The Rise of AI-Moderated Interviews

by Jeremy Cochran, PsyD

The conversation around AI in market research has quickly shifted from possibility to practice. Over the past two to three years, AI has reshaped core research activities from survey design and sample management to analysis and the use of synthetic respondents. Now, its influence is extending even further into the way insights are gathered, as a growing number of startups and platforms introduce AI-Moderated Interviews, also known as Qual-at-Scale. This rising methodology allows respondents to be “interviewed” by an AI moderator, allowing respondents to speak their answers in natural language. AI-Moderated Interviews, or AIMIs, give qualitative research a quant-like scale by reducing logistical barriers to speed and size that come with human moderators. AIMIs have become very popular, with startups raising large volumes of venture capital to develop this technology.

While the technology itself is relatively straightforward, it generates a lot of questions:

• What does it mean for a chatbot to “conduct” qualitative research?
• How should insights professionals think about or incorporate this technology into their research designs?
• How do we think about a data collection method that falls somewhere between qualitative and quantitative?

Over the past six months, Burke has tested, refined, and applied AIMIs alongside clients across a range of business challenges. Through this work, we’ve found that AIMIs don’t fit neatly within traditional qualitative or quantitative research frameworks. Instead, they represent a distinct methodology that blends the depth and context of qualitative approaches with the scale and structure of quantitative methods. As with any emerging methodology, realizing its full potential requires new ways of thinking, new quality standards, and new best practices.

The End of the Qual-Quant Binary

To understand the impact of AIMIs on the insights industry, it’s important to understand that market research has generally bifurcated research methodology into two types: quantitative and qualitative, each with their own assumptions and uses. Quantitative is left-brain, with rigid rules meant for testing hypotheses and ensuring validity. Qualitative is right-brain, with rough guidelines meant for exploration and deep, emotional understanding. It’s not that researchers can’t use either, but the methods solve different problems and, in most cases, are conducted separately.

But, with AI and the wave of innovation that accompanied it, the qual-quant binary has become more of a spectrum. Generative AI’s language and reasoning capabilities can simulate a qualitative interview by asking questions, processing responses, and generating relevant follow-ups.

This capability led to the creation of new qual-quant hybrid methodologies such as AI-moderated interviews, conversational surveys, AI community management, and others.

How Does AI Act as a Moderator?

If you envision qualitative and quantitative research as two ends of a spectrum, AI-moderated interviews would be near the center, but still on the qualitative side. These are still interviews where respondents are verbally sharing responses in their own language, and the analysis is still qualitative (deriving overall findings based on emergent themes in language across participants).

However, there are elements of traditional qualitative research where AI-moderated interviews fall short, such as connecting with respondents on a deep emotional level (AI interviews are generally more terse), asking more exploratory questions with respondents (AI interviews require more pointed questions), and getting the “backroom experience” of watching interviews live with others (AI interviews are conducted asynchronously).

Although AI-moderated interviews are inherently qualitative, they incorporate quantitative elements that should be considered in the initial research design. With AI leading the moderation, these interviews can be scaled tremendously in terms of size (no limit on the number of interviews you can collect) and speed (hundreds of interviews can be conducted at the same time). AIMIs are also conducted asynchronously, so interview and discussion guide design becomes very crucial. These elements can lead to seeing the otherwise qualitative research in quantitative mindset, asking questions like “How many people can we get? How fast can we get out of field? What are the differences by subgroups?”.

Potential Pitfalls with AI-Moderated Interviews

While the technology is impressive, the devil is in the details once you’re actively in field. There are three key areas where using AIMI platforms can negatively influence your research outcomes:

Interview Guide Writing

Experienced researchers know that how you ask a question is just as important as what you are asking, and thus take time to think through the best design for interviews and surveys. But AIMI platforms often use AI to help researchers move quickly by auto-generating guides based on general prompts. These make for good starting points, but ensuring your interview script includes everything you need takes time and continual editing.

The Solution? Work with an experienced qualitative expert to refine your survey script.

Sample Management

AIMI platforms often tout the ability to get hundreds or even thousands of interviews in days. But, that begs the question: Who are the people they’re interviewing and where do they come from? To get this scale, AIMI platforms often partner with large, aggregated panels that generally service quantitative surveys. Since these respondents aren’t used to qualitative-style interviews, they’re less likely to be engaged, thoughtful, and effusive in their responses. Instead, working through qualitative panels can yield more engaged respondents, but scaling this can be expensive.

The Solution? Adjust your expectations on speed vs. quality and work with a team of experienced sample managers that can identify the right respondents for your needs.

Analytical Takeaways

AIMI platforms are very effective in analyzing and summarizing top themes from the interviews and, as with other AI tools, can quickly find patterns across large numbers of respondents. However, these platforms can’t tell you what to do with the research. It gives you the “what,” but not the “so what.”

The Solution? Work with experienced analysts to verify key findings, put insights into perspective with other research, and make strategic recommendations.

Where do AI-Moderated Interviews Fit?

With their qual-quant hybrid nature, AIMIs are best for focused objectives requiring medium depth of insight.

These can include:

• Exploring known, but fuzzy, topics
• Early-stage concept generation
• Understanding language, metaphors, and framing customers use
• Illuminating pre-established segments
• Looking for qualitative differences across subgroups
• Journey/Path to purchase (if easily recalled)

Furthermore, by meeting participants where they are, whether in a store aisle, at home, or during everyday experiences, AIMIs enable richer in-context learning. This opens the door to more naturalistic applications such as shop-alongs, ethnographies, and in-home product tests, while reducing many of the logistical barriers associated with traditional approaches. However, in these cases you’ll be sacrificing some depth for speed and scale. AIMIs are not a good fit when a project requires understanding respondents or a topic at a deep or emotional level, or when topics are very complex or sensitive. AIMIs also cannot handle group dynamics, so human moderators are still best for instances where focus groups would be most advantageous.

New Thinking for a New Age of Research

It’s tempting to see AIMIs as a shortcut to faster, lower-cost qualitative research. In practice, however, they reflect something bigger: a new form of research where qualitative and quantitative approaches intersect. When applied thoughtfully, AIMIs can deliver timely, meaningful insights—but only with a clear understanding of their strengths, limitations, and appropriate role within a broader research strategy.

Interested in reading more? Check out Jeremy’s other articles:

Qual-at-Scale: A Clear Look Beyond the Hype

How Millennial Millionaires Really Think About Money

Helping Consumers Navigate Tariffs

Inflation’s Impact on Consumers

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Source: Feature Image – ©Jose Calsina – stock.adobe.com