By Matilda Sarah, co-founder and VP of sales and marketing at Displayr.
Two years ago, conversations about AI in market research were still speculative. Could large language models really analyze survey data? Would generative tools ever be reliable enough for client deliverables? For many, the risks were simply too significant.
That hesitation has faded. Today, AI is no longer a side experiment; it’s built into how survey research gets done. This transformation hasn’t come from blindly trusting AI; rather, it’s been a combination of advances in technology and a shift in how we work with it.
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Human in the loop
One major breakthrough when it comes to AI in survey analysis has been adopting a “human-in-the-loop” approach. AI does what it does best – speed, scale and pattern recognition – while researchers step in to review, validate and apply judgment. Rather than fearing hallucinations, teams can now see exactly which statistical tests have been run, trace results and charts back to the raw data, and decide what to keep.
Tools now make it easy to check outputs, apply safeguards and ensure statistical validity. What once felt like handing over control has become a partnership: AI accelerates the mechanics, and researchers bring their years of experience.
Data analysis without the risk
Perhaps the biggest change is in data analysis itself. Two years ago, the idea of letting AI interpret tables or run statistical comparisons was risky. The large language model could hallucinate or misapply statistics. Today, the risk of error is offset by transparent workflows. AI can build crosstabs, highlight significant differences and even draft commentary, while researchers can audit each step. The result is faster analysis without compromising accuracy.
What else has changed
Beyond data analysis, a host of previously time-consuming tasks have been transformed:
- Data cleaning can be automated, with AI flagging outliers, low-quality responses and inconsistencies in minutes.
- Text coding has gone from weeks of manual effort to instant theme detection and sentiment analysis across thousands of open ends.
- Reporting can now be drafted automatically, with charts, slides and commentary that update as new data comes in.
- Personalization of outputs for different audiences – once a labor-intensive rewrite – can be scaled effortlessly.
- Code writing is no longer confined to specialists, with natural language prompts generating auditable, reusable scripts.