Most market researchers in 2026 are using AI for three things: analyzing open-ended survey responses at scale, drafting discussion guides and screeners, and summarizing transcripts and findings. The hype has settled into something practical. Researchers aren't replacing their thinking with AI, but they are finding places where it saves real time.
I teach market researchers how to use AI, and I consult with teams trying to figure out where it fits. The pattern across industries is pretty consistent. Adoption is widespread but shallow. Most people are using ChatGPT or Claude for brainstorming and drafting. Far fewer have connected AI to their actual data or workflows in any systematic way.
Who's actually adopting AI in market research right now?
The split isn't about company size anymore. It's about whether someone on the team stepped up to figure it out.
Large companies in regulated industries (pharma, financial services, healthcare) are moving cautiously. They've got legal reviews, compliance checks, and procurement processes that slow everything down. Some have internal AI councils. Most are still in pilot mode.
Smaller agencies and independent consultants are moving faster on adoption but often without much governance. They're using free or pro accounts and figuring it out as they go. That's great for speed. It's risky for data handling, which I'll get into in the data security article.
The teams getting the most value have one thing in common: a champion. Someone who learned the tools, wrote the prompts, and brought everyone else along. Without that person, AI tools sit unused.
What specific tasks are researchers handing off to AI?
Three categories dominate right now.
First: open-end coding and theme extraction. This is the clear winner in 2026. Tools like Ascribe, Relative Insight, and even well-prompted general AI can process thousands of verbatim responses in minutes. The output still needs human review. But researchers consistently tell me it cuts coding time by more than half.
Second: survey and guide development. Researchers feed AI their objectives and get back first drafts of questionnaires, screeners, and discussion guides. Not finished products. But good enough to react to, which is faster than starting from scratch.
Third: summarization. AI pulls key themes from transcripts and writes first-pass summaries. Researchers treat this like an intern's draft. Useful as a starting point, but it needs a careful review before it goes anywhere near a client.
What's still not working?
Video and audio analysis tools promise a lot and deliver uneven results. Emotion detection, facial coding, tone analysis. The demos look impressive. The real-world performance is inconsistent enough that most researchers don't trust it yet.
Anything requiring deep cultural interpretation also falls short. AI can tell you what people said. It can't reliably tell you what they meant, especially across languages, regions, and cultural contexts. That judgment still sits with the researcher.
How is the researcher role actually changing?
The junior analyst role is changing fastest. Tasks that used to fill someone's first couple years (coding, transcript review, basic charting) are getting compressed. The upside is that junior researchers can start learning strategic thinking earlier. The risk is that we create researchers who never developed the instinct for reading raw data carefully.
Senior researchers aren't necessarily coasting through this. Many are struggling with something different: how to manage teams through the uncertainty. They're dealing with junior staff who are anxious about their roles, clients who want AI used everywhere (or nowhere), and leadership asking for an AI strategy they don't feel equipped to write. The technical skills are one thing. The people management around AI adoption is a whole separate challenge.
For more on how research roles are evolving, the Insights Association publishes regular industry benchmarking reports worth reading, and Greenbook has a section of dedicated articles around changes in insights careers.
How should a research team get started if they haven't yet?
Start with one workflow, not a platform decision. Pick the most painful repetitive task on your team. For most, that's open-end coding or transcript summarization. Find a tool that does that one thing well. Use it for three months. Then decide what's next.
Don't start by buying an enterprise AI suite. The teams I've seen do that spend six months on configuration and governance before anyone actually uses anything. The teams that get value fastest start small, with tools researchers can try immediately, then build structure around what's working.