What a panel with three AI-only competitors taught me about AI research strategy

By Adam Mertz, Chief Strategy Officer, Discuss Last updated: May 2026

Key takeaways: At Succeet in Frankfurt, I shared a panel with three AI-moderated research companies. One co-panelist said the quiet part out loud: AI moderation is commoditizing. What lasts is a platform designed for the full research life cycle, one where accumulated data compounds into organizational intelligence rather than retiring into folders. That’s the strategic difference. And a show of hands in a room of a hundred researchers confirmed it.

At Succeet in Frankfurt earlier this month, I sat on a panel with three companies that, from the outside, might look a lot like us. Conveo, Bolt Insight, and Tellet all operate in the AI-moderated research space. The moderator opened by noting, with good-natured directness, that we all had suspiciously similar marketing language on our websites.

He wasn’t wrong. But by the time the session ended, it was pretty clear we are not building toward the same thing.

The panel was titled “Qual-at-scale: How effective is it really?” and for 45 minutes we covered useful ground: where AI moderation earns its place, where it doesn’t, how to think about fraud, recruitment, and engagement rates. All worth discussing.

The most revealing moment came at the end, when the moderator asked each of us a simple question: how do you plan to differentiate from the others?

I’ll get to my answer in a moment. First, something one of my co-panelists said.

What are the long-term implications of so many AI-only research companies flooding the market?

Hendrik Van Hove, co-founder of Conveo, gave an honest answer to the differentiation question. He said, in effect, that AI moderation is commoditizing. The marginal differences between platforms will shrink, and what will ultimately matter is the data and the decisions you can make with it.

He said it more diplomatically than that, but commodity was his word. I give him credit for saying it, because it’s true, and it’s not a small thing to admit when you’ve built a company entirely around AI moderation.

What it signals is that AI-only entrants are already anticipating a pivot. The moat they’re building today, fast and scalable AI interviews, is eroding before they’ve fully established it. The direction they’re pivoting toward is making accumulated research data more useful over time. That’s a real strategic concession, because it acknowledges something the AI market research industry has been reluctant to say directly: the problem was never just speed of data collection. Research dies when the project closes. That’s the actual problem.

Why does research keep dying when projects end?

When researchers talk about scaling qual, the conversation almost always gravitates toward methodology. How do you get more interviews, faster, cheaper? That framing misses the deeper issue.

Most organizations are stuck in a loop. A stakeholder asks a question. Someone commissions a study. Weeks pass. A report lands. The project closes. Six months later, a different stakeholder asks a related question, and the whole cycle starts again because no one can find the previous answer, or it’s buried in a deck that doesn’t map to the new question, or the data isn’t queryable in any useful way. The organization keeps re-buying answers it already owns.

This is an architecture problem. And it’s what Conveo’s CEO was circling when he said the future is about data.

Research shouldn’t go dark when a project ends. Every interview, every session, every survey response should feed something that gets smarter. That belief is what separates an end-to-end platform vision from a tool built for one method, and it’s what I kept coming back to throughout the panel.

How do you scale qualitative research without losing depth or methodology breadth?

This was the point I kept returning to, because nearly every question from the moderator assumed scaling qual and AI moderation were the same thing.

Scaling qualitative research means thinking about the entire research life cycle: how you prepare a study, how you execute it, how you synthesize what you find, and how you make those findings useful to everyone who needs them. AI moderation is one powerful tool inside that life cycle. A platform built only around AI interviews is asking researchers to stitch together the rest on their own.

Take focus groups. No one is running a focus group with an AI moderator. The whole dynamic of group interaction requires a skilled human in the room. But can you use AI to build a tighter discussion guide? To give observers simultaneous translation in real time so a team in London can follow a session running in Japanese? To synthesize and theme the findings afterward so they feed something forward rather than sitting in a folder? Yes. You can scale qual across almost any methodology when you’re thinking about AI as a capability across the full arc, not just as the interviewer.

That’s a fundamentally different product vision than “we run AI interviews.” Curiosity shouldn’t have a queue. For most research teams right now, it does, because their tools are project-shaped rather than intelligence-shaped.

Is AI qualitative research reliable? What mixed-method research shows about response quality

One of the more interesting moments came from the audience. Someone asked what happens when respondents know they’re talking to an AI, specifically, what stops them from using tools like ChatGPT to generate their answers.

It’s a fair question, and it’s only becoming more relevant.

Signs are already there: responses that feel slightly too polished, pauses that don’t match natural conversation, engagement that looks present but isn’t fully there.

This is one reason many of our customers run mixed-method studies by design, combining AI interviews with a smaller set of human-moderated sessions to cross-check quality and catch divergence. That’s not a workaround. It’s becoming standard practice because the value of human-led interviews isn’t purely about depth. It’s also about verification.

When you’re talking to someone face-to-face, the quality of that signal is different. The accountability is real. That’s human-centered AI in practice: technology that extends what human researchers can do rather than cutting them out of the equation.

The other panelists had thoughtful approaches to fraud detection: video and response tempo analysis, dual-agent observation systems, quality scoring. All worth knowing about. The broader point stands: fraud in AI-moderated research is an emerging structural problem. Any AI research strategy that doesn’t account for it, and that doesn’t preserve human-to-human interaction as part of the mix, is incomplete.

What does an AI research strategy look like for enterprise insights teams?

When it was my turn to answer the differentiation question, I didn’t lead with a product feature or a roadmap item. I asked the audience a question instead.

“Raise your hand if 100% of your qualitative research is going to be done via AI interviews this year or next.”

One hand went up. Out of roughly a hundred people.

The premise that AI moderation is the future of qualitative research, full stop, is one that almost nobody in that room actually believed. Most researchers are running mixed method. Most always will be. Focus groups, IDIs, online communities, AI interviews, quant: these aren’t competing formats. They’re different tools for different questions.

Discuss is built with the breadth to support all of it: quant and qual, AI-moderated and human-moderated, whatever the project demands. And then, the part that gets undersold, to take all of that research data and turn it into a living, compounding asset rather than a collection of reports that retire the moment they’re delivered. An intelligence layer where every study makes the next one smarter, and where the organization’s understanding of its customers grows rather than resets.

The Forrester Wave Q1 2026 Experience Research Platforms report reflects exactly this direction. Being named a Leader in that evaluation matters to Discuss because the criteria Forrester applied map onto where the industry is actually heading: coverage across the full research life cycle, AI that goes beyond moderation, and the ability to compound accumulated research into something an entire organization can access and act on. Discuss is also one of only 150 organizations globally recognized by OpenAI for data processing volume, and the only market research company on that list.

How do you build a compounding consumer research program?

I spoke at another session at Succeet alongside Jo Lindenberg from HelloFresh, where she walked through exactly what this looks like in practice. Her team went from running occasional in-home ethnography with a small crew to running weekly AI-moderated sessions across ten countries with 18 respondents each. But the more meaningful shift wasn’t the cadence. It was what they’re doing with everything they’re accumulating.

That session is the subject of a companion blog, published alongside this one. I’d encourage you to read it, because it puts the strategic argument from the panel into concrete operational terms. The shift HelloFresh made, from research as a project to research as a memory, is a working example of what an AI research strategy looks like when it’s built to compound rather than repeat.

The version of AI research strategy that doesn’t get commoditized

There’s a version of AI research strategy that’s mostly about speed and cost. Run more interviews, faster, cheaper. That version has genuine value. It’s also exactly the version that gets commoditized, quickly and predictably, as one of my co-panelists acknowledged on stage.

The version that compounds, that gets more valuable over time, that changes how an entire organization relates to its customers, requires something more. A platform designed for the full research life cycle. The ability to blend methods to match the question. A commitment to the human side of insight: not as a philosophical stance, but as a practical recognition that some questions can only be answered in a conversation where both sides know a real person is listening.

Research that sleeps in folders is overhead. The goal is a system where any question gets an answer rooted in real human understanding, and where the knowledge your organization has built up over years of talking to customers doesn’t disappear at the end of a billing cycle.

That’s the bet we’re making. The Succeet panel reinforced that it’s the right one, and that the window for making it is narrower than it might look from the outside.

FAQ: AI Research Strategy and Qualitative Research at Scale

Is AI moderation the same as qualitative research at scale? AI moderation is one tool inside the qualitative research life cycle. Scaling qual means supporting the full arc: study design, execution, synthesis, and making findings accessible across the organization. A platform built only around AI interviews handles one part of that arc.

How does Discuss differ from AI-only research platforms? Discuss supports quant and qual, AI-moderated and human-moderated research, across the full life cycle. The platform is designed to turn accumulated research data into a compounding organizational asset rather than a series of closed projects. Discuss is named a Leader in the Forrester Wave for Experience Research Platforms, Q1 2026, and is the only market research company recognized by OpenAI for data processing volume.

What is human-centered AI in market research? Human-centered AI extends what human researchers can do rather than replacing them. In practice, that means AI handles moderation, translation, and synthesis while human-moderated sessions preserve the verification and accountability that AI interviews can’t replicate.

Download a complimentary copy of the Forrester Wave™: Experience Research Platforms, Q1 2026 to see how Discuss was evaluated. Want to see how this AI research strategy translates to your own research program? Talk to our team.