What happens when AI meets traditional market research?

Even in just the past year, so much has changed in my day-to-day as Head of Research, Strategy & Insights. AI has completely reshaped how I’m able to gather data, test ideas, and extract meaning from the noise. But one thing that hasn’t changed is the question that always starts my day:

What can we learn today that will make us better tomorrow?

That question has always been my spark. The only real difference now is how quickly and confidently I can start answering it.

Traditional research methods, like interviews, surveys, behavioral data, are still essential. But AI gives us the power to go further, faster. It helps us spot patterns across massive datasets, simulate audience reactions in real time, and translate global insights at speed.  

We’re using AI to enhance, not replace, the fundamentals of research—so we can deliver sharper, faster, more actionable insights for our clients.

Strengths and limitations of traditional research

There’s still nothing quite like a well-run focus group or a rock-solid segmentation study. Coffee.  Donuts. Cold, hard proprietary data. Traditional qualitative research gives us emotional nuance. Quantitative research gives us scale and confidence. Together, they’ve long served as the backbone of brand and campaign strategy.

But let’s be honest… these methods are fairly time- and resource-intensive. Interviews require time to recruit and moderate. Surveys take weeks to design, field, and analyze. Insights get delayed, and opportunities get missed.

And consumer sentiment can shift overnight. Marketers need research that’s rigorous AND agile. And clients are asking more of their marketing—more personalization, more performance, and more proof. The challenge is, traditional research hasn’t always kept pace.

AI’s entry into research… What’s changed?

Over the past 18 months, we’ve seen AI-powered tools move from novelty to necessity in the research space. A recent report from Statista shows that around 17% of marketers are using AI extensively in their data-driven marketing efforts.

• Natural language processing (NLP) tools now transcribe and analyze interviews or focus groups in seconds. 
• Machine learning algorithms can detect patterns in open-ended survey responses that a human might miss—or take days to find. 
• Generative AI agents simulate how consumers might respond to different concepts, headlines, or product benefits, giving us directional data before we hit the field. 

And in one of my favorite examples, multilingual AI translation tools can also help to uncover insights across global markets without the delays and costs of human translation. This opens doors to real-time pulse checks across geographies—something that would have been unthinkable in a traditional research-only model.

“We see AI as an incredible ‘partner’ to traditional research, by enabling efficiencies and speed that further unlock research’s power to drive the smartest, highest performing strategies,” says Erin Francis-Cummings, President and CEO of Future Partners, a creative market‑research firm that specializes in turning traveler behavior data into strategic guidance. “AI tools allow us to focus even further on applying our deep industry and consumer behavior expertise to research design and aid our commitment to uncovering the underlying motivations that drive human decision making.”

Accelerating and enhancing research with AI testing

We use an AI-bot model that allows clients to test messaging, creative, and campaign ideas in a confidential sandbox. These AI-generated personas simulate reactions from target audience segments—helping us understand potential campaign responses.

In parallel, we use AI-driven social listening tools to:

• Track real-time consumer sentiment around a destination, experience, or brand
• Identify content gaps by comparing what consumers are asking with what competitors are saying
• Rapidly test campaign hypotheses and refine creative direction 

All of this accelerates the feedback loop. Instead of waiting three weeks for post-launch insights, we’re making strategic decisions mid-flight.

“I love that these tools have opened up the possibilities to help our teams to create and iterate faster. Testing creative is still an important part of our process, but now we can make even smarter decisions about what might resonate with our target audiences and pivot more quickly.”

— Amber Davis, VP, Creative Director at Envisonit

Best practices for AI-based research

Here’s what we’ve learned about effectively folding AI into the research process:

1. Start with clear research questions: AI is powerful, but it needs direction. 
2. Use AI for the heavy lifting: Data scraping, pattern detection, and transcription—delegate these to the machine. Keep the interpretive and strategic layers human. 
3. Always check for bias: Algorithms reflect the data they’re trained on. We have a review process for AI outputs to flag any outliers. 

And this is where the magic happens. Why choose between rigor and speed when you can design an AI-enhanced process for both?

What’s next for AI in research?

When people ask me, “What is AI’s role in research?” I respond, “Wherever it can enhance the current state to be faster, more efficient, and less costly.” It should supplement traditional methods with a smart and strategic human fixed firmly at the lead.

Ask yourself, “Where in my current research or campaign planning process could faster insights drive better decisions? What would I do differently if I had strategic clarity a week earlier? What can we learn today that will make us better tomorrow?”

If you’re navigating complex campaign planning, brand evolution, or a multi-market rollout, we should discuss how AI-augmented research can give you a strategic edge. Let’s talk.