Qualitative research has always had a ceiling. Talk to enough people to truly understand the “why,” and you’ve spent weeks recruiting, scheduling, and synthesizing. Move fast with a quant survey, and you trade depth for scale. For decades, researchers have lived with that trade-off as the cost of doing the work.
That ceiling just got raised.
On May 6, 2026, Stephanie Vance, aytm’s VP of Customer Experience and Research Strategy, led a virtual session at the Greenbook Insights Tech Showcase demoing aytm’s Conversation AI. The 20-minute demo showed researchers what becomes possible when AI-moderated qualitative conversations run inside a quantitative survey, at the scale of hundreds of respondents, in a single platform.
Here’s what she showed, and why it matters for how you’ll run research next quarter.
Depth and scale, finally in one study
Quant tells you what people think. Qual tells you why. Conversation AI puts both in the same study, on the same platform, in the same fielding window.
Picture a concept test where every respondent rates your idea on a 7-point scale, then has a five-minute conversation with an AI moderator about what excited them, what confused them, and what would change their mind. Hundreds of those conversations, fully transcribed, automatically coded, and synthesized into themes by the time your morning coffee is cold.
That’s the move. Qualitative depth at quantitative scale, with no separate study, no second timeline, no parallel vendor.
How Conversation AI works
Skipper is the AI moderator running the conversation. You design the experience the same way you’d brief a human interviewer, and you have more control than you might expect.
Sample control. Set the percentage of respondents who’ll go through the conversational module, and give the rest a clean opt-out. Respondents without the bandwidth for a deeper task aren’t forced through one.
Depth settings. Tell Skipper how hard to probe. Light touch with no follow-ups, or full clarification mode where every interesting answer gets pursued.
Input flexibility. Respondents can type, talk, or switch between the two mid-conversation. They pick what feels natural.
Moderation styles. Choose Formal when you need consistency across respondents for clean comparisons, or Engaging when you want Skipper to follow each respondent’s lead and chase what’s interesting in the moment.
You’re not handing over the wheel. You’re configuring exactly the conversation you want, then running it at scale.
How people outside tech actually use AI
Stephanie’s live demo used a real study on AI in daily life, fielded to understand how people outside the tech industry perceive and use AI tools today. Traditional quant questions captured sentiment and usage. The Conversation AI module captured the texture beneath the numbers, where AI has slipped into daily routines, where skepticism still runs deep, and what people are quietly hoping it will do for them next.
The demo highlighted something subtle but important. Skipper personalized every follow-up to the respondent in front of it. A skeptic got different probes than an enthusiast. A heavy user got asked about edge cases a casual user never reached. Same study, hundreds of conversations, every one calibrated to the person on the other end.
Three ways to read the results
Once fielding closes, you have three views into the data, and you’ll use all of them.
Full transcripts. Every conversation, fully readable, fully searchable. The personalization shows clearly here, where you can watch Skipper adapt in real time to each respondent’s answers.
Coding and quantifying. Build a codebook or upload one you already have. The platform applies it across the dataset and turns unstructured conversation into chartable data. Quantified qual, ready for the deck.
Explore with Skipper. Automated synthesis across the full transcript set. High-level themes, sentiment at the conversation level and the subtopic level, and emergent topics you didn’t think to look for. The starting point for your readout, generated in minutes.
Quality you can trust
The fair question every time AI touches research data is whether the output is any good.
aytm built Conversation AI with that question at the center. Respondents get an opt-out for the conversational module, so the people who stay are the ones with the time and willingness to engage thoughtfully. Additional incentives compensate for the extra effort. A specialized sub-panel of respondents, vetted for articulate qualitative engagement, sits behind the studies that need that depth.
And the data centrifuge runs underneath all of it. The algorithm detects and removes poor-quality responses, including AI-generated ones, before they reach your dataset. You see the signal. The noise gets cleaned out.
How it fits into your research mix
Conversation AI complements your existing qualitative work. Focus groups still belong in your toolkit when you need group dynamics. IDIs still belong when you need an hour with a single high-value respondent.
Conversation AI lives in a different spot, the place where you’d previously settled for an open-end and hoped for the best, or commissioned a separate qual study you didn’t have the budget or timeline to run. Tack it onto the end of a short survey, or drop it into the middle of a longer one to break up the rhythm. You’ll get the “why” at a scale you couldn’t reach before.
For a quick reference on how Conversation AI fits into your research workflow, download the Conversation AI one-pager or learn more here.
If you’d rather see Conversation AI applied to your own research question, request a demo and we’ll build a study around it.
Frequently asked questions
What is aytm’s Conversation AI?
Conversation AI is aytm’s AI-moderated qualitative research tool. It runs inside a quantitative survey, letting researchers capture open-ended, probing conversations from hundreds of respondents in a single fielding window. Skipper, aytm’s AI moderator, runs each conversation and personalizes follow-up questions to every respondent.
How does Conversation AI compare to focus groups and IDIs?
Conversation AI complements traditional qualitative methods. Focus groups still belong in your toolkit when you need group dynamics. IDIs still belong when you need an hour with a single high-value respondent. Conversation AI fits the gap where you’d previously have settled for an open-ended survey question or skipped qualitative depth entirely because of timeline or budget.
Can AI-moderated qualitative data be trusted?
Yes, with the right safeguards. aytm builds quality controls into every Conversation AI study. Respondents get an opt-out for the conversational module, so the people who stay are willing to engage thoughtfully. Additional incentives compensate for the extra effort. A specialized sub-panel of articulate qualitative respondents sits behind the studies that need that depth. The data centrifuge algorithm detects and removes poor-quality and AI-generated responses before they reach your dataset.
How do I analyze the data from Conversation AI?
Three views into every dataset. Full transcripts let you read every conversation, fully searchable. Coding and quantifying turns unstructured responses into chartable data using a codebook you build or upload. Explore with Skipper synthesizes themes, sentiment, and emergent topics across the full transcript set, generated in minutes.
Where in a survey should Conversation AI go?
Placement depends on survey length and flow. Drop it at the end of a short survey, or into the middle of a longer one to break up the rhythm. The opt-out keeps respondent fatigue under control either way.
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