As AI becomes more common in market research, qualitative teams are asking a practical question: how can new tools help us move faster and hear from more people, while still protecting the nuance and interpretation that make qualitative insights useful?
For Leger, AI-moderated interviews are not a replacement for qualitative research. They are one more tool in the toolbox, helping researchers explore the “why” behind consumer behaviours, perceptions, and decisions with more speed, scale, and flexibility. As with any method, the value comes from using it in the right context, with strong design, quality control, and human interpretation.
To better understand how this approach works, we asked Lana Porter, Senior Vice-President, Consumer Insights (West), where AIMI fits and how Léger keeps human expertise at the centre of the process.
“AIMI is not about using AI for the sake of it. It is about making qualitative depth more accessible.”
1. When and why did Léger choose to integrate AI-moderated interviews into its qualitative research practices?
Leger began integrating AI-moderated interviews as part of our broader effort to use AI where it can genuinely improve the research experience for clients and participants. The timing was driven by a very practical need: clients increasingly need to understand the “why” behind behaviours, decisions and perceptions, but they often need those answers within compressed timelines.
For us, AIMI is not about using AI for the sake of it. It is about making qualitative depth more accessible. We can now conduct many one-to-one conversations in parallel, while still grounding the work in a structured discussion guide created by Léger researchers. That means clients can hear from more people, get richer verbatim feedback, and move from data collection to decision support much faster than with many traditional qualitative approaches.
The reason we adopted it is simple: it helps preserve what clients value most about qualitative research, the ability to explore motivations, emotions and context, while adding speed, flexibility and scale.
2. In concrete terms, how does an AI-moderated qualitative interview work, and how is it different from a traditional qualitative approach?
A good way to think about it is as a one-to-one interview where the moderator is AI-powered, but the research design is still human-led. A Léger researcher starts by defining the business question, the audience, the stimuli and the discussion guide. Participants are then invited into the interview, often through our existing LEO panel and recruitment infrastructure. Once they enter the platform, the AI moderator guides them through a natural conversation. Participants can respond by typing or speaking, and they can react to different types of stimuli, such as concepts, messages, images, videos or audio.
The important difference from a standard survey is that the interview is adaptive. If someone gives a short or unclear answer, the AI can ask a follow-up. If a participant mentions something interesting, it can probe for more detail. The experience is designed to feel conversational rather than like a static questionnaire.
Compared with traditional qualitative research, the biggest differences are scale, consistency and speed. A human moderator typically conducts interviews or groups sequentially, often with smaller samples. With AIMI, dozens or hundreds of interviews can happen in parallel, using the same core guide and probing logic. That gives clients a larger base of qualitative conversations, faster early reads, and outputs that can be reviewed and analyzed more efficiently. It does not replace every traditional qualitative method, but it creates a new agile option between a survey open-end and a fully human-moderated qualitative study.
“Researchers remain central. We use AI as a tool for moderation and organization; it’s not the strategist.”
3. What role do researchers play in an AI-powered research process, and how do you ensure the quality and relevance of the insights?
Researchers remain central. We use AI as a tool for moderation and organization; it is not the strategist. Léger researchers determine whether AIMI is the right fit, translate the client question into a strong study design, write the discussion guide, build the probing structure, design the screener, review stimuli, monitor fieldwork and interpret the results.
Quality starts before fieldwork. We need clear objectives, well-written questions, the right sample and precise instructions so participants know we are looking for thoughtful, detailed answers. During fieldwork, researchers can monitor responses as they come in, check whether participants are engaging at the right level, and adjust instructions or fielding if needed. After fieldwork, we do not simply accept an automated summary as the final insight. We review the themes, interrogate the verbatims, look for differences across audiences or cells, and test whether the conclusions are truly supported by what participants said.
The strongest insights come from the combination of AI-enabled scale and human judgement. AI helps us collect, structure and surface patterns quickly. Researchers bring category knowledge, methodological rigour, scepticism and business context – the things that turn a set of responses into a useful recommendation.
4. Can you share a concrete example of how AI-moderated interviews have been used in a recent project (from the client challenge to the insights generated and the decisions they helped support)?
One useful example is a specialty retail project where the client needed to choose between two brand platform directions. The question was not simply “which one do people like more?” The client needed to know which platform could better attract customers from other specialty retailers, strengthen the brand’s role across the purchase journey, reinforce expertise and credentials, and ultimately support engagement, visit frequency and loyalty.
Using AI-moderated interviews, we were able to expose participants to brand platform stimuli and then probe their reactions in depth. Participants explained what felt emotionally resonant, what felt credible, what would make them more likely to consider the retailer, and where the ideas needed refinement. Because the interviews were conducted at scale, the client could see both the richness of individual reactions and the broader patterns across the sample.
The learning was that both territories resonated but played different roles. One primarily reinforced existing perceptions and affinity, while the other proved more effective at driving differentiation and motivating action.
That distinction helped the client make a clearer decision. If the objective was growth beyond the existing footprint, the stronger platform was the one that gave consumers a reason to engage more actively with the retailer, while still leaving room to refine the creative execution. That is the kind of value AIMI can provide: it moves the conversation from surface-level preference to a deeper explanation of what each idea is likely to do in market and why.
“It does not replace every traditional qualitative method, but it creates a new agile option between a survey open-end and a fully human-moderated qualitative study.”
5. In what types of projects is this approach most relevant, and when would a more traditional qualitative approach remain preferable?
AIMI is most relevant when the client needs to understand the “why” behind a behaviour, reaction or decision quickly, and when hearing from a larger number of people would make the learning more useful. Strong use cases include early-stage exploration, concept and idea development, message or creative testing, brand positioning, customer journey questions, user experience feedback, and post-quantitative deep dives where we need to explain a surprising survey result.
It is especially helpful in agile environments. For example, a team may need to choose between several product claims, understand why a new concept is not landing as expected, or quickly explore what is driving a shift in customer behaviour. AIMI allows us to gather qualitative feedback at a scale and speed that can support those decisions without waiting for a full traditional qualitative cycle.
Traditional qualitative approaches remain preferable when the human relationship is central to the method. That includes highly sensitive or emotional topics, complex interviews where rapport and judgement are critical, co-creation sessions, group discussions where interaction between participants matters, ethnographic work where observation and context are essential, or senior stakeholder work where the moderator needs to adapt in very nuanced ways. In practice, we see AIMI as complementary to traditional qual. The right choice depends on the decision, the audience, the timeline and the level of human moderation required.
6. As a client, why should I choose this approach over another?
You should choose AIMI when you need qualitative depth, but you also need speed, scale and efficiency. It gives you more than a few open-ended survey comments, because participants are engaged in a guided conversation with follow-up probes. At the same time, it gives you more reach than a small set of traditional interviews, because many conversations can happen in parallel.
For clients, the benefit is very tangible. Imagine you are refining a new campaign, product idea or service experience. Instead of waiting weeks to hear from a small number of participants, you can hear from dozens or even hundreds of people, understand the words they use, identify what resonates or creates friction, and use those insights to make a decision quickly. It is particularly valuable when you need directional confidence, rich verbatims and a clear explanation of what is happening behind the numbers.
Another reason to choose it is participant flexibility. People can take part at a time that works for them, and they can often respond in the way that feels most comfortable, including voice or text. That can lead to more natural feedback. The key is that Léger does not treat the platform as a black box. We combine the technology with our research design, quality control and interpretation so clients get fast outputs that are still grounded in methodological expertise.
“AI can support qualitative research, but human judgement is what turns participant feedback into meaningful insight.”
7. How do you see qualitative research evolving as AI becomes more integrated into research practices?
Qualitative research is becoming more scalable, more continuous and more integrated with the rest of the research process. AI will make it easier to collect and structure large volumes of open-ended feedback, identify patterns quickly, and connect qualitative learning more directly to business decisions. It will also make it easier to bring more real consumer voices into the room, instead of relying only on small samples or short survey comments.
But the future of qualitative research is not “AI instead of researchers.” It is AI-supported research, with researchers spending more time on the work that matters most: asking better questions, choosing the right method, interpreting nuance, challenging easy conclusions and translating insight into action. Human moderation will still be essential in many contexts, particularly where empathy, group dynamics, sensitivity or complex judgement are required.
The biggest change is that clients will have more methodological choices. Traditional interviews, focus groups, online communities, surveys and AI-moderated interviews can each play a different role. The opportunity for Léger is to help clients choose the right tool for the decision they need to make – and to use AI in a way that makes research faster and more accessible without losing the human understanding that makes qualitative research valuable. I like to call this our “humAIne” approach to market research: using AI to bring speed, scale, and structure to qualitative learning, while keeping human expertise, judgment, and empathy at the centre of every insight we deliver.
Expanding what qualitative research can do
AI-moderated interviews do not replace traditional qualitative research. They add another option to the qualitative toolkit, helping organizations hear from more people, move faster, and better understand the motivations behind consumer choices.
As Lana’s perspective makes clear, the strength of AIMI lies in the combination of AI-enabled scale and human expertise. The technology can support faster conversations, richer verbatims, and more efficient analysis. But it is Leger’s researchers who frame the right questions, interpret the nuance, and turn what participants say into meaningful recommendations.
For clients, that creates a practical opportunity: bringing more consumer voices into decision-making without losing the methodological rigour and human judgement that strong qualitative research requires.
Want to learn more about how Leger can support your next research challenge?
Contact our team to explore whether AI-moderated interviews are the right fit for your project.