What if your open-ended answers could get deeper in real-time? Or could your focus groups scale up like a survey? That’s the world we’re walking into: Where artificial intelligence is giving qualitative research a bit of a glow-up.
We’re not talking about robots replacing researchers; we’re talking about making qualitative insights easier to gather, faster to analyze, and a whole lot more scalable. Thanks to AI, qual is shaking off its reputation for being slow and hard to quantify, and stepping into a new era of speed, structure, and smarts.
Let’s explore how it’s happening.
Why we still love good old-fashioned qualitative research
There’s a reason qualitative research never went out of style. It gives you the stuff quant can’t. Human nuance, emotion, motivation, context. A real sense of self. It’s how you find out that people buy your product not just because it’s cheaper, but because it reminds them of home. Or that they’re loyal to your brand because it makes them feel seen, not sold to.
The stories people tell, the language they use, the moments of hesitation or surprise in a conversation, that’s where the magic happens.
But here’s the catch: As valuable as qual is, it hasn’t always been the most agile method in the researcher’s toolkit.
The traditional qualitative research pain points
We all know the usual headaches:
Small sample sizes due to time or budget constraints.
Manual analysis that takes weeks (and a strong tolerance for Post-its).
Subjective interpretation that’s harder to defend with stakeholders who want stats and certainty.
Scalability issues when you’re trying to pull themes from hundreds (or thousands) of open ends.
In short: It’s rich, but messy. Insightful, but slow. Powerful, but not always practical. Especially at scale.
AI is changing the qualitative research game
Enter AI. Suddenly, that pile of messy verbatims starts looking a lot more manageable. AI can now analyze open-ended responses, surface themes, cluster ideas, track sentiment, and even generate hypotheses based on qualitative inputs. What used to take a full team days or weeks to do, AI can now do in hours, or even minutes.
According to the 2024 GreenBook report, one of the greatest advantages that AI brings to market research is the ability to uncover insights that were previously unattainable: “Tasks that once required considerable time, money, and effort are now made easier with AI”.
Little wonder then that 69% of technology providers and 56% of full-service research providers are already using Generative AI to supplement work processes: “This widespread adoption enables researchers to extract actionable insights faster by analyzing unstructured data, summarizing meetings, and even drafting reports. This means better, more actionable insights for businesses, derived from previously hidden patterns and connections.”
Even better? It’s not just about tidying up your data. AI is also helping to enrich it.
AI probing helps you go deeper without doing more
One of the more exciting developments is AI-assisted probing, where algorithms are trained to ask the kind of follow-up questions a seasoned moderator might.
You can now run surveys that automatically ask clarifying questions when someone gives a vague or intriguing answer. For example, if a respondent says, “I just don’t trust that brand,” AI can prompt them with, “What don’t you trust about the brand?”
This simple, scalable probing can turn a throwaway comment into a rich insight. It’s making depth accessible to more projects, more clients, and more timelines.
What qualitative research at quantitative scale looks like
It’s not just probing. Researchers are using a variety of AI-powered tools to analyze qualitative data like it’s quantitative:
Thematic clustering of open ends using machine learning.
Sentiment analysis across entire datasets.
Text coding and tagging in real time.
Pattern recognition across interviews, focus groups, or open survey responses.
Automated summarization to create digestible insight narratives.
Some teams are even running large-scale qual studies with 1,000+ participants – something that would have been unthinkable a few years ago.
And yes, human oversight still matters (and always will). But with AI doing the heavy lifting, researchers get to spend more time interpreting and storytelling, and less time sorting through spreadsheets or sticky notes.
What this means for insight teams
AI-powered qual doesn’t just save time. It opens doors.
More usability: Teams with smaller budgets or tighter timelines can now include qual in their mix.
More agility: You can iterate faster, test messaging with more nuance, or respond to emerging trends in real time.
More credibility: When you can show stakeholder-friendly charts from your qual data, you’re more likely to get buy-in and action.
More creativity: Freed from the grind of manual coding, researchers can spend more energy on the why, not just the what.
In short: Qual isn’t being replaced. It’s being supercharged.
So, what’s next?
The momentum is real, and it’s pushing qual into places it couldn’t previously reach.
Forsta’s own AI capabilities (including smart text analytics and responsive follow-up questions) are already making this possible. And as we build, refine, and learn, we’re making sure qual keeps its human heart; even when it’s processed by machines.
In fact, AI-powered tools like Forsta’s AI probing personalizes surveys in real time to give customers what they really want: More meaningful interactions. The benefit for brands is faster insights, stronger engagement, and the ability to stay ahead of issues before they even arise. And with tools like Forsta’s AI Summary condensing large volumes of feedback into digestible quant-like insights, decision-makers can act fast without wading through endless qual responses.
The future of qualitative research
For years, qualitative research has been the soulful side of insights. Rich, deep, and (let’s face it) a little unruly.
AI won’t change that. But it does mean we can scale soulfulness, and bring more of that depth into decisions, campaigns, strategies, and boardrooms. Because when you can treat open-ended data with the same rigor (and speed) as quant, the possibilities multiply. And we think that’s something worth getting excited about.
