Voice AI and the Future of Qual: Rethinking Depth, Speed, and Scale

Why Qual Needs Reinvention—Not Just Automation

Qualitative research is stuck in a trade-off: depth vs. speed, budget vs. scale. Interviews are rich but time-consuming and costly. Surveys shouldn’t be used for qual—but it’s often bloated with last-minute open-ended questions added in. Focus groups? Expensive with lots of social bias (you need very good moderators). And across diverse APAC markets, doing any of this at scale is near impossible.

Yet the pressure on insights teams keeps growing. Product launches are faster. Clients increasingly demand more—with better data and faster turnarounds. Markets and customer segments are more fragmented than ever. Teams need meaningful, emotionally intelligent feedback—yesterday.

Voice AI isn’t just another AI tool—it’s a fundamental shift in how we listen. It allows teams to capture rich, authentic, emotionally nuanced feedback at scale, empowering researchers to uncover deeper, more powerful and actionable insights.

Here’s how it directly addresses the biggest issues holding researchers back.

The Real Problems with Surveys Today

Surveys have become noisy, inauthentic, and increasingly unreliable.

People skip through them. Open-ends are vague, copy-pasted from Google and ChatGPT-generated. Worse, bots are completing surveys with all AI generated answers. It’s becoming harder to trust the data, let alone act on it.

A study showed that nearly half (46%) of survey respondents had to be removed due to bad data— such as incoherent responses or speeding through surveys. (Qrious Insight)

And even when people do take time to respond, typed answers tend to be short and impersonal.

Compare that to voice: spoken responses are 4–5x longer on average, offering richer language, tone, and emotion. People speak the way they think, which gives researchers a window into how they really feel.

Voice AI makes it easy to prompt, record, and analyze open-ended audio—turning surveys into something closer to a conversation.

How to Scale 1:1 Interviews Without Burning Your Budget or Time

One-on-one interviews are the gold standard—but let’s be honest, they’re challenging for many resource and time constrained teams. You’d be hard pressed to find a team that can properly recruit, schedule, run sessions, transcribe, analyze—in less than 3 weeks.

With asynchronous voice interviews, participants respond on their own time. And with AI handling transcription, emotion detection, summarization, and theme extraction, researchers can jump straight to what matters.

“But do people actually want to talk to an AI researcher (a bot)?”

From what we’ve seen across thousands of AI moderated voice user interviews on our platform: yes—and not just “yes,” but highly engaged.

We consistently see around 20x more words than typed surveys, in roughly the same completion time. And unlike customer service bots or robocalls, people often describe the experience of speaking with the AI researcher as “cathartic” or even “therapeutic.”

There is a Gen Z, AI boyfriend/girlfriend app called Replica with 30m+ users where users voice chat with their AI companion. Voice AI is that good now. And getting even better by the week.

You Don’t Need a Local Team in Every Market Anymore

Doing qualitative research across APAC regions usually means hiring local moderators, translators, and coordinators. The result? Budget overload and logistical complexity—especially if you’re targeting more than two or three countries.

But today, AI can transcribe and translate multilingual voice data instantly, retaining tone and emotional nuance that often gets lost in text. Of course contextual and cultural misinterpretations with AI have a ways to go. But until AI takes over all of our jobs—we will always need human intuition and context to guide and oversee the work.

So what can we do today? You no longer have to choose between speed, cost, and cultural fidelity. With Voice AI, you can run consistent studies across Singapore, India, Indonesia, and Australia—all without sacrificing quality or breaking the bank.

Voice AI Doesn’t Replace Researchers—It Amplifies Them

Voice AI doesn’t eliminate human insight—it supercharges it.

AI takes care of the heavy lifting: running interviews 24/7, transcription & translation, emotion detection, and concise summaries. Researchers get to focus on what they do best: spotting patterns, exploring edge cases, building narratives, and uncovering deeper meaning.

One fintech unicorn in India used AI voice feedback to identify a key underserved investor segment from a long tail of investor personas before they even started their research project. It saved them weeks of research time and made their follow-up interviews 10x more focused—resulting in more confidence and buy-in from their larger product teams.

Here’s another example. An online graphic design SaaS tool in Singapore used Voice AI to get a clearer understanding of why their NPS score was low. 30+ participants completed 5-10min voice conversations with an AI researcher after they did a conversion action.

Within a few days, the team pinpointed key pain points, identified nuanced profiles of the promoters and detractors and got actionable insights on how to improve their NPS.

The possibilities are wide and we’re just scratching the surface. Teams can run daily diary studies, understand why behind NPS, CES or CSAT scores, do product discovery, brand sentiment analysis and more—fast, cheap and deep—without logistical overhead.

Conclusion: Voice Isn’t a Trend—It’s the Missing Layer in Modern Qual

We’ve optimized survey platforms, scaled digital analytics, and automated recruitment. But in doing so, we’ve left behind the most human input we have: their real voice.

Meanwhile, panel quality is declining. Response rates are dropping. Multimodal UX research is rising, and generative AI is already helping researchers with tasks like hypothesis generation, insight extraction, and report drafting.

Voice AI can 10x your research quality—at scale, across borders, and with the speed today’s teams demand. It creates more space for nuance, empathy, and honest human feedback, without the bottlenecks that slow research down.

As researchers, we’ve always known that the richest insight lives in human stories. Voice AI gives us the power to hear more of them—more honestly, more often, and across more borders than ever before.