AI in Market Research: Yesterday, Today, and Tomorrow

A Two-Year Reflection on AI in Market Research. Where We’ve Been and Where We’re Headed.

Key Points

• Yesterday: We saw AI as an intriguing but untested idea: full of potential and uncertainty.

• Today: AI is part of our everyday toolkit: helping in ways we imagined; and in some we didn’t.

• Tomorrow: AI’s role will likely grow: but the value of human judgment will remain.

Introduction

Two years ago, we published a short piece asking: Will AI Help or Hinder the Market Research Industry?

At the time, AI in market research felt like something on the horizon rather than something in our daily work. We imagined faster data processing, richer insights, and automation that could take the heavy lifting out of analysis. We also wondered: could it miss important context? Could it unintentionally lower data quality?

Now, looking back, some of those early thoughts held up; some didn’t.

AI has surprised us in good ways, challenged us in others, and still leaves plenty of open questions.

Here’s how we see it: from yesterday to today, and where we think it might be headed next.

Yesterday

Back in 2023, AI felt more like a conversation starter than a daily driver.

We thought of it as:

• A way to speed up analysis; though maybe not capture the nuance that humans bring.

• Struggling to read tone or emotion in open-ended responses.

• Potentially introducing risks if AI-generated or bot responses slipped past screeners.

• Something we’d see rolled out slowly: tested on small, low-stakes projects before becoming mainstream.

• A tool surrounded by ethical questions about data privacy and bias.

  
  

In short: yesterday’s AI lived more in conference talks and industry blogs than in the flow of actual project work.

Today

Fast forward to now, and AI is no longer a “someday” idea: it’s here, and we use it more than we might have expected.

Today, we see:

• Integration in platforms: from automated coding to instant dashboards, AI has become part of the process without replacing the human element.

• Better qualitative capabilities: large language models can code open-ends, detect sentiment, and surface themes faster than before.

• Fraud prevention with AI: it’s ironic, but AI is now helping catch fraudulent AI-generated responses; though human review still matters.

• Wider adoption: consulting teams are using AI-assisted insights in major projects, not just pilots.

• Shifting ethics conversations: the focus is now on transparency, participant consent, and fairness in how AI models are trained.

Today’s AI isn’t perfect; but it’s proving to be more of a partner than a threat.

Tomorrow

If we look two years ahead, it’s hard to say exactly what AI’s role will be: but a few things seem likely.

We imagine:

• More personalized AI assistants that adapt to each team’s way of working.

• Dynamic surveys that adjust in real time to keep participants engaged.

• Multi-modal analysis that blends text, voice, and video data for richer insights.

• Clearer rules around responsible use: giving both clients and participants more confidence.

• A steady place for human expertise: to interpret, question, and connect AI-generated insights to real-world strategy.

If yesterday was curiosity, and today is integration, tomorrow might be about balance: letting AI handle the heavy lifting while people focus on the thinking that only people can do.

Final Thought:

When we first asked if AI would help or hinder market research, we couldn’t have fully pictured the ways it would weave into our work. It’s been both a helpful partner and a learning curve; its future will depend as much on how we choose to use it as on how the technology evolves.

Medical Mile Research