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Gain Consumer Insight With Generative AI
MIT Sloan Management Review
|Summer 2026
Large language models are compressing research timelines with synthetic consumers, AI-moderated interviews, and qualitative research at scale.
MARKETING LEADERS OFTEN FACE a dilemma: Deriving the insights they need in order to make confident decisions can cost tens of thousands of dollars and involve several months of data gathering and analysis, by which time market conditions may have shifted. Can generative AI fundamentally reshape this calculus?
Drawing on recent research, including our own study published in the Journal of Marketing, as well as interviews with marketing leaders from major organizations, we have identified five ways that large language models (LLMs) are beginning to transform the marketing function and reshape the $153 billion insights industry. LLMs can viably compress marketing research timelines from months to days by introducing new approaches for rapid concept testing, such as the use of synthetic consumer “digital twins,” and enabling qualitative research at scale. These techniques allow companies to better harness unstructured data and smaller research teams to conduct much larger studies than they could previously.
Organizations conduct marketing research to uncover consumer insights that guide strategic and tactical business decisions. Historically, insight generation has been a multistage, time-consuming, and labor-intensive process.
A typical marketing research pipeline includes problem definition, research design, study design, sample selection, data collection, data analysis, and insights delivery. Some aspects of marketing research are qualitative (such as interviews and focus groups), and others (surveys, for example) are quantitative in nature. These studies may be conducted by in-house marketing research teams or outsourced to agencies with specialized expertise. A research project can take a few weeks to several months, depending on its scope, and can cost anywhere from tens to hundreds of thousands of dollars.
This story is from the Summer 2026 edition of MIT Sloan Management Review.
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