Founder at <a href="http://outset.ai" target="_blank" rel="noreferrer noopener">Outset</a>, as he explores how AI can eliminate the trade-offs between depth and speed in qualitative research to gather richer, more actionable insights at scale—without the traditional time and cost limitations.<br /><br />We also discuss: <ul><li>How to avoid common pitfalls in AI adoption by focusing on user needs and real-life applications.</li><li>The importance of thinking iteratively and adapting as research progresses.</li><li>How to balance depth and scale when conducting research.</li><li>The power of mixed-methods research + how AI can enhance both qualitative and quantitative approaches.</li></ul>