The Role of AI in Market Research with David Boyle

In a wide-ranging conversation on the MRII Insights & Innovators podcast, David Boyle, strategist, researcher and Director of Audience Strategies, makes a compelling case for why the insights profession is better positioned than any other field to harness the power of AI-driven language models. Drawing on a career that spans consulting, politics, retail and entertainment, Boyle argues that AI doesn't replace researchers; it accelerates them. Below are five of the most illuminating exchanges from their conversation, edited for brevity and clarity.

How do you separate AI hype from real substance? 

“I try not to use the phrase ‘AI’. I try to use the phrase ‘language models’ because that's the thing that's making the big difference. Let's be specific. These are machines that let you put some words in and get some more words back out again as a result."

Boyle identified four areas where language models deliver genuine value: synthesizing information (especially qualitative data that previously went unanalyzed), writing more compelling reports and pitches, thinking through audience insights more deeply, and working through the strategic implications; the "so whats" of research findings. “It's about helping with lots of little tasks all day, every day, rather than one single breakthrough at any one point in the journey.”

How has AI changed your approach to audience segmentation?

"I firmly believe that strategy should be absolutely rooted in what audiences need." On segmentation specifically, Boyle described a process that has been "completely upended" by AI. Brainstorming category-related needs, work that previously took a week or two, sometimes requiring qualitative research, can now be accomplished in under an hour. "We can go into the pitch with a client with a hypothesis of what the answer is, which is 70 or 80 percent of what the answer will be when you pay us to do the research off the back of it."

What skills do insights professionals need to stay relevant?

"At once, all of the skills the research industry has are still critical. Like, every single one of them is absolutely critical. AI doesn't replace any of those skills." But researchers should "get ready to have a broader repertoire of things that you do because AI will help you to do more" and "get ready to work on more clients than you used to because each project's quicker and mentally easier." Most critically: "Learn from the AI, use it to help you to learn and grow your career. So do all the things we used to do, but weave AI into every single one of them as a thought partner and as an accelerant."

Is AI additive or a substitute for human researchers?

"When used well, it's absolutely additive. We use the analogy that it's like an electric bike for the mind. You're still in control, you're still steering, you're still navigating, you're still pedaling, actually, but you know what? It's an accelerant. And I think the danger is when people use it as an electric car for the mind.

They zone out and watch YouTube instead of following, keeping their eyes on the road and keeping their hands on the wheel. That's really dangerous. So we don't advocate for automation. We don't advocate for replacing humans. It's an additive accelerant to human abilities. Absolutely."

What advice do you have for leaders looking to harness AI across their organizations?

"You are not exempt. It will help you as well, I promise." He described AI adoption as fundamentally an organizational change challenge, not a technology or systems problem. "Making everything your whole team does every day better, quicker, and happier and reducing the mental overhead is an incredible boost and accelerant to the organization overall."

He urged leaders to support fast adopters without holding them back, while providing structured support for those who struggle. "I would insist that they develop the new skills, but support them in developing the new skills. Give them the support they need as well." The bottom line: "It's more of an organizational change problem than it is a technology problem or a systems problem. And that's a whole different way of thinking about it. It's a whole different challenge."

Closing Thoughts: The Researcher’s Moment

David Boyle’s overarching message is both a challenge and an invitation. The insights profession has long possessed the raw material: deep audience knowledge, rigorous methodology and the ability to surface what people truly need, but has often struggled to translate that material into strategic influence. Language models don’t solve that problem on their own, but they dramatically lower the barriers that kept researchers from doing so: the time-consuming synthesis, the laborious hypothesis building, the gap between evidence and executive-ready narrative. 

For MRII members and the broader research community, that’s a significant opportunity — but only for those willing to pedal. As Boyle puts it, the electric bike is there; the question is whether you’ll use it to go further, or simply coast. Organizations that treat AI adoption as the cultural and organizational change challenge it truly is, rather than a tool purchase, will be the ones that finally close the gap between insight and strategy, and claim the seat at the table that market research has always deserved. 

Listen to the full episode of MRII’s Insights & Innovators podcast featuring David Boyle talking to host Jon Last.