Research Reinvented: Scaling global qualitative research without losing the human touch

This is the fourth article in our five-part series based on the webinar we hosted with Forrester and Quadrant Strategies, Research Reinvented: How to Master GenAI and Ensure You’re Not Left Behind.  In this fourth post, we’ll look at how organizations can scale global qualitative research while still preserving the human understanding that makes qual so valuable.

Why this question matters

Scaling qualitative research has always been tricky. The deeper you go into human stories, the harder it gets to repeat that process across markets and cultures. Global studies multiply the challenge: different time zones, languages, and nuances stretch even the best teams to their limits.

AI is reshaping that equation. The real challenge now isn’t whether we can scale qual. It’s whether we can do it without sacrificing the empathy and nuance that make qualitative insights so powerful in the first place.

When AI interviews make the impossible possible

Robin Lindberg of Quadrant Strategies shared a recent project that made this crystal clear. His team needed to test 20 messages in under two weeks. Traditional qual would have meant dozens of focus groups across time zones – too slow and too costly.

Instead, they turned to AI-moderated interviews. In just a few days, they collected feedback at scale, narrowing the 20 messages to five strong contenders. Then, they brought those five into human-led focus groups for deeper exploration.

The hybrid model flipped the script. Instead of replacing focus groups, AI set them up for success by filtering the noise and spotlighting where human expertise was most valuable.

“It wasn’t about replacing focus groups. It was about setting us up for success by narrowing it down so we could test the best messages in that short time frame.” — Robin Lindberg, Quadrant Strategies

Human understanding isn’t optional

Rowan Curran, Principal Analyst at Forrester, reminded us that scale only works if trust and transparency are built in. Natural language querying and semantic search make it easier to sift through mountains of data, but they can also surface “fuzzy” answers that don’t hold up under scrutiny.

That’s why human checkpoints are essential. AI can run interviews, accelerate analysis, and connect themes. But experts still anchor the process: validating outputs, interpreting nuance, and deciding what matters.

Without those guardrails, scale becomes shallow. With them, scale multiplies the impact of every hour a researcher spends.

The hybrid model is here to stay

AI or human-led methods is a false choice. The power is in using them both.

AI-led interviews bring:

• Speed and reach. Hundreds of conversations in days, across languages and time zones
• Comfort on sensitive topics. Some participants feel freer opening up to an AI in a chat interface.
• More depth than surveys. Probing questions that capture nuance instead of checkbox answers.
  

Human-led methods bring:

• Nuance and empathy. Picking up body language, tone, and the unspoken signals AI can’t.
• Complex conversations. Essential for tough topics or sessions that need live navigation.
• Stakeholder confidence. Some questions demand a live moderator in the room.
  

Together, they give teams real flexibility: scale when speed matters, dig deep when empathy matters.

Discuss’ perspective on scaling qual

At Discuss, this hybrid model is built into the platform. AI is woven through the project lifecycle:

• Project Agent streamlines setup – designing studies, drafting guides, recruiting participants.
• Interview Agent scales conversations globally with AI-led moderation that feels natural.
• Insights Agent cuts through the noise of transcripts, surfacing themes, highlight reels, and quotes for storytelling.

These tools don’t replace researchers. They give them back time and space to focus on strategy, context, and recommendations so they can focus on the work that wins trust and drives action.

As Jilleun Eglin, Sr. Director of Product at Discuss, put it during the webinar: the goal isn’t to let AI “run the show.” It’s to use AI as a partner across the lifecycle so researchers can focus on the questions only humans can answer.

Use cases that prove the model

AI-moderated interviews are already helping scale qual in ways surveys can’t:

• Message and concept testing. Narrow down dozens of ideas quickly, then hand the best to human teams for deeper exploration.
• Consumer closeness studies. Capture in-the-moment reactions in real settings – stores, homes, digital journeys – without sending moderators everywhere.
• Prototype feedback. Share visuals or demos, capture reactions instantly, and probe on the “why.”
• Global empathy work. Hear voices from across cultures in their own words and languages, on timelines and budgets that would have been impossible before.
  

The pattern is consistent: AI expands reach, humans preserve empathy, and together they unlock an understanding that neither could achieve alone.

Your next step

Want to see Robin and Rowan unpack these examples in detail? Watch the full webinar recording.

And stay tuned for the last post in this series, where we’ll tackle a big question: what keeps researchers from being replaced – or left behind – as AI takes on more of the research process?