This review is part of a larger series of LinkedIn newsletters titled AI in Market Research: Reviews of AI tools, platforms, and solutions that market researchers should use today.
Conversational AI is no longer experimental—it’s already transforming how businesses operate at scale. In HR, platforms like Paradox and HireVue are automating parts of the interview process, screening thousands of candidates with voice-based interactions that simulate human conversations. In customer service, AI agents are fielding millions of calls per month for companies like DoNotPay, Bank of America, and others—handling tasks ranging from billing questions to technical support with an increasingly natural and emotionally aware tone.
And now, that same potential is extending into market research. Tools like Sesame, OpenAI’s voice demo, and Hume AI are showcasing voice-driven interactions that can recall context, adjust tone, and even detect emotional nuance in real-time. These developments hint at a future where large-scale qualitative research might be conducted by an AI that can hold a fluid conversation, potentially probe deeper when needed, and capture subtle signals in how something is said—not just what is said.
Of course, this raises an important and ongoing question: Are these tools truly ready to replace humans in nuanced research settings? In some cases—such as high-volume surveys, initial screeners, or prep work—they’re already proving useful. In others, especially where empathy, interpretation, or contextual flexibility are required, they still have limitations. But the progress is undeniable, and for research teams willing to explore their strengths and boundaries, conversational AI is becoming a powerful part of the toolkit.
So what’s the current state of the art when it comes to conversational AI? Here are three interesting platforms shaping the future of voice-driven AI:
Sesame (https://www.sesame.com/) recently open-sourced their large voice model, positioning themselves as a serious player in the space. Their live demo is impressive—fluid back-and-forth conversation with consistent memory, tone control, and contextual awareness. It’s a reminder that voice AI isn’t just about transcription or dictation anymore—it’s about creating AI agents that can engage naturally, across accents, intonations, and emotional registers.
OpenAI’s Voice Mode (https://www.openai.fm/) is also pushing boundaries. They’ve launched a new portal to demo real-time conversation with their voice models, which can reason, remember, and even show personality in tone. The ability to interrupt the AI mid-sentence, or carry on a fluid, layered conversation, makes this one of the most advanced experiences to date.
Hume AI (https://platform.hume.ai/) adds a layer of emotional intelligence—literally. Their platform analyzes vocal tone, emotional expressions, and subtle cues in a person’s voice, giving researchers insight into not just what someone is saying, but how they feel when saying it. In applications like ad testing, concept validation, or user interviews, that emotional layer could provide an entirely new depth of insight.
If you haven’t tried these demos yet, it’s worth pausing here and giving them a spin. Click through the links, test a few voice interactions, and then come back—you might be surprised by just how far this technology has come. What once felt like sci-fi is now starting to look like a real, usable tool in the hands of researchers.
So what does all this mean for market research?
First, we’re entering an era where high-scale, high-fidelity voice-based research becomes viable. We can now imagine running thousands of qualitative interviews—automatically conducted by AI voice agents—then analyzing not just the transcripts, but the emotional nuance, vocal tone, pacing, and delivery. These layers of expression, previously hard to capture at scale, are now becoming structured data points that can inform everything from messaging strategy to product positioning.
But the implications go well beyond efficiency. These tools also introduce an entirely new layer of complexity around influence, identity, and trust.
In espionage, there’s a concept known as operating under a “legend”—a fully constructed false identity complete with backstory, location, accent, and even documentation to support it. With today’s conversational AI, we’re creeping into a world where an AI could convincingly embody such a legend. Imagine an AI research participant who not only speaks with the right accent and vocabulary for their supposed location or background, but also reflects the education level, cultural references, and speech patterns consistent with a LinkedIn profile or professional persona. If that AI was generated with the intent to deceive, would we know? Could we know?
The same persuasive potential exists on the researcher’s side. AI voice agents could be designed to adjust their tone, accent, or pacing to match a respondent’s style—a technique commonly used in sales (often called mirroring). A fast talker might be met with energetic enthusiasm, while a slower, more thoughtful participant might be engaged with calm, patient pacing. On one hand, this could lead to more natural conversations and richer insights. On the other, it raises ethical questions: is this tailoring… or manipulation? Where is the line between rapport and influence?
And the possibilities extend further:
- Persuasive AI moderators: What happens when the AI interviewing a respondent starts guiding them—not just probing for clarity, but subtly reinforcing certain answers through tone or phrasing?
- Synthetic empathy: If an AI sounds sympathetic, responds warmly, and mimics human concern, will respondents feel more open—or more misled if they find out later it wasn’t a human?
- Bias in tone matching: If an AI is trained to match certain accents or tones better than others, could that unconsciously favor responses from some demographic groups while alienating others?
- Synthetic respondents: Could bad actors train AI models to impersonate real respondent types—providing fake but plausible responses in high-volume quant studies? And would those responses pass even a careful screen.
To be clear, many of these risks exist in some form today. Fraud, deception, and response bias are familiar challenges in research. What’s changing is the level of realism and scale that AI introduces—and how difficult it may become to distinguish the real from the synthetic, the sincere from the engineered.
For market researchers, this means doubling down on quality controls, transparency, and human oversight. It also means we’ll need to wrestle with new ethical questions about what it means to understand someone, especially when we’re increasingly talking to machines that sound human—or humans who might actually be machines.
In Sum:
These developments point to a future where conversational AI won’t just support research workflows—it may actively conduct them. Voice-based systems are already capable of guiding conversations, reading emotional cues, and adjusting their behavior mid-dialogue. That opens the door to AI interviewers—tools that could conduct in-depth qualitative interviews on their own, potentially across hundreds or thousands of participants.
But what does it mean to let a machine lead the conversation?
Can these AI agents ask follow-up questions that genuinely surface insight—or are they just moving through logic trees with human polish? Do they build rapport, or simulate it? And if they’re effective at drawing people out, are they doing so in ways that are ethical, or merely persuasive? These are questions we’re going to explore further in an upcoming set of pieces, where we’ll look more closely at what it means to put an AI in the moderator seat.
To uncover that, look for some upcoming pieces we’ll be sharing over the coming weeks, as we’re currently spending time benchmarking a wide variety of AI interviewer platforms that specifically target market research workflows. We’ll share what worked, what didn’t, and what we think is missing from these tools. Importantly, we won’t be sharing which tools we looked at, as the goal isn’t to publicly shame or praise anyone. But we do want to provide a sense of where these tools and platforms can meaningfully help a market research team today and where they aren’t quite ready for prime time.
Because while the tech is catching up quickly, the harder conversation is about how we choose to use it—and what kind of interviewer we really want AI to become. And that future ahead of us will definitely present some interesting opportunities and challenges that we’ll all need to address as a market research community.ar checker might offer. It provides tailored suggestions that simplify complex content into accessible and engaging content.
