Where AI Actually Creates Value in Market Research

The value of research is in the thinking, not the mechanics

Most conversations about AI in market research focus on speed: faster surveys, faster coding, faster charts. Speed helps. But it isn't where the real value sits.

Our founder, Vijay Rajan, put it simply in a recent interview with International Business Times, Is AI Finally Modernizing Market Research?:

"The value is added from analyzing what the code produced, not from writing the code."

The same applies to most of the research process. The value lies in interpreting results and turning them into decisions. Much of the technical and operational work that comes before interpretation is where AI can reduce the burden on researchers.

Where researchers' time goes today

A large share of a research team's week goes to work that produces outputs, not insights:

• Writing code for regression models, segmentation and other advanced analytics.
• Preparing data: cleaning, merging and reformatting files exported from different tools.
• Coding open-ended responses and tagging interview transcripts.
• Building reports by hand: copying charts and tables into slides.
• Managing tools: moving data between survey platforms, spreadsheets and statistical software.

This work is necessary, but it's rarely where a project's value comes from. A regression model is only useful once someone explains what it means for the business. A slide deck is only useful if it leads to a decision.

What AI should take over

AI creates the most value when it removes the technical and operational layer between a research question and its answer.

The point isn't to remove researchers from the process. It's to remove the work that keeps them from doing research.

What stays human

Once more of the mechanics are handled, researchers can spend more time on the work where human judgment and accountability matter most:

• Interpretation. Explaining what the data means and why it matters.
• Context. Connecting findings to the market, the brand and the business problem.
• Advice. Turning results into clear recommendations.
• Decisions. Helping stakeholders choose what to do next.

This is also where the profession is heading. As cited by IBTimes, the Market Research Institute International's 2026 study found that 58% of insights professionals expect their function to become more important. That only happens if insights teams spend more time on strategy and less on operations.

Why AI output has to be traceable

Handing technical work to AI only helps if researchers can trust the results. If an insight can't be traced back to the data and the question behind it, nobody can stand behind it.

That's why AI works best inside a connected workflow, not as a set of separate tools. When planning, fieldwork, analysis and reporting live in one system, every output keeps its context. (We cover this in more detail in Why Market Research Workflows Are Still Fragmented in the Age of AI.)

This is how we built Compeers AI. The platform uses AI to accelerate technical and operational work across qualitative, quantitative and advanced analytics, while researchers remain involved in methodology, review, interpretation and recommendations. Report assembly that once took hours can be reduced to minutes, and findings remain connected to their underlying research evidence so researchers can review what AI produces before acting on it.

Frequently asked questions

Where does AI create the most value in market research?

AI creates the most value by handling technical and operational work: writing analysis code, preparing data, coding open-ended responses and assembling reports. This frees researchers to focus on interpretation and decision-making.

Will AI replace market researchers?

AI is more likely to change where researchers spend their time than eliminate the need for researcher judgment. It can automate or accelerate many technical and operational tasks, while researchers remain responsible for methodology, context, interpretation and recommendations.

What market research tasks should be automated with AI?

Good candidates are tasks that produce outputs rather than insights: data cleaning and merging, statistical modeling code, qualitative coding and report building.

How can researchers trust AI-generated insights?

AI output should be traceable to the data and research question behind it. That's easiest when the whole workflow runs in one connected system, like Compeers AI.

Do researchers need data science skills to run advanced analytics with AI?

Not necessarily. Platforms like Compeers AI can handle more of the technical execution, allowing researchers to use advanced analytics without having to write the underlying analysis code themselves.

The bottom line

AI's real value in market research isn't writing code or building slides faster. It's giving researchers back the time to think, interpret and advise.

Read the full International Business Times feature: Is AI Finally Modernizing Market Research?

Want to spend less time on mechanics and more on insights? Book a demo of Compeers AI.

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