Market Research and AI: Looking ahead to 2026

If you think back to being a teenager, you’ll probably remember opinions being much clearer cut than later in life. Things were either brilliant or terrible, revolutionary or pointless. Grey areas were boring.

As we grow up, that certainty fades. We learn that most things worth caring about are complicated. The truth usually lies somewhere between the extremes.

During 2025, the Market Research industry collectively went through something similar with respect to AI. The original breathless hype is giving way to a more grounded understanding based on practical experience and experimentation. The industry is moving beyond the naïve reckless teenage years into a more focused and measured phase of early adulthood.

From hype to practical reality

Over the last few years, there have been some fairly wild projections about where AI (and specifically LLMs) is leading us: “AI will solve climate change!”, “AGI is only 3 years away!”, “The robot plumbers are coming!”.

Meanwhile, insights professionals, being a curious bunch, continue to dabble, experiment and find out for themselves where AI can be useful and what the limits are.

For example, Synthetic Data has not brought about the mass extinction of primary research as some originally predicted. However, it is increasingly being recognised as useful way to screen ideas prior to undertaking full-blown research.

Qualitative analysis, fraud detection and segmentation are similar areas where researchers are finding AI provides real value and potential, not just hype and FOMO.

As we progress through 2026, we expect to see a continuation of this trend – researchers continuing to learn, experiment and understand where AI actually brings value.

Going it alone

In 2025, we had many conversations with people who had decided to ‘go it alone’ and do their own AI in-house. This ranged from simply uploading data to ChatGPT through to full-blown enterprise software development initiatives. More often than not, the result was disappointment – disappointment with the output and disappointment that it wasn’t all as easy as they imagined.

We wrote about this in a separate blog earlier in the year and we expect this trend to continue in 2026. Companies will continue to try and build their own AI systems – it’s just too tempting not to – and many will struggle, get it wrong and fail. To get it right, companies need a clear, compelling understanding of how their home-grown system will provide greater competitive advantage than an off-the-shelf system.

Going beyond blind automation

The more people gain practical experience using AI, the more it becomes apparent that human supervision is essential for accurate, reliable output. AI systems that can work autonomously, with no human involvement just haven’t materialised in market research - and show no sign of doing so any time soon.

This is particularly true of verbatim coding. While AI can generate an initial codeframe in seconds, researchers still need to refine it - merging codes, relabelling themes, and catching the nuance that algorithms miss. The question isn’t whether humans are needed, but how to make the collaboration between human and AI as seamless as possible.

We expect this understanding to deepen in 2026. The conversation is already shifting from “can AI replace this task” to “how can AI and humans work together more effectively?” It’s a more boring answer than the hype promised, but it’s proving to be the right one.

Looking forward

It’s clear that the market research industry’s relationship with AI is maturing and a clearer picture is emerging of where AI genuinely helps and where human expertise remains irreplaceable.

This maturation is healthy. It means fewer wasted resources on solutions that don't work, more focus on tools that genuinely improve efficiency and accuracy, and a more realistic understanding of what AI can and cannot do.

The teenage years are over. AI in market research is starting to look like a proper grown-up.

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